Introduction to Supply Chain

Joannès Vermorel

September 23, 2025

Contents

1 Primer 1

1.1 Decisions . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Optionality . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Physical goods . . . . . . . . . . . . . . . . . . . . 5

1.4 Variability . . . . . . . . . . . . . . . . . . . . . . . 7

1.5 Mastery . . . . . . . . . . . . . . . . . . . . . . . . 9

1.6 Parting thoughts . . . . . . . . . . . . . . . . . . . 12

2 History 15

2.1 Early 1800s to pre-war 1900s . . . . . . . . . . . . . 16

2.2 WWII and post-war developments . . . . . . . . . . 17

2.3 1980s to early 2000s . . . . . . . . . . . . . . . . . . 20

2.4 Present day considerations . . . . . . . . . . . . . . 23

2.5 Mainstream perception . . . . . . . . . . . . . . . . 25

2.6 Looking ahead . . . . . . . . . . . . . . . . . . . . . 27

3 Epistemology 29

3.1 Economics . . . . . . . . . . . . . . . . . . . . . . . 30

3.2 Mathematics . . . . . . . . . . . . . . . . . . . . . . 34

3.2.1 Falsiﬁability . . . . . . . . . . . . . . . . . . 36

3.2.2 The incentives of academia . . . . . . . . . . 39

3.2.3 The pitfalls of safety stocks . . . . . . . . . 42

3.3 Sociology . . . . . . . . . . . . . . . . . . . . . . . 44

3.3.1 The pitfalls of S&OP . . . . . . . . . . . . . 48

3.4 Economic history . . . . . . . . . . . . . . . . . . . 51

3.5 Auxiliary sciences . . . . . . . . . . . . . . . . . . . 52

ii CONTENTS

3.6 Epistemic corruption . . . . . . . . . . . . . . . . . 56

3.6.1 Case studies . . . . . . . . . . . . . . . . . . 58

3.7 Negative knowledge . . . . . . . . . . . . . . . . . . 61

4 Economics 67

4.1 Essential economic principles . . . . . . . . . . . . . 68

4.2 Markets and supply chains . . . . . . . . . . . . . . 69

4.3 The value of supply chains . . . . . . . . . . . . . . 72

4.3.1 Standardization . . . . . . . . . . . . . . . . 73

4.3.2 Pooling . . . . . . . . . . . . . . . . . . . . 75

4.3.3 Batching . . . . . . . . . . . . . . . . . . . . 77

4.3.4 Versatility . . . . . . . . . . . . . . . . . . . 79

4.3.5 From forces to policies . . . . . . . . . . . . 81

4.4 The goal of supply chain . . . . . . . . . . . . . . . 83

4.4.1 Proﬁt and loss . . . . . . . . . . . . . . . . . 84

4.4.2 Objective valuations . . . . . . . . . . . . . 85

4.4.3 Rate of return . . . . . . . . . . . . . . . . . 87

4.4.4 Cronyism and regulatory capture . . . . . . 98

4.4.5 Supra-economic goals . . . . . . . . . . . . . 99

4.4.6 Solutions and trade-oﬀs . . . . . . . . . . . 111

4.5 Valuation concerns . . . . . . . . . . . . . . . . . . 114

4.5.1 The economic model . . . . . . . . . . . . . 115

4.5.2 Implicit valuations . . . . . . . . . . . . . . 116

4.5.3 Spending and revenue attribution . . . . . . 119

4.5.4 Projected spending and revenues . . . . . . 122

5 Information 131

5.1 Data, information, and knowledge . . . . . . . . . . 133

5.1.1 Data storage . . . . . . . . . . . . . . . . . . 134

5.1.2 Information theory . . . . . . . . . . . . . . 137

5.1.3 Knowledge . . . . . . . . . . . . . . . . . . . 140

5.2 Classes of enterprise software . . . . . . . . . . . . 144

5.2.1 The place of software . . . . . . . . . . . . . 146

5.3 Systems of records . . . . . . . . . . . . . . . . . . 148

5.3.1 The origin of enterprise software . . . . . . . 149

5.3.2 The CRUD design . . . . . . . . . . . . . . 150

CONTENTS iii

5.3.3 Technological trajectory . . . . . . . . . . . 152

5.3.4 Functional overlaps . . . . . . . . . . . . . . 156

5.3.5 Functional limits . . . . . . . . . . . . . . . 159

5.3.6 Vendor’s conundrum . . . . . . . . . . . . . 162

5.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . 165

5.4.1 Origin of the data . . . . . . . . . . . . . . . 166

5.4.2 Semantics of the data . . . . . . . . . . . . . 168

5.4.3 Shenanigans in data . . . . . . . . . . . . . 172

5.4.4 Shadow IT . . . . . . . . . . . . . . . . . . . 174

5.4.5 Derived data . . . . . . . . . . . . . . . . . 176

5.4.6 External data . . . . . . . . . . . . . . . . . 180

5.5 Mundane knowledge . . . . . . . . . . . . . . . . . 187

5.6 Bad data . . . . . . . . . . . . . . . . . . . . . . . . 189

6 Intelligence 193

6.1 Systems of intelligence . . . . . . . . . . . . . . . . 194

6.1.1 A productive asset . . . . . . . . . . . . . . 196

6.1.2 Muddy emergence . . . . . . . . . . . . . . . 197

6.1.3 Culture and incentives . . . . . . . . . . . . 199

6.1.4 Conﬂicting design requirements . . . . . . . 201

6.1.5 No problem without solution . . . . . . . . . 205

6.2 General intelligence . . . . . . . . . . . . . . . . . . 207

6.2.1 General intelligence problems . . . . . . . . 209

6.2.2 Clerical jobs in supply chain . . . . . . . . . 212

6.2.3 Large language models . . . . . . . . . . . . 213

6.2.4 Misconceptions about intelligence . . . . . . 218

6.3 Specialized intelligence . . . . . . . . . . . . . . . . 224

6.3.1 Algorithms . . . . . . . . . . . . . . . . . . 225

6.3.2 Operations Research . . . . . . . . . . . . . 227

6.3.3 Statistical learning . . . . . . . . . . . . . . 229

6.3.4 Deep learning . . . . . . . . . . . . . . . . . 232

6.3.5 Generative Era . . . . . . . . . . . . . . . . 242

7 The Future 247

7.1 Visions . . . . . . . . . . . . . . . . . . . . . . . . . 248

7.1.1 Values . . . . . . . . . . . . . . . . . . . . . 251

iv CONTENTS

7.1.2 Time . . . . . . . . . . . . . . . . . . . . . . 252

7.2 The teleological vision . . . . . . . . . . . . . . . . 254

7.2.1 Historical emergence . . . . . . . . . . . . . 255

7.2.2 Time-series paradigm . . . . . . . . . . . . . 257

7.2.3 Planning through time-series . . . . . . . . . 260

7.2.4 Limits of planning . . . . . . . . . . . . . . 261

7.2.5 Bureaucratic appeal . . . . . . . . . . . . . 265

7.3 The Rugged Vision . . . . . . . . . . . . . . . . . . 268

7.3.1 Irreducible uncertainty . . . . . . . . . . . . 270

7.3.2 Opportunistic allocations . . . . . . . . . . . 272

7.3.3 Asymmetry of time . . . . . . . . . . . . . . 275

7.3.4 Superiority of the rugged vision . . . . . . . 278

7.4 Predictability of human aﬀairs . . . . . . . . . . . . 279

7.4.1 Momentum and cyclicities . . . . . . . . . . 280

7.4.2 The dead end . . . . . . . . . . . . . . . . . 284

7.4.3 The law of small numbers . . . . . . . . . . 286

7.4.4 Probabilistic forecasts . . . . . . . . . . . . 288

7.4.5 Fat tails . . . . . . . . . . . . . . . . . . . . 294

7.4.6 The price of knowledge . . . . . . . . . . . . 297

7.4.7 High-dimensional forecasts . . . . . . . . . . 299

7.4.8 Functional forecasts . . . . . . . . . . . . . . 302

7.4.9 Stochastic simulations . . . . . . . . . . . . 305

8 Decisions 309

8.1 Thinking the decision . . . . . . . . . . . . . . . . . 311

8.1.1 Surfacing the frame . . . . . . . . . . . . . . 311

8.1.2 Banishing artifacts . . . . . . . . . . . . . . 312

8.1.3 System economics . . . . . . . . . . . . . . . 315

8.1.4 Embracing the margin . . . . . . . . . . . . 316

8.2 Why automate . . . . . . . . . . . . . . . . . . . . 318

8.2.1 A productive asset . . . . . . . . . . . . . . 319

8.2.2 Manual overrides . . . . . . . . . . . . . . . 320

8.2.3 Culture and unrest . . . . . . . . . . . . . . 321

8.2.4 Human exceptionalism . . . . . . . . . . . . 322

8.2.5 From management to machinery . . . . . . . 322

8.2.6 Where to start . . . . . . . . . . . . . . . . 325

CONTENTS v

8.3 Deciding is optimizing . . . . . . . . . . . . . . . . 327

8.3.1 The optimization paradigm . . . . . . . . . 327

8.3.2 On optimality . . . . . . . . . . . . . . . . . 329

8.3.3 The solver within . . . . . . . . . . . . . . . 330

8.3.4 The economics of compute . . . . . . . . . . 333

8.4 Deciding under uncertainty . . . . . . . . . . . . . . 334

8.4.1 Fragile decisions . . . . . . . . . . . . . . . . 336

8.4.2 Unenforceable constraints . . . . . . . . . . 339

8.4.3 Exploration vs. exploitation . . . . . . . . . 341

8.4.4 Nurturing options . . . . . . . . . . . . . . . 344

8.5 Sequential decisions . . . . . . . . . . . . . . . . . . 345

8.5.1 Window of responsibility . . . . . . . . . . . 347

8.5.2 Permutation invariance . . . . . . . . . . . . 351

8.5.3 Postponement principle . . . . . . . . . . . . 354

8.5.4 Policy search . . . . . . . . . . . . . . . . . 357

8.6 Changing course . . . . . . . . . . . . . . . . . . . . 358

8.6.1 The load of a decision . . . . . . . . . . . . 360

8.6.2 The half-life of a decision . . . . . . . . . . . 361

9 Engineering 363

9.1 Why program . . . . . . . . . . . . . . . . . . . . . 365

9.2 Experimental optimization . . . . . . . . . . . . . . 367

9.2.1 Cartesianism . . . . . . . . . . . . . . . . . 369

9.2.2 Mundane issues . . . . . . . . . . . . . . . . 370

9.2.3 Insane decisions . . . . . . . . . . . . . . . . 372

9.2.4 Iterative falsiﬁcation . . . . . . . . . . . . . 373

9.2.5 Instrumentation . . . . . . . . . . . . . . . . 374

9.2.6 Continuous improvement . . . . . . . . . . . 375

9.3 Whiteboxing . . . . . . . . . . . . . . . . . . . . . . 376

9.4 Fermi-ization of the elusive . . . . . . . . . . . . . . 379

9.5 On programmability . . . . . . . . . . . . . . . . . 382

9.5.1 Programming paradigms . . . . . . . . . . . 385

9.5.2 Spreadsheets . . . . . . . . . . . . . . . . . 393

9.5.3 Generic languages . . . . . . . . . . . . . . . 395

vi CONTENTS

10 Deployment 399

10.1 Selecting a vendor . . . . . . . . . . . . . . . . . . . 401

10.1.1 Request For Proposals . . . . . . . . . . . . 402

10.1.2 Adversarial market research . . . . . . . . . 406

10.2 Scoping the deployment . . . . . . . . . . . . . . . 411

10.2.1 The numerical recipe . . . . . . . . . . . . . 413

10.2.2 The data pipeline . . . . . . . . . . . . . . . 416

10.2.3 Roles in the initiative . . . . . . . . . . . . . 418

10.3 Onboarding . . . . . . . . . . . . . . . . . . . . . . 420

10.3.1 Timeline overview . . . . . . . . . . . . . . . 421

10.3.2 Iterative deployment . . . . . . . . . . . . . 422

10.3.3 Progressive rollout . . . . . . . . . . . . . . 424

10.3.4 Long-term maintenance . . . . . . . . . . . 428

10.4 Organizational changes . . . . . . . . . . . . . . . . 432

10.4.1 Planner vs. Flow Manager . . . . . . . . . . 434

10.4.2 The Supply Chain Scientist . . . . . . . . . 439

10.4.3 The greater company . . . . . . . . . . . . . 443

11 Stagnation 449

11.1 The paradox of success . . . . . . . . . . . . . . . . 450

11.2 Real-world complexity . . . . . . . . . . . . . . . . 452

11.2.1 Operational vignette . . . . . . . . . . . . . 452

11.2.2 Local maximum . . . . . . . . . . . . . . . . 453

11.3 Why the equilibrium holds . . . . . . . . . . . . . . 454

11.4 Who sustains it . . . . . . . . . . . . . . . . . . . . 456

11.4.1 Software vendors . . . . . . . . . . . . . . . 459

11.4.2 Consultants . . . . . . . . . . . . . . . . . . 461

11.4.3 Corporate data science . . . . . . . . . . . . 463

11.5 The organizational trap . . . . . . . . . . . . . . . . 465

11.5.1 Second-class status . . . . . . . . . . . . . . 465

11.5.2 Bureaucracy and ticket latency . . . . . . . 466

11.5.3 Subordination to IT . . . . . . . . . . . . . 466

11.5.4 Patch accretion . . . . . . . . . . . . . . . . 467

11.5.5 Seam and ownership . . . . . . . . . . . . . 467

11.6 Rejection ﬁlters . . . . . . . . . . . . . . . . . . . . 468

11.6.1 Trivia . . . . . . . . . . . . . . . . . . . . . 469

CONTENTS vii

11.6.2 Authorities . . . . . . . . . . . . . . . . . . 470

11.6.3 Puzzles . . . . . . . . . . . . . . . . . . . . . 470

11.6.4 Using the ﬁlters . . . . . . . . . . . . . . . . 471

11.7 Looking onward . . . . . . . . . . . . . . . . . . . . 472

11.7.1 What must stop . . . . . . . . . . . . . . . . 473

11.7.2 Towards a better way . . . . . . . . . . . . . 474

A Technical notes 477

A.1 Aperiodic rate of return . . . . . . . . . . . . . . . 477

A.2 Discounted cash ﬂows . . . . . . . . . . . . . . . . . 479

A.3 Informational entropy . . . . . . . . . . . . . . . . . 481

A.4 Topological sort . . . . . . . . . . . . . . . . . . . . 483

A.5 Four-layer perceptron . . . . . . . . . . . . . . . . . 486

A.6 Mathematical optimization . . . . . . . . . . . . . . 488

viii CONTENTS

Chapter 1

Primer

Between a bad economist and a good one lies the whole

distinction: the former stops at the visible eﬀect; the latter

reckons both with the eﬀect that is seen and with those

that must be foreseen. This diﬀerence is vast [..] the bad

economist pursues a small present good that will be followed

by a great future harm, whereas the true economist seeks a

great future good at the risk of a slight present hardship.

What is Seen and What is Not Seen (1850), Frédéric Bastiat

Supply chain is an abstraction—an intellectual tool that co-

ordinates the eﬀorts of many to deliver physical goods cheaply

and reliably. This instrument was devised to tame the incredible

complexity that our industrialized civilization entails. Supply chain

cannot be seen or touched. Factories, warehouses, vessels, and

trucks—visible as they are—are not the supply chain. The supply

chain lies in the web of expectations binding those assets together.

Supply chain is an intent, not a thing.

Over the last two decades, automation has become a central

concern for supply chain. If the 20

century was the century of

mechanizing physical labor, the 21st is decidedly about mechanizing

intellectual labor. The ambient complexity of most large supply

chains already vastly exceeds what can be handled even by the

most diligent managers. As such, supply chain must be approached

and understood in ways that maximally leverage what modern

2 CHAPTER 1. PRIMER

computers oﬀer.

Approaching supply chain is an arduous task. It is at once

abstract—demanding reﬂection on concepts like time or human

nature—and concrete, requiring attention to mundane details such

as a container’s maximum tonnage or national holidays. Most diﬃ-

cult of all, as Bastiat rightly noted, is the ability to envision what

cannot be seen—only foreseen. Indeed, the connection between

economics and supply chain runs deep.

We therefore need a precise deﬁnition of supply chain—a foun-

dation that will guide the chapters ahead.

Deﬁnition (Supply chain).

Mastery of optionality under variability in managing the

ﬂow of physical goods.

Supply chain is both a ﬁeld of study and a corporate prac-

tice. The ﬁeld of study is descriptive: it describes the ﬂow of

physical goods by uncovering its intrinsic principles and properties.

The goal is to develop a predictive theory that assesses both the

immediate and long-term consequences of every factor inﬂuenc-

ing the ﬂow. Some levers, such as issuing a purchase order to a

supplier, are within the company’s control, whereas others, like

receiving a purchase order from a client, are not. Ultimately, these

consequences shape the company’s long-term proﬁtability.

The corporate practice is prescriptive. It aims to design the

organization and methods best suited to guide the ﬂow of physical

goods within—and potentially beyond—the company. The goal is

to pinpoint the perspectives, tools, and practices generally consid-

ered desirable for enhancing supply chains. Conversely, practices

generally detrimental to proﬁtability should be recognized as such.

Naturally, the prescriptive approach is grounded in descriptive

analysis: understanding decision consequences should guide the

company toward better choices.

1.1. DECISIONS 3

1.1 Decisions

A decision allocates a scarce resource to a speciﬁc purpose, thereby

excluding alternatives that were previously available. For exam-

ple, if a company spends money to purchase one additional unit

of a product, that money can no longer be used for any alter-

native use. Over time, the company will need funds for new

purchases—possibly recovered by disposing of or repurposing the

previously acquired product. For now, the money remains com-

mitted as a result of the decision. Supply-chain decisions are the

subset of economic decisions concerned with the ﬂow of goods;

many economic choices involve no such ﬂow at all.

All resources, not just money, are scarce. Inventory is another

fundamental resource in supply-chain settings. For example, if

a unit in the warehouse is dispatched to one store, it cannot be

dispatched to any other. Eventually, the company might restock

the same product in the warehouse or even return the original unit

from the store. However, at least for now, that inventory unit is

committed as a result of the decision.

Pricing decisions also play a critical role in supply chain. Set-

ting a product’s price creates a market condition for its exchange,

eﬀectively precluding other pricing options for that sale. Although

market demand always involves uncertainty, the chosen price com-

municates the company’s valuation of its inventory. Over time,

companies may adjust their pricing strategies for subsequent sales

in response to market feedback.

Any deliberate action that alters what moves, where, when, or

how much is a supply-chain decision. Raising a purchase order,

scheduling a truck, allocating an inbound container to a warehouse

rather than a cross-dock, or discounting a slow-mover all reshape

the material ﬂow within operational lead times; they therefore fall

squarely under the remit of supply chain.

Conversely, initiatives whose inﬂuence is only remote—funding

basic R&D, commissioning an advertising campaign, scouting a

new geography—may eventually aﬀect the ﬂow but do so through

several intermediate steps. Their relevance is strategic rather than

4 CHAPTER 1. PRIMER

operational, and thus they sit outside the scope addressed here. In

practice, the boundary is crisp: among the hundreds of levers a

planner manipulates every week, only a handful resist immediate

classiﬁcation, and for those rare cases the label—supply chain or

not—has negligible impact on proﬁtability.

What usually obstructs this recognition is not analytical diﬃ-

culty but corporate tradition. Over decades, companies have la-

beled certain levers—pricing, merchandising, assortment choices—as

the exclusive province of marketing or sales and have thereby exiled

them from supply-chain conversations. Yet these very levers mold

demand and, by extension, every downstream movement of goods.

In the pages that follow, they will be treated where logic places

them: squarely within the perimeter of supply chain.

1.2 Optionality

A decision is made among competing options. However, having

multiple available options is not assured for a company. For

example, if a company has identiﬁed only one supplier for a product,

its choices are limited to selecting the order date and quantity from

that supplier. If that supplier proves too unreliable or expensive,

the company may be forced to stop ordering, likely frustrating

both the business and its clients. Thus, fostering favorable options

is crucial. Optionality is vital because any forecasts a company

produces inherently involve irreducible uncertainty, regardless of

the technology used.

Supply chain options are limited by the capacity—and, by

extension, the cost—required to establish them. Management

attention—and organizational attention in general—is another

scarce resource that must be allocated carefully. Having multiple

reliable suppliers is preferable, but it requires identifying, qualifying,

and contracting them to enable timely ordering. Depending on the

circumstances, this attention may be better invested in deepening

the relationship with the current—and possibly best—supplier.

Viewing unused options as mere overhead to be trimmed by

1.3. PHYSICAL GOODS 5

management is naive and misguided. There may be cases where

a company overinvests in optionality, yielding diminishing re-

turns when additional options fail to deliver proportional ben-

eﬁts. Nonetheless, such overinvestments are infrequent, even if

maintaining options may seem costly to outsiders.

Numerous options are self-evident, yet for practical purposes

many are ruled out. This happens whenever options cannot be

easily assessed. For instance, a retail network with many stores

may recognize occasional opportunities to rebalance inventory by

selecting an optimal route for a truck to load and unload goods

sequentially. Yet, if such rebalancing must be planned by hand—or

with generic tools ill-suited to the task—the combinatorial explo-

sion swiftly overwhelms people and spreadsheets. In the absence

of dedicated optimization software able to sift through millions of

routing permutations in seconds, this class of options is quietly

dismissed as “too complex” and never reaches the decision table.

More often, options are assessed only intermittently, largely

due to the company’s limited attention or limited tooling. For

example, a company might place orders with a supplier only once

a week—not because weekly cycles have inherent limitations or

beneﬁts, but simply because composing daily orders would be too

time-consuming. A daily assessment does not mean an order is

placed every day; it means the option to order is reviewed daily,

even if no order is made on most days.

1.3 Physical goods

Supply chain is about the ﬂow of physical goods. Remove the goods,

and what remains is little more than ﬁnance and trading. The craft

unfolds in three recurring moves: ﬁrst, money is converted into

goods—stock units purchased from suppliers; next, those goods are

processed—inspected, stored, transported, or transformed; ﬁnally,

the goods are turned back into money, whether through direct sale

or by being consumed in producing further products and services.

The process is meant to be repeated indeﬁnitely, though never

6 CHAPTER 1. PRIMER

in the exact same way, as every aspect of the ﬂow changes over

time. Only the existence of the ﬂow remains constant, enduring as

long as the company exists.

Physical goods impose constraints that cannot be eluded. Every

action involving them—purchasing, moving, transforming—burns

cash, yet every non-action—keeping, waiting, storing—levies an

implicit toll just the same. Money is spent whether the goods ﬂow

or stand still; the only leverage lies in choosing the mix of actions

and inactions that minimizes total cost while preserving optionality.

These realities remain invisible when goods are reduced to electronic

markers in modern trading systems. The high-frequency trader

dealing in copper need not care where the metal rests, how it is

protected from oxidation or theft, or whether it ﬁts the gross-weight

limit of a twenty-foot container. The supply-chain practitioner,

conversely, must grapple with each of these constraints, and the

market rewards companies that master both the explicit costs of

action and the implicit costs of inaction.

Handling physical goods is constrained by matter, energy, space,

and time. Crates cannot tunnel through walls, pallets cannot

out-lift their forklifts, and a vessel that sailed from Shanghai

yesterday will not berth in Rotterdam tomorrow. By contrast, the

informational layer that steers those movements is, for practical

purposes, elastic: a commodity laptop can explore millions of

permutations per second, and a modest cloud cluster can tame

billions.

Elastic does not mean trivial. Turning raw data into actionable

insight is ﬁendishly diﬃcult—noise, ambiguity, and sheer volume

conspire to blur the picture. Yet every minute a planner spends

waiting for a report to refresh, every replenishment batch delayed

by “system limitations”, ﬂag a design failure. The bottleneck

should reside in steel and diesel, not in an SQL query.

Timing makes the point vivid. An algorithm that computes

procurement recommendations for a thousand SKUs in ﬁfty millisec-

onds is instantaneous next to the fortnight needed to manufacture

the goods and the six weeks needed to ﬂoat them across an ocean.

Relative to atoms, bits move at light speed; any decision latency

1.4. VARIABILITY 7

that looms large beside physical lead time is unnecessary waste.

Scale tells the same story. Enumerating ten million cartons

costs pennies in storage and milliseconds of CPU. Housing those

cartons demands hectares of racking, ﬂeets of conveyors, and armies

of operators. When a project is declared “blocked by data volumes”,

the remedy is never more forklifts; it is always better data modeling

and leaner software.

In short, the limiting factor of a well-run supply chain ought

to be reality itself—cubic meters, kilowatts, calendar days. When-

ever the choke point sits in the digital cockpit—dashboards that

stall, optimizers that time-out, planners who “cannot see” the op-

tions—management has located a low-hanging opportunity. Elim-

inating decision friction is not cosmetic; it is the primary lever

through which modern computing squeezes more returns from the

same scarce resources.

1.4 Variability

Every supply-chain decision is a wager on the state of the ﬁrm and

its market at the moment the goods ﬁnally move. Because shifting

or transforming atoms always takes time—sometimes hours, often

days—the decision must reach into a future that is fundamentally

uncertain. That future may mirror today’s assumptions, or it may

diverge sharply, turning a careful plan into a loss or, with equal

ease, into an unexpected windfall.

For example, when a company places an order with an overseas

supplier, it must navigate multiple uncertainties. Production and

transportation delays are inherently unpredictable, as neither the

manufacturer nor the carrier is entirely reliable. Furthermore, the

quantity of goods received might be diminished due to random

transit damage. Finally, customers’ future willingness to pay

for these goods remains uncertain—further complicated by the

company’s lack of inﬂuence over competitors’ pricing strategies

during and after delivery.

A brief comparison between the isolated manufacturing process

8 CHAPTER 1. PRIMER

and supply chain as a whole clariﬁes the nature of variability. On

the factory ﬂoor, any deviation from the nominal thickness of a

steel sheet, the purity of a reagent, or the timing of a pick-and-

place arm is a defect. Variability is an enemy that can always be

beaten—at a price—and the price keeps falling as sensors, actuators,

and algorithms push control ever closer to the atomic scale; the

semiconductor industry is proof enough. Given suﬃcient capital,

the residual spread around the target mean can be driven arbitrarily

low. In supply chain, the situation is inverted: demand, lead

times, supplier reliability, and competitive moves are produced by

countless independent agents whose behavior cannot be engineered

away. No amount of money can purchase a deterministic future.

The only rational stance is to expect variability, cultivate options,

and design decisions that turn uncertainty into proﬁt rather than

loss.

Indeed, supply chains are exposed to a broader world on a

scale far beyond that of a closed manufacturing environment. No

company can shield itself entirely from external forces—be they

from clients, suppliers, regulators, the military, or competitors. In

addition to man-made factors, natural events—such as snowstorms,

ﬂoods, forest ﬁres, tsunamis, earthquakes, and pandemics—still dis-

rupt supply chains, even though the human toll such catastrophes

exact has, thankfully, declined markedly over the last century.

Unlike in a manufacturing process, where variability is invari-

ably a net negative, supply chain variability can often be trans-

formed into a competitive advantage. Consider a bottled-water

producer that intentionally builds ﬁlling capacity far above its

average demand. Most of the time the extra lines sit idle; yet

when a heatwave strikes—an episode that recurs only a handful

of summers per decade—the company can surge output overnight

while competitors ration stock. The transient spike in volume,

coupled with the premium consumers willingly pay under swelter-

ing temperatures, more than repays the dormant steel. Surplus

capacity here is an option bought in advance precisely to monetize

extreme weather—a textbook illustration of variability turned into

proﬁt.

1.5. MASTERY 9

In short, whenever variability brings risks, it simultaneously

opens up opportunities. In fact, risks and opportunities reveal

more about an observer’s vested interests than about their intrinsic

nature. Tour operators organizing trips to Mount Etna have a

vested interest in the volcano’s continued activity. For them, unlike

the people of ancient Pompeii, an active volcano is not a disaster

but a crucial source of income.

1.5 Mastery

Mastering a supply chain means nurturing favorable options while

committing just enough resources to remain proﬁtable today and

resilient tomorrow. Such mastery is never the feat of a lone planner;

it crystallizes through the coordinated work of many disciplines

acting in concert.

Even in well-aligned organizations, ﬁve stubborn traits keep

supply chains hard to tame: intangibility, mediation, complexity,

connectivity, and wickedness. The pages that follow examine each

trait in turn and show how it can be transformed from a hindrance

into a lever of competitive advantage.

All visible assets—factories, trucks, pallets, even people—matter

to the supply chain only through the intent that animates them.

Supply chain is thus fundamentally intangible: a set of expectations

about future exchanges of goods, money, and service.

Consider a single bottle of milk resting on a supermarket shelf.

Tangible though it is, its presence is justiﬁed only by a pair of

overlapping wagers. The retailer bets that a customer will buy

the bottle before it spoils; the customer bets that the retailer will

keep the shelf stocked. These bets remain silent when they succeed

and become painfully audible only when they fail—when the milk

expires or the shelf is bare.

This intangibility is the ﬁrst obstacle to mastery. Expectations

never appear on a balance sheet; they must be inferred from their

symptoms—stockouts, overstocks, lead-time drift, margin erosion.

Like economists, supply-chain practitioners advance by surfacing

10 CHAPTER 1. PRIMER

the latent assumptions buried in every MOQ, every replenishment

cycle, every promotional calendar. Mastery begins only once those

assumptions are made explicit, quantiﬁed, and challenged.

Modern supply chains cannot be observed directly; every glimpse

is mediated by software records. What planners read as on-hand

inventory, open purchase orders, or transit quantities is only a dig-

ital representation, assembled by an applicative landscape pieced

together over decades. Crucially, this landscape did not arise

as a scientiﬁc experiment designed to capture the most relevant

data—contrary to what many academic treatments imply—but as

a patchwork of tools built to keep operations running day after

day. Consequently, the landscape dictates both the visibility and

the vocabulary of the ﬂow; the atoms themselves remain one step

removed.

Practitioners often worry about numerical inaccuracies—an

errant cycle count, a late barcode scan—yet such errors are minor

and usually contained. The real hazard is semantic drift. A

ﬁeld that meant confirmed_order yesterday may, after a vendor

patch or an improvised user workaround, capture something quite

diﬀerent tomorrow while its label stays unchanged. Meanings

mutate faster than counts, and distortions multiply as data hops

between systems. Each record is therefore a hypothesis about

reality, not reality itself, and must be revalidated continuously.

The complexity of supply chains far surpasses the capacity of

any single mind, regardless of the eﬀort invested. Even modest

ﬁrms now juggle hundreds of thousands of SKUs (stock-keeping

units). Part of this proliferation is accidental—legacy regulations,

haphazard processes, badly patched software. Yet an ever-larger

share is intentional: intricacy deliberately introduced because it

augments the value proposition.

E-commerce makes the point vivid. Delivering a toothbrush

to a studio apartment entails last-mile routing, parcel tracking,

and reverse logistics—steps absent from a pallet drop at a hyper-

market. The extra layers are undeniably complex, yet they spare

customers the trip and therefore command a premium. In aviation,

engine makers no longer sell hardware; they sell “power by the

1.5. MASTERY 11

hour”, aligning perfectly with what airlines value while shifting

maintenance variability onto the manufacturer’s MRO network.

Likewise, a fashion brand that broadens its assortment from 1,000

to 10,000 SKUs complicates buying, stocking, and markdowns, but

the wider choice enlarges the addressable audience and cements

loyalty.

Such essential complexity earns its keep because it creates

customer value that can be monetized—provided it is proﬁtably

tamed. What remains—compliance quirks, phantom SKUs, policy

relics—is accidental complexity and deserves to be excised. Dis-

tinguishing the two, suppressing the accidental, and harnessing

the essential is one of the grand challenges of supply chain in the

century.

Supply chains operate as systems in the sense articulated by

Russell Ackoﬀ: the performance of the whole emerges from the inter-

actions of its parts, not from the isolated excellence of each. They

are therefore inherently interconnected. Every decision—however

small—ripples through the entire network. Consider a single unit of

inventory resting in a regional warehouse. The moment a planner

allocates it to Store A, all other stores are instantly deprived of

that option. A local relief—preventing a stockout at Store A—may

simultaneously seed future shortages at Stores B, C and D. This

zero-sum tension is structural, for every product, supplier, truck,

and minute of managerial attention draws upon the same ﬁnite

pools of resources.

These couplings are magniﬁed by economic forces. Customers

expect complementary items to be available together; carriers

quote full-truck rates, not pallet rates; a conveyor belt amortizes its

capital only when volume is consolidated. Attempting to “optimize”

one node in isolation merely displaces cost or risk elsewhere in the

chain. Such mechanical, divide-and-conquer reasoning—excellent

for machines—fails for supply chains because it ignores the feedback

loops that bind decisions across time and space.

Mastery therefore demands systems thinking: evaluating every

lever through its inﬂuence on the entire ﬂow, both now and in

the future. Only a company that sees warehouse allocation, truck

12 CHAPTER 1. PRIMER

scheduling, replenishment rules, and pricing as facets of the same

decision space can transform interdependency from a handicap

into an advantage.

The problems that supply chains face are wicked—not in the

moral sense, but in the technical sense coined by Rittel and Webber

to describe challenges that mutate as soon as one intervenes. A

wicked problem has no crisp formulation, no ﬁnal solution, and

every “ﬁx” reshapes the very system it tries to mend.

A concrete illustration makes this plain. Suppose a company

announces a bonus tied to “94 % forecast accuracy.” Once planners’

livelihoods depend on that ﬁgure, the fastest route is to shorten

horizons, lump SKUs together, or pad safety stocks until the

arithmetic behaves. Working capital soars, service slides, yet the

dashboard glows green. The measure became the target and thus

ceased to be a good measure—Goodhart’s Law laid bare.

The paradox runs deep. Supply-chain management needs ex-

plicit, numerical targets so optimizers—often software immune to

personal agendas—can pursue them relentlessly. Yet the moment

a target is announced, it broadcasts fresh incentives for employees,

suppliers, and even competitors to game the rules. No prescription

remains optimal for long, and any rule that is obvious, static, or

predictable will be undermined as agents adapt. Wickedness there-

fore overturns naive academic hopes of tidy “problem + resolution”

pairs: mastery lies in continually revisiting both the objectives and

the algorithms that chase them.

1.6 Parting thoughts

The deceptively compact deﬁnition of supply chain hides a radical

prospect. Once every lever that moves goods is captured as data,

decisions no longer wait for a planner to click “OK”; they can

be enacted, audited, and reﬁned by algorithms that run day and

night at negligible marginal cost. Automation—understood as

unattended, software-driven decision making—is poised to do for

white-collar schedulers what the assembly line did for manual labor.

1.6. PARTING THOUGHTS 13

Far from being mere labor arbitrage, such automation unlocks

productivity that spreadsheet-ridden organizations can scarcely

imagine. Many companies now employ more analysts grooming

spreadsheets than operators handling cartons, yet a single decision

engine can outpace both, slashing latency and error while freeing

scarce talent to tackle novel, higher-order challenges.

More importantly, software can roam decision spaces that hu-

mans never will. Continuous repricing during viral demand spikes,

mid-voyage rerouting of ocean containers, or continent-wide inven-

tory balancing every hour becomes tangible once computation—not

human patience—is the bottleneck. Just as e-commerce reinvented

retail rather than polishing telesales, unattended optimization will

reshape supply chains instead of merely accelerating yesterday’s

procedures.

The quantitative supply-chain literature amassed since the UNI-

VAC I (1951) overﬂows with intimidating algebra and petabyte-

hungry algorithms, yet it still fails to harness modern computing

to deliver tangible supply chain results. Its sophistication is mis-

directed: it optimizes what is easy to tally rather than what is

economically decisive, clings to brittle point forecasts, and col-

lapses when confronted with the combinatorial sprawl of real-world

options. By missing the target, it turns today’s near-free gigaﬂops

into background noise while planners continue to wrestle with

spreadsheets. The qualitative prescriptions on leadership have

fared no better: libraries are full of supply-chain “handbooks” that

few leaders read and from which fewer still would beneﬁt.

A striking symptom of this vacuum is the prevalence of self-

taught practitioners in large companies. Society rightfully tolerates

no self-taught surgeons—the gap between amateurs and trained

experts is too vast to risk human life. Yet when billions in inventory

are at stake, enterprises routinely rely on planners who have pieced

together their craft from tribal lore and blog posts. The comparison

is stark, and it underscores how underdeveloped the discipline

remains.

Mainstream methods have not merely underdelivered; in many

cases they have actively misled, promoting illusory precision and

14 CHAPTER 1. PRIMER

spreadsheet heroics in place of scalable science. Their shortcom-

ings—and the path to move beyond them—are examined head-on

in the ﬁnal chapter of this book.

The chapters ahead assemble the scientiﬁc building blocks

needed to encode supply-chain mastery into software and to unlock

the unattended optimization that tomorrow’s leaders will take

for granted. Replacing folklore with a precise, computer-ready

science is the prerequisite for turning variability and optionality

from recurring headaches into repeatable gains.

Chapter 2

History

Deﬁnitions of logistics, supply chain, and supply chain management

(SCM) diverge: some restrict “supply chain” to the physical ﬂow of

goods and reserve “SCM” for coordination; others treat the terms

as synonyms. Vendors, consultants, and practitioners twist the

terms further to ﬂatter org charts and marketing pitches; a brief

historical detour helps explain the resulting fragmentation.

Supply-chain ideas sit between two poles: Euclidean crispness

and AI’s shape-shifting jargon. Labels change every decade; the

underlying task—mastering optionality in the ﬂow of physical

goods—does not. Claims of expertise in logistics, operations re-

search, supply chain, or enterprise optimization often reveal more

about the claimant’s vantage point than about the ﬁeld.

This chapter serves a dual purpose. First, it retraces how the

loose, ever-shifting label supply chain emerged from earlier notions

such as logistics and operations research, showing that today’s

meaning is still provisional. Second, it illustrates the adversarial

mindset that will thread through the rest of the book. Below, each

buzzword is paired with the incentives that birthed it. Vendors

trumpet novelty to sell licenses; consultants repackage yesterday’s

ideas to sell slides; executives, anxious not to fall behind, adopt the

language before testing the substance. Sustaining this skeptical

posture protects practitioners from costly mirages while still letting

16 CHAPTER 2. HISTORY

authentic innovations surface.

2.1 Early 1800s to pre-war 1900s

Long before “logistics” or “supply chain” were coined, the activity

itself was the lifeblood of every city-state. Rome relied on African

grain ﬂeets, Amsterdam on Baltic timber convoys, and Edo on

rice barges timed to favorable tides. Each case posed the same

enduring puzzle: bridging, day after day, the spatial gap between

production and consumption amid uncertainty. The challenge en-

dures; the jargon and analytical tools change. The ﬁrst systematic

mathematical attempts surfaced only in the late 18

century—a

story we now turn to.

If the modern supply chain era means pursuing operational im-

provements analytically, it begins in that period. In 1781, French

mathematician Gaspard Monge published his Memoir on the The-

ory of Embankments and Rubble (Mémoire sur la théorie des déblais

et des remblais), proposing to minimize the labor required to move

soil for leveling terrain with both elevations and depressions.

The term logistics, once used interchangeably with supply

chain, took shape in a military context during the ﬁrst half of the

century. The earliest attested use—logistique—appears in the

Summary of the Art of War (Précis de l’Art de la Guerre) (1830) by

the Swiss oﬃcer Antoine-Henri Jomini

. Logistics meant moving

resources—people, equipment, materials—to the right place at

the right time. It reﬁned the longstanding concept of military

stewardship dating to antiquity.

In 1840, the British Post Oﬃce adopted the “Uniform Penny

Post”, an early civilian demonstration of what we would now call

operations research. Mathematician-engineer Charles Babbage

compiled cost tables showing distance contributed only marginally

to postal expenses; the real drivers were collection, sorting, and

ﬁnal delivery. Armed with these quantitative insights, Parlia-

ment accepted a nationwide ﬂat rate of one penny per letter, a

Jomini derives it from the French logis, “lodgings.”

2.2. WWII AND POST-WAR DEVELOPMENTS 17

decision that radically expanded the volume of mail and, by exten-

sion, the reach of commerce. Babbage’s study preﬁgured modern

supply-chain analytics by replacing folklore with measurement and

optimization. His later pursuit of programmable calculating ma-

chines—culminating in the Diﬀerence Engine—aimed to automate

exactly the statistical tabulations his postal work required.

In 1888, economist Francis Ysidro Edgeworth, of Irish and Span-

ish descent, articulated an insight later known as “the newsvendor

problem”. His idea, ﬁrst introduced in his Mathematical Theory

of Banking, described the challenge of determining the optimal

inventory level when excess stock must be disposed of at the end

of a period, as is done daily with unsold newspapers. The contri-

bution blended statistical risk analysis with operational problem-

solving—a perspective that would not become mainstream for

roughly a century.

The rise of a stock-owning middle class in the United States at

the end of the 19

century, in turn, gave birth to the early economic

forecasters. Among these pioneers, Roger Babson and Irving Fisher

rose to prominence at the start of the 20

century. They promoted

quantitative methods for allocating resources to individuals and

corporations—a spirit echoed by many modern enterprise software

oﬀerings. They also pioneered statistical time-series forecasting

techniques.

The evolution of economics in the ﬁrst half of the 20

century

spurred numerous methods for solving small operational puzzles.

Several of the attempted solutions led to theories and formulas

that would become enduring supply chain shibboleths. For exam-

ple, the economic order quantity (EOQ) formula—a supply-chain

staple—was introduced in 1913 by Ford Harris and popularized in

the 1930s as Wilson’s formula.

2.2 WWII and post-war developments

At the outbreak of the Second World War, the ﬁeld returned to its

military roots. Britain assembled nearly 1,000 men for what became

18 CHAPTER 2. HISTORY

operations research, deﬁned by the British Army as “a scientiﬁc

method of providing executive departments with a quantitative

basis for decisions regarding the operations under their control”.

The eﬀort delivered resounding results: fewer artillery rounds to

down enemy aircraft and higher survivability for British ships and

planes.

Buoyed by its wartime prestige, operations research rapidly

expanded in academic circles. The advent of general-purpose pro-

grammable computing hardware enabled the community to explore

methods that would have been utterly impractical to compute by

hand. Hardware limits kept most 1950s–60s research on static or

stationary problems—those assumed unchanged over time. For

example, determining the location of warehouses to service a re-

tail store network, if we assume the demand to be constant, is

a stationary problem. In contrast, optimizing warehouse alloca-

tion processes and dispatch routes is dynamic, as these problems

are sensitive to ﬂuctuations in demand, lead times, traﬃc, driver

availability, truck maintenance, and more. Dynamic problems

outstripped the hardware of the day and were largely set aside.

Although operations research initially captivated corporate

managers, its appeal began to fade in the 1970s. By the 1980s,

wartime prestige had faded and corporate interests had shifted

The 1970s brought novel advances, including computing hardware

capable of leveraging barcodes

. Coupled with Codd’s 1970 rela-

tional database, barcodes would reshape supply chains and their

digital landscape in subsequent decades. This convergence yielded

the inventory management system—an early precursor to modern

business software.

In software circles, “management” then meant the routine tasks

of lower-level managers. The so-called “management system” was

designed to replace the manager entirely. Although their work was

largely clerical, these managers belonged to an educated workforce

By the 2020s, operations research (OR)—in Americanized spelling—remains

productive, but mostly in designing solvers for mathematical optimization, with

little connection to business or supply chain practice.

Patented in 1952 by Norman Woodland and Bernard Silver.

2.2. WWII AND POST-WAR DEVELOPMENTS 19

adept at keeping meticulous, accurate records. Moreover, these

white-collar workers held positions of authority and prestige that

surpassed those of blue-collar workers responsible for physically

moving goods.

With the emergence of management systems, software vendors

soon recognized that supply chain management encompasses two

distinct facets: bookkeeping and decision-making. Bookkeeping

involves electronically tracking stock levels and handling routine

clerical tasks. Decision-making involves determining the optimal

inventory orders and allocations for the company. Vendors initially

aimed to automate the managerial role; bookkeeping yielded readily,

but decision-making proved vastly more complex—well beyond

1970s hardware.

Nevertheless, “bookkeeping” lacked the allure of “management”.

Software vendors opted for the more marketable term—even at

the cost of clarity—because it bolstered their product image. Sub-

sequently, the software industry widely adopted the term “manage-

ment” in naming systems: WMS (warehouse management system),

CRM (customer relationship management), PLM (product lifecycle

management), etc.

The ubiquity of “management” eventually diluted the prestige

of the title “manager”. In the 2020s, the title “manager” frequently

implies managing only oneself—as seen in roles like project man-

ager, oﬃce manager, or account manager. Conversely, employees

with genuine hierarchical authority typically beneﬁt from job titles

that are more speciﬁc than “manager”. Instead, these employees

might be assigned titles like “lead” or “leader”.

Thus, “supply chain management” denotes the activities of

managers in the traditional corporate sense—individuals with

formal authority. In contrast, “supply chain” is broader, naming

both the practice and the academic ﬁeld.

20 CHAPTER 2. HISTORY

2.3 1980s to early 2000s

By the 1980s, software vendors expanded their scope beyond in-

ventory to encompass orders, payments, salaries, and more. Dis-

tributed software was not yet economically viable for vendors.

Thus, if a company was to acquire one system to bookkeep inven-

tory and another to bookkeep salaries, the later integration of the

two systems was usually deemed impractical. Vendors therefore

consolidated functions into a single mainframe monolith.

From post-war fame, logistics had slipped behind ﬁnance and

marketing in prestige. A blunt aphorism circulating in large

consumer-goods companies at the time summed up the prevailing

hierarchy: “Smart people go to marketing, reliable people to pro-

duction, and those lacking both qualities to logistics.” Although

clearly colored by amicable rivalry between corporate vocations,

the saying captured a widespread perception of diminished status

within the logistics function.

One explanation is that, until the 1980s, logistics was largely

empirical. The initial promise of operations research to transform

logistics into a modern analytical science had faded. In 1979, Rus-

sell Ackoﬀ, an American pioneer of the corporate era of operations

research, summarized the situation in his paper The Future of

Operational Research is Past:

Operations Research is dead even though it has yet to be

buried. I also think there is little chance for its resurrection

because there is so little understanding of the reasons for

its demise [...] The life of OR [Operations Research] has

been a short one. It was born here late in the 1930s. By the

mid-60s it had gained widespread acceptance in academic,

scientiﬁc, and managerial circles. In my opinion this gain

was accompanied by a loss of its pioneering spirit, its sense

of mission and its innovativeness. Survival, stability and

respectability took precedence over development, and its

decline began.

In the 1980s, decision-making stagnated while bookkeeping

ﬂourished; by decade’s end, many companies had digitized their

transactional operations. Electronic records enabled new ways

2.3. 1980S TO EARLY 2000S 21

to monitor operations; simple totals and averages, recalculated

continuously, began to reshape corporate practice.

Having made modest early-20

-century debuts, consultancy

ﬁrms became corporate giants by the early 21

century. Among

many causes, the spread of electronic records in the 1980s signiﬁ-

cantly boosted management consulting.

Despite the mundane observation that the sole occupation of

consultants appears to be wrestling with spreadsheets and slides

the corporate rationale is that consultants are in the business of

selling impact. Those electronic records created numerous oppor-

tunities to enhance a variety of processes without resorting to any

grand level of analytical sophistication.

Consulting’s perennial challenge is diﬀerentiation. As consult-

ing comes with low entry barriers, it is diﬃcult to maintain an

oﬀering that remains distinct from what the competition is oﬀering.

Thus, it is hardly surprising that inﬂuencing the evolution of cor-

porate language is highly appealing to consultants. A now-classic

move is to coin—or at least adopt—new terms every few years to

refresh the oﬀering and brand. The authority gained from being

ostensibly linked with a novel, trendy buzzword creates a highly

desirable competitive edge, at least until competitors inevitably

replicate it.

Supply Chain Management (SCM) was coined by the British

logistician-turned-consultant Keith Oliver in 1982. Sales and Oper-

ations Planning (S&OP) was coined in 1985 by American software

engineer-turned-consultant Richard Ling. Both perspectives re-

volve around the idea of seeking end-to-end alignment of the various

corporate functions. Both practices would have remained abstract

without the increasing availability of digital corporate records.

Backed by software vendors, consultant-advocated practices

spread and demanded new skill sets. Logistics directors became

responsible for producing time-series forecasts and tuning service-

level parameters, as exempliﬁed by the safety stock formula. These

new, more analytical responsibilities marked a signiﬁcant departure

Also invented in the 1980s.

22 CHAPTER 2. HISTORY

from traditional logistics directors, who were primarily ﬁeld leaders

capable of managing large teams of warehouse workers and truck

drivers (no small feat).

By the 1990s, large vendors integrated a bewildering array of

bookkeeping processes into software monoliths. For clarity, these

products should have been named ERM (‘Enterprise Resource

Management’); however, this terminology never caught on. By

then, computing hardware had advanced so much that the decision-

making aspect was no longer considered intractable. Enterprise

clients soon realized that, despite the claims, these tools were

limited to bookkeeping.

Nevertheless, many vendors began—or resumed—investing in

the decision-making aspects of supply chain. These vendors wanted

to avoid confusion with bookkeeping competitors, so they diﬀeren-

tiated their oﬀerings. For this reason, vendors, assisted by market

analysts, coined the term ERP (Enterprise Resource Planning).

Within enterprise software, “planning” became the rallying cry

signaling decision-making capability. Indeed, with a master plan

that drills down to the most granular level—thanks to improved

computer hardware—every decision becomes, in theory, merely a

matter of clerical execution

. In practice, planning modules com-

bined point time-series forecasting with deterministic inventory

optimization. The intent was to deliver a technological solution

that would replace the lower-level manager entirely in one fell

swoop, rather than gradually.

Soon, competing vendors labeled their products ERPs regard-

less of how little planning they actually included. Granted, en-

terprise products are inherently complex, aiming to cover a wide

array of scenarios; opacity often extends to clients and even to

vendors themselves. As a result, vendors enjoyed considerable free-

dom in choosing which capabilities to showcase in their marketing

materials. One might even argue that a simple moving average

sales forecast qualiﬁes as ‘planning’, thereby justifying the ERP

This insight is incorrect – deeply so – but we will return to it at a later

time.

2.4. PRESENT DAY CONSID ERATIONS 23

label. Many vendors took advantage of this semantic legerdemain.

From the 1990s onward, new vendors focused exclusively on

decision-making, positioning themselves as an analytical layer

above the transactional layer oﬀered by ERPs (which arguably

should have been called ERMs). In the supply chain context,

these vendors broadly fell into two camps: the algorithmic and the

forensic. Both camps leveraged the same electronic records found

in preexisting business systems.

The algorithmic camp focused on time-series forecasting tech-

niques as well as the inventory optimization techniques underlying

the planning paradigm. Because ERPs already claimed planning,

marketing extra “planning” was diﬃcult; emerging vendors, aided

by analysts, coined “Advanced Planning System” (APS). In other

words, “yes, your ERP has planning already, but our complemen-

tary solution has advanced planning.”

The forensic camp concentrated on producing business reports

designed to help managers make better decisions. ERPs already

featured “business reporting”, so software vendors—again with

analyst help—popularized “business intelligence” (BI) for this

class of tools

. Technically, BI’s multidimensional cubes marked

a notable departure from Codd’s relational model two decades

earlier.

2.4 Present day considerations

In 2025, the descendants of both camps (algorithmic and forensic)

remain active. Within the algorithmic camp, integrated busi-

ness planning is promoted with an emphasis on machine learn-

ing—pitched as a superior descendant of earlier statistical tech-

niques. In the forensic camp, some promote concepts such as “sup-

ply chain digital twins” or “supply chain control towers,” which

are aimed at providing insights to company leadership.

All this shows that creatively rebranding existing products with

Here “intelligence” means collecting and presenting data; by any reasonable

standard those tools are not intelligent.

24 CHAPTER 2. HISTORY

the latest buzzwords—a practice traceable to the 1970s—remains

prevalent. The case of “planning” capabilities was merely a minor

skirmish in the history of enterprise software. Computing hardware

and software have advanced at an impressive pace over the past

70 years. Due to continuous advances in hardware, technological

obsolescence arguably poses the greatest threat to enterprise soft-

ware vendors today. Newer hardware makes once-impossible feats

feasible—but typically demands complete software re-engineering

to exploit its capabilities. Given this reality, it is far easier—and

more cost-eﬀective—to rebrand one’s oﬀering than to completely

re-engineer it to fully leverage the latest technological develop-

ments.

Software vendors encounter a real-world example of the classic

prisoner’s dilemma. Collectively, vendors would beneﬁt from more

honest and transparent communication about their products. Mak-

ing promises that cannot be delivered inevitably leads to costly

problems for both parties. Yet, if one vendor exaggerates its claims

more than competitors, it gains a strategic market edge by making

its solution more appealing to clients. Overpromising and underde-

livering are thus unsurprising consequences of enterprise software’s

dynamics.

The lexical inﬂation soon reached the org chart itself. Supply

chain directors emerged in the early 2000s—sometimes as a simple

rebranding of the incumbent logistics director, sometimes as a

freshly minted role meant to champion cross-functional analytics.

By the 2020s, logistics directors seldom engaged in sophisticated

predictive analytics, whereas supply chain directors rarely handled

day-to-day warehouse operations.

Although advanced analytics—machine learning and mathe-

matical optimization—have been available for about three decades,

adoption in supply-chain divisions remains the exception; spread-

sheets still dominate decision-making.

Today, a logistician handles warehousing and transportation.

When logistics is outsourced, providers are known as “third-party

logistics providers” (3PLs), which receive an electronic order for

each stock movement. Ironically, although logistics was originally

2.5. MAINSTREAM PERCEPTION 25

founded on the principle of moving resources at the right time

and place, 3PLs proﬁt from ineﬃciencies—charging for inventory

movement and for storage duration. Thus, the greater the number

of movements and the longer the storage durations, the higher the

revenue earned by the logistician.

2.5 Mainstream perception

Over the last decade, the term “supply chain” has become increas-

ingly common in mainstream media. In news coverage, a “supply

chain” refers to a network of specialized companies that cooper-

ate—sometimes very closely—to deliver sophisticated products,

such as semiconductors or automobiles. News about a “supply

chain” typically emerges from negative events—such as a catas-

trophic port ﬁre—that not only aﬀect one entity but also ripple

through a network of interdependent companies, amplifying the

original impact.

Journalists frequently question what should be done to pre-

vent such problems from happening again in the future. While

legitimate, this perspective overlooks the absence of any central

authority governing those “supply chains”. Indeed, in this context,

a supply chain is composed of numerous uncoordinated compa-

nies. Furthermore, introducing an authority—such as through

government intervention—would likely worsen the situation, as

such an authority would lack the necessary information to allo-

cate resources eﬀectively. As Friedrich Hayek noted in The Use

of Knowledge in Society (1945), relevant knowledge is widely dis-

persed among countless individuals in disparate companies. No

single authority can ever fully harness this decentralized informa-

tion, and any attempt to impose a “captain” inevitably leads to

resource misallocation.

Such newsworthy failures are emergent properties of the econ-

omy’s division of labor. Any division of labor carries inherent

fragility; if a highly specialized segment falters, its repercussions

spread throughout the economy. The only alternative to this

26 CHAPTER 2. HISTORY

fragility is to renounce the division of labor, an unreasonable

proposition. Indeed, it would dramatically increase the cost of

physical goods consumed every day, from rolls of toilet paper to

smartphones. The tremendous beneﬁts of the division of labor have

been well-understood for centuries, with the ﬁrst formalization

dating back to the 19

century with the work of David Ricardo

and his law of comparative advantage.

Yet the division of labor characterizes markets as well: people

trade precisely because specialization beneﬁts both sides. The

"supply chains" observed by the media are merely groups of tightly

dependent companies that emerge due to the competitive advan-

tages conferred by their respective specialization.

Supply chains are also conﬂated with outsourcing, perceived as

“stealing” jobs from wealthier Westonia to beneﬁt poorer Ruritania.

Yet, in modern settings, gaining access to cheap labor overseas

is rarely more than a secondary concern. If supply chains were

solely about accessing the cheapest labor, then Africa would be a

prime supply chain partner for advanced nations. Yet, the opposite

is true: advanced nations primarily trade between themselves.

The least-developed countries—often with the cheapest labor—are

largely absent from global trade.

In practice, infrastructure, technology, and capital requirements

usually overshadow pure labor considerations. For instance, the

containerization boom of the 1950s–60s drastically reduced mar-

itime shipping costs, but also demanded massive port upgrades

that only countries with robust capital investments and stable

regulatory frameworks could aﬀord. More recently, high-value

electronics require advanced robotics, rigorous quality standards,

reliable power, and specialized engineering talent—complexities

that dwarf the advantage of marginally lower wages. As a result,

countries with strong logistics networks, dependable legal systems,

and a critical mass of technical know

how beneﬁt from large trade

volumes with similarly developed nations, whereas regions lacking

these assets often remain largely disconnected from modern supply

chain opportunities.

2.6. LOOKING AHEAD 27

2.6 Looking ahead

Supply-chain terminology remains in ﬂux, and vendors are more

active than ever. Some consultancies advocate the “agile”, “dy-

namic”, and “holistic” supply chain—and for “supply-chain design”.

Some forgo the “supply chain” suﬃx altogether, as in ﬂowcasting or

Demand Driven Material Requirements Planning (DDMRP). All

these paradigms address similar problems, although their methods

and techniques diﬀer signiﬁcantly.

Given that over a million scientiﬁc papers and more than ten

thousand books have been published in the last 70 years, the ﬁeld

of ‘supply chain’ is generally regarded as mature. Yet companies

still rely on spreadsheet heuristics that bear little resemblance to

mainstream supply-chain theories.

One could blame practitioner ignorance of the vast literature;

the charge is hardly tenable. Most large companies with an inter-

national footprint operate under ﬁerce competitive pressure. Given

how little mainstream theory has evolved over the last half-century,

claiming persistent corporate ignorance strains credulity.

A simpler proposition is that mainstream theory is inadequate:

practice deviates because the theory is ﬂawed and the anticipated

proﬁts fail to materialize. In other words, despite its vast literature,

supply chain remains in a pre-scientiﬁc stage where knowledge fails

to yield consistent and predictable results.

Over seventy years, bookkeeping layers leaped forward—barcode

scans, real-time databases, cloud ledgers—yet the rules deciding

what to buy, make, move, or stock still resemble mid-century text-

books. Progress has been spectacular on the transaction side; on

the decision side it has stalled. The root is not technical but

epistemic: the ﬁeld lacks a robust way to separate durable insight

from fashionable doctrine.

Explaining—and ultimately closing—this gap requires a pause

in the historical narrative. The next chapter, therefore, shifts

from chronology to epistemology. Before prescribing remedies, we

will examine how claims are generated, who beneﬁts from which

narratives, and what tests ideas must pass before governing a single

28 CHAPTER 2. HISTORY

pallet. Only once these standards are explicit can progress in

decision-making—interrupted for ﬁve decades—resume in earnest.

Chapter 3

Epistemology

Improving a supply chain begins with improving the knowledge

that steers it. This chapter therefore shifts from the mechanics

of moving goods to the epistemic question that precedes every

recommendation: How do we decide that an idea about supply chain

merits belief, funding, and deployment? Answering this question is

not idle philosophy; it is the only safeguard against the fashionable

doctrines, glossy slides, and “proven” algorithms that routinely

consume budgets while leaving performance unchanged.

Supply-chain knowledge is unusually error-prone. Uncertainty

blurs the feedback loop between decision and outcome; incentives

reward conﬁdent storytelling over careful testing; and the sheer

physical latency of global ﬂows makes controlled experiments rare.

As a result, spreadsheets still dominate day-to-day practice while

journals and books overﬂow with theories that claim—on paper—to

be superior. The tension is epistemic, not technical: we lack a

robust method for ﬁltering durable insight from transient hype.

This chapter therefore pursues three objectives:

Classiﬁcation. We locate supply chain on the map of

knowledge—as an applied branch of economics—and show

why mistaking it for mathematics, sociology, or mere record-

keeping leads to systematic failure.

30 CHAPTER 3. EPISTEMOLOGY

Validation. We examine what counts as evidence in a

domain where randomized trials are scarce, counterfactuals

dominate, and success is measured in proﬁt rather than

elegance or consensus.

Corruption. We analyze how actors—academics, consul-

tants, software vendors—despite good intentions, unwittingly

distort the knowledge pipeline, and outline safeguards that

preserve integrity amid adversarial incentives.

Throughout this book, the singular “supply chain” denotes

the discipline, whereas the plural “supply chains” refers to the

concrete ﬂows run by companies. The discipline’s role is to arm

any given chain—not an idealized archetype—with decisions that

raise long-run proﬁt under uncertainty. Conversely, extending the

term to whole markets drifts into general economics and blurs the

practical lens adopted here.

By the end of the chapter, the reader will possess a yardstick

for judging every subsequent claim—algorithmic, organizational,

or technological: Does this idea allocate scarce resources more

proﬁtably when variability and adversarial incentives are accounted

for? Establishing that yardstick is the prerequisite for the analyses

that follow.

3.1 Economics

Supply chain must be understood as an applied branch of eco-

nomics, not a free-standing discipline. Overlooking this hierarchy

is perhaps the most frequent—and most severe—epistemic error in

the literature. This single error explains much of why mainstream

supply-chain knowledge proves barren in practice.

Regrettably, economics itself is frequently misunderstood, even

though its core ideas are quite simple. The British economist Lionel

Robbins gave what is typically regarded as the classic deﬁnition of

economics:

3.1. ECONOMICS 31

Deﬁnition (Economics).

The study of the use of scarce resources which have alter-

native uses

An Essay on the Nature and Signiﬁcance of Economic Science (1935),

Lionel Robbins.

Robbins’ sentence is deceptively dense; each word carries weight.

Resources encompass far more than cash. Factory hours, pal-

let space, truck capacity, managerial attention, even goodwill

with a supplier all qualify. Whatever can be employed to satisfy

a want—or withheld to satisfy another—enters the economist’s

ledger.

Scarcity is the universal constraint. Desires outrun means in

every society, whether a medieval manor starved for arable acres, a

planned economy constrained by steel quotas, or a venture-backed

startup racing against its burn rate. Scarcity is not a quirk of

capitalism; it is the human condition.

Alternative uses encode the bite of opportunity cost. A ton

of copper rolled into power cables cannot also be stamped into

cartridge cases; an hour of a planner’s focus spent chasing a late

shipment cannot reﬁne next season’s assortment. Every allocation

vetoes its rivals, and that foregone beneﬁt is the hidden price of

every choice.

Because these three elements—resources, scarcity, and alterna-

tive uses—persist under feudalism, socialism, or capitalism alike,

economics remains valid regardless of the social order. The mecha-

nisms that resolve scarcity diﬀer (prices, queues, decrees), but the

underlying calculus of trade-oﬀs does not. Supply-chain practice

must therefore rest on economic bedrock, no matter how the ﬁrm

is owned or how markets are organized.

Set against our deﬁnition of supply chain, the relationship

becomes clear:

Supply chain is the mastery of optionality under variability

in managing the ﬂow of physical goods.

Supply chain fundamentally parallels economics in its treatment

32 CHAPTER 3. EPISTEMOLOGY

of scarcity. Here, “optionality” names the alternative uses the

company must ﬁrst cultivate and then exploit. Each supply chain

decision utilizes a scarce resource with alternative potential. Every

item bought contends with all other possible purchases, and every

item manufactured competes with alternatives that could have

drawn on the same materials. Similarly, each site receiving an

extra dispatch unit vies with all other sites for that single item

from the warehouse.

In summary, if we focus solely on allocating scarce resources,

supply chain would be indistinguishable from economics. However,

supply chain diverges from economics in several important ways.

First, “mastery” denotes the proﬁt-seeking stance of companies.

Supply chain is an applied science; unlike the broader ﬁeld of

economics, it aspires to be prescriptive rather than merely descrip-

tive. While economics explains why some individuals are wealthier

than others, it does not claim to directly guide individuals toward

greater wealth—except indirectly through a better understanding

of market mechanisms.

Furthermore, the scope of supply chain is restricted to the

ﬂow of physical goods—a small subset of economics. For example,

intellectual-property questions—patents and copyrights shaping

production choices—belong to economics, except where they con-

strain production despite adequate physical resources.

Moreover, the scope narrows further with the element of vari-

ability; without variability, an issue would cease to be a supply

chain concern. For instance, consider a warehouse process that

can be nearly perfectly automated, setting a strict upper bound

on pick-and-pack time—provided the arrival rate stays below a

threshold. In such cases, supply chain accepts the automation as a

given, without scrutinizing the economic decisions that enabled it.

Properly classifying supply chain is crucial, because the dis-

cipline is routinely approached through misguided lenses—sterile

mathematics, clerical bookkeeping, or managerial platitudes—while

its genuine foundation is, and always has been, economics.

Contrary to popular belief, economics is far from a “dismal

science”. Granted, it cannot achieve the numerical certainty of

3.1. ECONOMICS 33

most natural sciences; the free will of participants precludes that.

That said, many economic principles can be considered as solidly

established as any scientiﬁc theory can be.

Consider the well-documented case of rent controls. When

a government caps rents below the market-clearing level, several

predictable outcomes follow. Property owners lose the incentive

to maintain or improve existing units, as they cannot recoup

those costs through higher rents. Developers become reluctant to

build, and long-term shortages ensue. Shortages lengthen waiting

lists, reduce choice, and degrade housing quality. Such eﬀects are

observed time and again across diﬀerent cities and eras, illustrating

how economic principles remain remarkably consistent, regardless

of cultural and temporal diﬀerences.

A nearly identical mechanism appears inside individual ﬁrms

whenever a product is sold below its market-clearing price. Imagine

a consumer-electronics manufacturer launching a new console at

a price intentionally set 20% below the market-clearing level in

the hope of “delighting the customer”. Demand instantly over-

whelms the short-term production capacity. Retail channels are

wiped out, waiting lists form, and—most revealingly—gray-market

intermediaries (scalpers) purchase the scarce consoles at list price

only to resell them online at the actual market price. The margin

the company voluntarily relinquishes is captured in full by these

middlemen, while end-customers still pay the higher amount. The

ﬁrm forfeits revenue, frustrates loyal clients, and tarnishes its brand

by appearing disorganized.

Unless the company can raise output fast enough to meet the

unexpected surge, the economically sound remedy is to remove the

self

imposed price ceiling and let prices rise until demand equals

capacity.

Any other policy merely subsidizes an ecosystem of

arbitrageurs whose sole contribution is to pocket the diﬀerence

between the constrained price and the true clearing price.

The 2020–2021 shortage of next

generation game consoles illustrated this

dynamic: resale prices on major platforms frequently reached double the manu-

facturer’s suggested retail price.

34 CHAPTER 3. EPISTEMOLOGY

The laws of economics are no easier to evade than the laws of

electromagnetism. Failing to characterize supply chain as applied

economics yields, at best, theories and practices rightly ignored by

practitioners, and at worst, decisions that harm ﬁrms.

3.2 Mathematics

In academic circles, a prevalent mistake is to regard supply chain

as a branch of mathematics. The book Fundamentals of Supply

Chain Theory (2019) by Snyder and Shen is a canonical exponent

of this misguided path. Over the past seven decades, despite over

a million published papers

, academic supply chain literature has

contributed little to real-world practice. Supply chains remain,

with few exceptions, governed by spreadsheets. A company is more

likely to proﬁt from buying lottery tickets than from adopting any

“provably optimal” inventory optimization technique. Nonetheless,

thousands of researchers seem to have devoted their careers to this

approach. It is astonishing that the community has persisted in

such a massive error for so long.

When supply chain is treated as a branch of mathematics, it

is reduced to a series of mathematical puzzles. The newsvendor

model—where a newspaper vendor decides how many copies to

stock under uncertain demand, with unsold copies becoming worth-

less—is one of the oldest and most notable examples. Similarly,

the economic order quantity, deﬁned as the order quantity that

minimizes total holding and ordering costs under constant future

demand, stands as another classic example. Solving these puz-

zles typically involves analytical methods, probability theory, or

algorithms.

In this paradigm, the supply chain puzzle begins with a formal

characterization of the physical ﬂow of goods—the products, sites,

demand and lead-time equations, and constraints. From this

As of June 2024, Google Scholar returns 827,000 papers for “inventory

optimization techniques”. While there are certainly duplicates and false positives,

one million published papers is the correct order of magnitude.

3.2. MATHEMATICS 35

formalization, authors derive a mathematical solution—ideally

with provable properties or, at minimum, a heuristic validated on

(often synthetic) data.

Internal consistency is not enough; survival in contact with

reality is. What matters is whether a numerical recipe survives

contact with trucks, lead times, and customers. The criterion we

adopt—and its consequences for this book—are stated below under

Falsiﬁability.

Put plainly, a theory that survives solely by refusing to ex-

pose itself to reality tells us nothing about how trucks roll, how

customers buy, or how pallets wait. Internal consistency guaran-

tees correctness only inside the walls erected by the assumptions;

the moment those walls meet the ﬁrst promotional spike or the

ﬁrst customs delay, the “optimal” policy reverts to an untestable

conjecture.

When treated as a branch of mathematics, supply chain puzzles

remain detached from real-world conditions. For example, under

a few assumptions—stationary demand, constant lead time, non-

perishable goods, etc.—an inventory replenishment policy can be

proved optimal. No real-world supply chain can contradict such

a result: it is impossible to devise an experiment to challenge it.

The solution is based solely on internal consistency derived from

the problem’s assumptions.

Proponents of this approach would undoubtedly argue that the

puzzles were not selected at random. On the contrary, these puzzles

have been carefully crafted to accurately reﬂect the challenges of

real-world supply chains. Moreover, many papers claim support

from “real-world” datasets and report rigorous tests against them.

However, practice oﬀers a clear and forceful rebuttal of this

proposition. Companies neither use these supposedly “optimal”

methods to manage their supply chains nor adopt any of their

variants, not even as a source of inspiration. If these algorithms

were genuinely valuable, the community would see shocks akin

to those in software, where superior techniques—transactional

databases, cloud computing, deep learning—are rapidly embraced.

The claim that companies are unaware of these “modern” supply

36 CHAPTER 3. EPISTEMOLOGY

chain methods does not hold up under scrutiny either. Since the

late 1970s, enterprise vendors have packaged thousands of such

methods. Yet, they are invariably discarded, as supply-chain

practitioners swiftly revert to their spreadsheets.

Applying Occam’s razor, the simplest explanation is that these

mathematically neat “puzzles” fail to produce any repeatable,

proﬁt-relevant gain once confronted with the messiness of day-

to-day operations. Because they neither cut total cost nor raise

service levels in a way that outweighs their implementation bur-

den, practitioners quite rationally set them aside and return to

tools—even spreadsheets—that have proven their worth.

Finally, much of the apparent “progress” in this branch of

mathematics may be illusory. Research is just as vulnerable to

fashion as any other branch of knowledge. In the 1960s, explicit

analytical resolutions for supply chain puzzles were in vogue; in

the 1990s, parametric time-series models gained popularity; and in

the 2020s, Monte Carlo simulators are the trend. Authors focusing

on current scientiﬁc fads do not necessarily contribute to genuine

progress—or its absence—in the ﬁeld. Moreover, earlier trends

are labeled “outdated”, regardless of what those papers may have

contributed to real-world supply chains.

3.2.1 Falsiﬁability

Having established that the mathematical posture by itself can-

not steer a supply chain, we need a positive criterion for sorting

knowledge that deserves to govern a pallet from what does not.

A decisive perspective on complex theories emerged in the

ﬁrst half of the 20

century with the work of Karl Popper

. In

his lifelong attempt to characterize science, Popper introduced

the criterion of empirical falsiﬁcation. This criterion is central to

the present book. An anecdote from his life sheds light on this

criterion.

See “The Logic of Scientiﬁc Discovery” (1934) by Karl Popper, a landmark

in the history of science.

3.2. MATHEMATICS 37

In the opening chapter of his book “Conjectures and Refuta-

tions” (1963), Popper explains how his youthful experiences in

Vienna around 1919 shaped his idea of falsiﬁability as the hallmark

of science. He notes that whereas Einstein’s relativity made bold

predictions—and risked being refuted by observation—Marxism

(and also Freudian psychoanalysis) was perpetually “saved” by ad

hoc explanations whenever contrary evidence appeared. Physicists

testing Einstein’s theory welcomed, and even sought out, the pos-

sibility of falsiﬁcation, using observations—e.g., starlight bending

around the sun during a solar eclipse—to see if the theory held up.

Physicists sought maximal exposure to reality, pursuing the angles

most likely to tear a theory apart.

In contrast, Marxists would “immunize” their doctrine from

refutation by reinterpreting events to ﬁt the theory. For example,

Marxist theory predicted the proletarian revolution would occur

ﬁrst where factory workers were most numerous—i.e., Great Britain.

Yet the revolution occurred in Tsarist Russia in 1917, one of

Europe’s least industrialized countries. Instead of rejecting the

theory as disproved, Marxists revised the interpretation to explain

why the revolution would happen ﬁrst in the least industrialized

country.

Obviously, physicists were the ones with the correct intellectual

stance. Their intellectual inclination would end up delivering

two monuments of our present understanding of the universe,

namely general relativity and quantum physics. Two theories that

turned out extremely capable of making predictions of empirical

signiﬁcance. Conversely, the Marxist theory would keep being

endlessly refuted by dozens of countries that implemented reforms

inspired by this doctrine, and got dismal economic results as a

result, baﬄing their Marxist economists in the process

These observations led Popper to articulate falsiﬁability. It

highlights the radical asymmetry, in science, between truth and

falsehood. No theory can ever be proven true; it can only be

In contrast, Austrian economists were entirely unsurprised by those out-

comes. It was exactly what their theory predicted.

38 CHAPTER 3. EPISTEMOLOGY

disproved. Indeed, to prove a theory exhaustively—say, general

relativity—one would have to verify that every celestial body,

everywhere and always, follows the laws it states. This is obviously

a wholly impossible feat. At best we can gather a long series of

measurements in agreement with the theory, but beyond that, we

can only extrapolate the validity of the theory to the rest of the

universe. However, the converse is much more accessible: it only

takes a single pair of celestial bodies

contradicting the theory in

order to prove that the theory is wrong.

Thus, at best, we can only attempt to disprove a theory. How-

ever, for such an attempt to ever take place, the theory must expose

itself to an empirical validation process that could, in principle, fail.

Popper referred to such theories as falsiﬁable. It follows that the

longer a theory survives relentless experimental attack, the more

conﬁdence it earns. In contrast, an unfalsiﬁable theory cannot

be disproved; such theories do not belong to scientiﬁc knowledge.

Falsiﬁability is now widely recognized as one of the central tenets

of science.

Circling back to supply chain, falsiﬁability reveals what is wrong

with the purely mathematical posture: it is immune to refutation.

It belongs to applied mathematics, not to the natural sciences. As

a result, the correctness of the numerical recipes derived from this

perspective is shallow. It merely reﬂects consistency with an initial

bundle of assumptions. Those recipes are left entirely vulnerable to

the correctness of their premises, but also to even more mundane

issues.

Falsiﬁability matters because supply chain is judged in proﬁt,

not in elegance. A recommendation that cannot be shown false

when shelves stay empty or when inventory bloats is not operational

science—it is mathematical storytelling. Mathematical rigor is

indispensable, provided the premises invite empirical challenge.

The “anomalous” orbit of Mercury around the sun was instrumental in

rejecting the old Newtonian mechanics in favor of the relativity theory of Einstein.

In fact, there was no anomaly, merely an incorrect theory. Once relativity was

adopted, the anomaly vanished.

3.2. MATHEMATICS 39

3.2.2 The incentives of academia

Before examining concrete illustrations of the oversized assump-

tions that cripple these models, we must ask why academia keeps

championing them. The answer lies in the incentives that shape

scholarly careers: journals reward novelty and formal elegance—not

operational impact—so research naturally gravitates toward puz-

zles that are publishable rather than practicable.

In The Future of Operational Research is Past (1979), Russell

Ackoﬀ identiﬁes a series of issues undermining his ﬁeld. In contem-

porary terms, “operational research” is synonymous with “supply

chain”, and Ackoﬀ’s critiques remain entirely relevant today. Those

problems include:

1. Solving made-up problems;

2. Taking the objective for granted;

3. Ignoring system properties;

4. Assuming the future is knowable;

5. Being inward-looking, and increasingly so;

6. Rationalism passing for reason.

More than four decades later, it may seem astonishing that academia

still overlooks those foundational issues, especially given Ackoﬀ’s

signiﬁcant reputation. Yet this behavior becomes less surprising

when one considers the incentives.

Academics are primarily assessed on two tightly coupled axes

of evaluation. First comes the volume of papers produced and

the impact those papers achieve inside the scholarly echo cham-

ber—impact here meaning little more than how often other papers

cite them. Whether the cited idea ever trims a procurement bill,

shortens a lead time, or prevents a stockout does not register in

this metric.

40 CHAPTER 3. EPISTEMOLOGY

The second axis is teaching performance, which in practice re-

duces to the lecturer’s aptitude for transmitting that same citation-

oriented material to the next cohort of would-be scholars. This twin

yardstick creates a self-referential loop. Intellectuals are judged

solely by how well they persuade other intellectuals; classrooms are

staﬀed and syllabi written by those who have already mastered the

art of persuading peers. Empirical eﬀectiveness—the capacity of an

idea to survive contact with forklifts, tariﬀs, or customers—never

enters the equation.

Consequently, mathematical problems ﬁtting the supply-chain

theme oﬀer an inexhaustible stream of publishable statements and

resolutions. Adding constraints, modifying demand equations,

altering network topology, or incorporating additional terms into

objective functions produces nearly endless variants. Each variant

serves as ideal material for an academic publication. Moreover,

the speciﬁc details from a business willing to share even modest

datasets can provide a unique—and thus novel—perspective on

the subject. This inevitably fosters the illusion that resolving an

isolated puzzle holds signiﬁcance for the domain at large.

Thus, academia relentlessly treats supply chain as a branch

of mathematics because it is the most expedient way, in terms

of time invested, to get papers published. Furthermore, these

papers exhibit the fashionable hallmarks of “great science”—such

as equations, algorithms, and quantitative results. By categorizing

supply chain research as applied mathematics, authors avoid the

rigorous falsiﬁcation challenges that natural sciences routinely

confront.

In classroom teaching, because promotion hinges on student

evaluations and pass rates, instructors have every incentive to

present material that mirrors what their colleagues will set in

examinations—again privileging continuity over confrontation with

reality.

Also, mathematics is unusually easy to teach—assuming the

speaker understands the material. Unlike in the natural sciences,

no dubious compromises are necessary to ﬁt the presentation into

the allotted time. The speaker can simply tune the number and

3.2. MATHEMATICS 41

complexity of puzzles to ﬁt the clock.

Furthermore, when it comes to grading, mathematical puzzles

are maximally convenient. Their number and complexity can be

precisely adjusted to ﬁt the exam context, enabling nearly ﬂawless,

objective grading with minimal eﬀort. Unfortunately, this results

in puzzles that bear little resemblance to the messy, ambiguous,

and complex realities of actual supply chains. Intellectuals teach

future intellectuals the material authored by past intellectuals,

with no external milestone to break the circle.

No matter how poorly this approach—treating supply chain as

applied mathematics—performs in practice, it still addresses the

enduring concerns of academic career seekers. Thus, as Ackoﬀ illus-

trates, merely proving that this research approach is misguided is

insuﬃcient; the underlying detrimental incentives within academia

must be addressed—a task entirely beyond the scope of this book.

Let’s clarify that there is nothing inherently wrong with using

the supply chain theme in mathematics—or in ﬁelds such as anal-

ysis, algorithms, and statistics—if a mathematical problem is best

illustrated by a supply chain metaphor. For instance, consider the

famous Seven Bridges of Königsberg problem, solved by Leonhard

Euler in 1736. The challenge was to devise a route through the

city that crossed each bridge exactly once. Euler proved that no

solution exists, introduced the concept of graphs, and thereby

paved the way for the ﬁeld of topology.

Under these circumstances, a paper is worthy only if it possesses

intrinsic mathematical merit: Does it reveal a hidden truth or

structure? Is the exposition particularly elegant? Does the method

pave the way to solve other, seemingly unrelated, mathematical

problems? Otherwise, the work is deemed worthless. Simply

using a supply chain theme cannot confer merit on work that

fundamentally belongs to the realm of mathematics.

Any reader familiar with the literature will know that supply

chain papers meeting this standard are exceedingly rare.

42 CHAPTER 3. EPISTEMOLOGY

3.2.3 The pitfalls of safety stocks

Demonstrating the shortcomings of the vast literature that treats

supply chain as mathematics is daunting; with roughly a million

papers to challenge, the task borders on insurmountable—a vivid

illustration of Brandolini’s law, which notes that refuting nonsense

usually demands an order of magnitude more eﬀort than producing

it.

To keep the discussion manageable, we focus on one widely

accepted concept—safety stocks—to illustrate the general short-

comings of approaching supply chain as mathematics.

Safety stock is the buﬀer required to achieve a desired service

level for a given SKU (stock-keeping unit). Here, the service level

is deﬁned as the frequency with which a stockout is avoided during

a given period. The model assumes demand during lead time is

normally distributed and lead time is constant. These assumptions

yield a closed-form formula for the required stock.

Lead demand

Probability

Working stock = 5

(90 % service)

Safety stock = 1.81

10 % risk

Figure 3.1: Under the safety stock model, the reorder point is set as the

sum of the working stock (mean demand during the lead time) plus the

safety stock (scaled deviation of the demand during the lead time).

Since the calculation is of historical interest only, it is omitted

here. Suﬃce it to say, the model yields an explicit formula that

can be easily computed in a spreadsheet with minimal eﬀort from

both the programmer and the computer.

Yet safety stocks are fundamentally ﬂawed—“hazardous stocks”

would be apter—because the mischaracterization yields poor out-

3.2. MATHEMATICS 43

comes. As a silver lining, safety stocks serve as a simple litmus

test to identify gross incompetence in the ﬁeld of supply chain.

Safety stocks present three major issues, each of which is suﬃ-

cient to preclude satisfactory outcomes when applied in a real-world

supply chain. The ﬁrst two are categorical errors, while the third

is technical.

The ﬁrst—and most damaging—ﬂaw is methodological isola-

tionism. Safety-stock formulas examine every SKU as if it were

the only claimant on the balance sheet, striving to compute an

“optimal” quantity for that item in a vacuum. Yet each extra unit

ties up cash that could have funded stock for another product

or been deployed to any more proﬁtable use. Indeed, all SKUs

compete for the same ﬁnite pool of working capital, and any model

that ignores this rivalry is blind to the very trade-oﬀs it is supposed

to orchestrate.

Outside micro-companies, no supply chain deals with a single

SKU; ﬁrms typically manage hundreds, if not thousands. Moreover,

replenishing one SKU is fundamentally diﬀerent from managing

replenishment across many SKUs. The two problems are radically

distinct. Safety stocks fail even to acknowledge that SKUs cannot

be treated in isolation; their very deﬁnition sidesteps the problem

that must be solved: the allocation of money for inventory.

Second, even if we momentarily accept treating each SKU in

isolation, the safety-stock recipe remains hopelessly incomplete. It

hinges on picking a “target service level” — a percentage plucked

from thin air — and then treating this number as if it possessed a

built-in link to proﬁt. The formula never explains why 95% should

outperform 92% or 97%; it simply delegates the most diﬃcult

judgment call to managerial ﬁat and proceeds as if the matter were

settled.

Proponents sometimes reply that a higher service level will

“generally” boost proﬁtability, but correlation is not causation.

Set the level too high and dead stock accumulates the moment

demand shifts; set it too low and frustrated customers defect to

competitors. Because the mathematics permits any target between

0% and 100%, the practitioner ends where they began: facing the

44 CHAPTER 3. EPISTEMOLOGY

real economic question of how much capital to commit to which

items. The safety-stock apparatus does nothing to answer it.

The third—and ﬁnal—ﬂaw is statistical naïveté. Real-world

demand is almost never bell-shaped: it is sparse for long stretches,

then jumps without warning, and its extremes occur far more

often than a normal law would allow. Lead times show similar

unruliness, clustering around distinct modes—“on-time”, “customs

delay”, “supplier stockout”, and so on—rather than hugging a

single, symmetric peak. These patterns are the norm, not outliers,

and any model that ignores them starts from the wrong coordinates.

Textbooks and software cling to the normal curve because

it keeps the algebra tidy, not because it matches the data. In

principle one might swap in zero-inﬂated, heavy-tailed, or mixture

distributions to repair the theory, but doing so strips away its lone

virtue: simplicity. Once the arithmetic becomes opaque, the safety-

stock recipe loses its only practical appeal while still inheriting all

the earlier categorical mistakes. If a method is neither realistic nor

simple, nothing remains to recommend it.

The vacuum left by mathematically sterile academia is, alas,

quickly ﬁlled. Into it step consultants and business-book authors

who swing the pendulum to the opposite extreme: abandoning

numbers for narratives. Where the academic fallacy treats supply

chain as a puzzle bereft of incentives, the consultant fallacy treats

it as a story bereft of quantiﬁcation. Both camps ignore the

economic core, merely from diﬀerent ends of the spectrum. The

next section examines how this sociological turn, though wrapped

in inspirational prose, proves no more reliable than the equations

it claims to supplement.

3.3 Sociology

If the academic path sterilizes supply chain into abstract puzzles,

the consulting path swings to the opposite extreme: it recasts

the discipline as a catalog of sociological observations and lead-

ership bromides. Business consultants have therefore dominated

3.3. SOCIOLOGY 45

supply-chain publishing for the last four decades, framing every

problem in terms of culture, teamwork, and “best practices.” Their

output is vast—well over 10,000 titles appear under “Supply Chain

Management”

. Dynamic Supply Chains (2015) by John Gattorna

is a canonical example of the genre—and of its epistemic weakness:

persuasive anecdotes replace testable evidence, leaving the reader

with stories rather than substantiated knowledge.

This literature belongs squarely to the self-help genre. Its

intended readership is the corporate executive—or the manager

who hopes to become one—who believes that mastering a new buz-

zword will both lift company performance and accelerate personal

promotion. The consultant-author, for his part, adopts the posture

of a guru whose insight can be rented by the day.

Each book then unfolds along two predictable axes. The strat-

egy axis oﬀers a handful of slogans—typically three to ﬁve—that

promise to explain how one should “think” about the supply chain;

the discipline is sliced into neat quadrants, waves, or maturity

levels. The organization axis converts those slogans into a checklist

of rituals: steering committees, cross-functional workshops, respon-

sibility assignment matrices, and the like, all presented as turnkey

pathways to execution.

To sustain momentum, the narrative is laced with borrowed

authority: sound bites from Fortune 500 executives, quotations

from Navy SEALs about resilience, and blow-by-blow retellings of

last-minute sports victories. Besides brieﬂy enlivening the prose, a

sports anecdote—or an athlete’s quote—adds no evidential weight

to supply-chain arguments. Celebrity in athletics or entertainment

has no bearing on the allocation of pallets and purchase orders.

Such cameos create the illusion of battle-testing; in fact they deliver

color, not proof.

The genre relies on a familiar ﬁrst trick: buzzword inﬂation.

Terms such as “agile”, “digital twin”, or “AI-driven” are repeated

until they sound inevitable, yet their deﬁnitions remain elas-

Amazon.com search performed in 2025; the overwhelming majority of results

are authored by consultants rather than academics.

46 CHAPTER 3. EPISTEMOLOGY

tic enough to ﬁt any circumstance. Behind the shimmer, one

ﬁnds only evergreen truisms—“the world is changing, you must

adapt”—rebranded as revelations. Taxonomies that slice ﬁrms into

“leaders” and “laggards,” or “innovators” and “followers”, cloak

tautologies in ﬂow-chart form and create the illusion of depth

where none exists.

The second trick is manufactured urgency. Authors spotlight a

handful of allegedly neglected levers—supplier intimacy, customer

centricity, sustainability, culture—and intone that immediate es-

calation to the C-suite is vital. This “act-now-or-perish” framing

is impossible to falsify in a boardroom setting. It deftly diverts

attention (and budget) toward the consultant’s favored initiative

without ever weighing the opportunity cost imposed on all the

other constraints a functioning supply chain must honor.

The ﬁnal trick is borrowed authority. Case studies name-drop

admired brands, parade selective success metrics, and sprinkle

quotations from athletes or Special-Forces veterans. The implied

syllogism is specious: “Company X is famous; Company X endorses

this framework; therefore, the framework creates fame.” Correlation

is quietly traded for causation, and the failures that would break

the spell are screened out of view.

These rhetorical devices are not harmless entertainment; they

crowd out the quantitative reasoning that genuine improvement

requires. By wrapping platitudes in prestige and urgency, the

literature ﬂatters executives into feeling decisive while leaving the

actual ﬂow of goods untouched. Strip away the logos and the

motivational anecdotes, and nothing remains that can survive

analytical scrutiny.

In other words, the consulting canon treats supply-chain dys-

function as a human-relations problem whose resolution lies in

reshaping group behavior. Its favored variables are roles, rituals,

and narratives; its instruments are workshops, charters, and incen-

tive grids. This viewpoint is textbook organizational sociology: it

studies how collectives maintain cohesion, how status is negotiated,

and how norms are enforced, then prescribes ceremonies meant to

realign those social constructs. Pallets, containers, and lead times

3.3. SOCIOLOGY 47

appear only as props in a narrative whose real protagonists are

departments vying for inﬂuence. Whether tonnage actually ﬂows

more smoothly is deemed an emergent by-product of better social

harmony. This literature is therefore sociological in method and

aim—even if it rarely advertises itself as such.

Even if this literature contained genuine insights—which it sel-

dom does—treating supply chain chieﬂy as a sociological endeavor

leads improvement eﬀorts astray. Consider a thought experiment:

imagine a near-future platform in which a single autonomous artiﬁ-

cial intelligence (AI) ingests every demand signal, lead-time update,

and cost curve, then issues—without human intervention—every

purchase order, price change, and routing instruction. In that

world there is nothing left to “align” or “empower”; the entire scaf-

folding of committees, workshops, and incentive grids evaporates

overnight. Yet the economic substance of the discipline endures:

scarce resources must still be allocated under variability. The

AI would lean on principles of optionality, opportunity cost, and

risk—core economics—not on any theory of inter-departmental

politics. The thought experiment clariﬁes the hierarchy: orga-

nizational constructs are temporary workarounds we erect only

because people, slower and scarcer than CPUs, must coordinate.

Automation is already eroding this sociological wrapper—pricing

bots, routing optimizers, and allocation engines now perform tasks

that once ﬁlled entire departments—just as a laptop replaced the

actuarial tables that formerly justiﬁed platoons of clerks. Until

full automation arrives, humans will stay in the loop. The com-

pass that guides them must remain economic and operational, not

sociological.

Granted, some supply chain challenges still resist software.

Once the necessary steps are explicit, it is acceptable to rely on

human intelligence as a stopgap until state-of-the-art methods can

automate them. Where workload exceeds one person’s capacity,

add people and organize them accordingly; even then, sociology

should be a last resort. Indeed, to insist that an intellectual task

“requires human intelligence” is to admit: “I have no clear idea

how to solve this problem, but if push comes to shove, someone

48 CHAPTER 3. EPISTEMOLOGY

will ﬁgure something out.”

For more than two centuries, it has been a recurring mis-

take to treat human involvement as a virtue in itself. The Lud-

dites—British weavers and textile workers who opposed mecha-

nized looms and knitting frames in the early 19

century—are

the canonical example of this mistake. In supply chains, human

involvement in the daily micromanagement of millions of SKUs is

not inherently desirable. The absence of mechanization imposes

predictable limits—long processing delays, reduced reactivity, and

mediocre allocations—because operators cannot weigh all relevant

quantitative factors.

Taken together, these consultant-driven tricks explain why

glossy frameworks so often leave containers, prices, and purchase

orders exactly where they started. They turn resource allocation

into group therapy, mistaking harmony for proﬁt. To see the

hard cost of confusing sociology with supply-chain mastery, we

now examine the ﬂagship doctrine of this literature—Sales and

Operations Planning.

3.3.1 The pitfalls of S&OP

Sales and Operations Planning (S&OP) has been heralded for

four decades as the cure for functional silos, yet it epitomizes the

sociological fallacy just exposed. Its elaborate calendar of meet-

ings seeks consensus around a single—highly uncertain—forecast

and then asks every department to pledge allegiance. Renamed

variants such as integrated business planning (IBP) inherit the

same weakness: they treat pricing, purchasing, and capacity as a

corporate ritual rather than an economic optimization. Because

S&OP is both inﬂuential and symptomatic, it will serve as the lone

exhibit in the critique that follows, though the argument applies

equally to all its look-alikes.

Since the rise of the giant corporation in the 19

century,

specialization has supplied most of what we label “economies of

scale”. By narrowing their focus, teams deepen expertise, lift

productivity, and make outcomes more predictable. Yet every

3.3. SOCIOLOGY 49

new specialty opens another seam that must be stitched back to

the whole, and the stitching grows combinatorially harder as the

organizational tapestry expands.

When coordination lags, the misﬁres are dramatic. Marketing

can stoke demand that plants cannot meet, squandering advertising

budgets and disappointing customers. Manufacturing can ﬂood

warehouses with units the market will not absorb, forcing ﬁre-sale

discounts or outright write-oﬀs once carrying costs eclipse any

plausible margin.

Sales and Operations Planning (S&OP) was devised in the early

1980s as a ritualized cease-ﬁre between the factions just described.

Its promise was simple: convene every function around a single

calendar checkpoint, agree on one forward view of demand, and

cascade that view into synchronized supply, capacity, and ﬁnancial

plans. In theory, the meeting guarantees that the people who

create demand (sales, marketing, merchandising) and the people

who fulﬁll it (procurement, manufacturing, logistics) leave the

room committed to the same numbers and the same constraints.

The framework gained momentum in the 2000s when ERP ven-

dors and consultancies began selling S&OP dashboards, maturity

models, and benchmarking surveys. Despite the glossy tooling, the

mechanics remain spartan: a rolling 18- to 24-month horizon, a

consensus forecast sometimes styled “one set of numbers”, and a

monthly sequence of pre-reads and workshops that massage those

numbers until every vice president can sign oﬀ.

In historical perspective, S&OP is less a breakthrough than a

modern sequel to Alfred Sloan’s executive production conferences

at General Motors in the 1920s. The vocabulary has changed, but

the intent is identical: impose an institutional routine that keeps

functional empires from drifting apart when variability threatens

to pry them loose.

Despite its longevity, S&OP collapses under close inspection.

Four structural defects undermine the practice and show why,

from an epistemic standpoint, it cannot be trusted to steer scarce

resources.

50 CHAPTER 3. EPISTEMOLOGY

The forecast illusion. S&OP begins by assuming that a

single, time-series forecast of demand not only exists but is

precise enough to govern capacity, pricing, and inventory

months in advance. That premise treats an uncertain fu-

ture as a known constant, turning the hardest part of the

problem into a spreadsheet input. By disguising uncertainty

as certainty, the ritual severs the feedback loop that would

otherwise test whether those numbers ever deserved belief.

The decision vacuum. Having conjured a consensus de-

mand forecast, S&OP then hands oﬀ every quantitative

decision—procurement lots, production schedules, routing

plans—to whatever heuristics each functional silo already

uses. The framework contributes no economic criterion for

choosing one allocation over another; it merely notarizes

choices made elsewhere. Epistemically, this is bookkeeping

masquerading as optimization.

Ritual overreach. The monthly cadence of pre-meetings,

alignment workshops, and executive reviews is presented

as the cure for miscoordination, yet the cure scales with

headcount, not with the physics of the ﬂow. Every extra

SKU, lane, or promotion demands another slide deck, not a

sharper algorithm. The complexity is accidental—created by

meetings—rather than essential—created by atoms and lead

times.

Arbitrary taxonomies. S&OP partitions time into opera-

tional, tactical, and strategic buckets and reduces reality to

a two-axis “demand vs. supply” matrix. These partitions

are rhetorical conveniences, not empirical discoveries; they

can be rearranged at will without changing economic out-

comes. A theory whose parameters are free to ﬂoat cannot be

falsiﬁed and therefore cannot accumulate reliable knowledge.

Together these defects explain why companies that adopt S&OP

often return to homegrown spreadsheets: the practice oﬀers pro-

3.4. ECONOMIC HISTORY 51

cess theater without predictive power. A meeting can enforce

temporary consensus, but it cannot reveal which decision maxi-

mizes long-run proﬁt under variability and adversarial incentives.

Until a framework exposes its assumptions to empirical challenge

and grounds its prescriptions in opportunity cost, it belongs to

organizational folklore, not to the science of supply chain.

We now turn to economic history to examine how similar

epistemic missteps have played out in the past—and what lessons

they hold for the present.

3.4 Economic history

Economic history catalogs what actually happened—the prices

paid, the goods moved, the crises endured. It is indispensable for

context, yet, as Ludwig von Mises stressed in Human Action (1949),

facts do not interpret themselves. Only a prior economic theory

can assign meaning by tracing cause, eﬀect, and counterfactuals.

In short, economics must precede economic history; we ﬁrst need

a logical framework before we can usefully retell the past.

The same ordering governs supply chain. Dashboards brimming

with lead-time histograms, service-level charts, and inventory turns

are nothing more than chronicles. Without a theory of opportunity

cost and optionality, an analyst cannot tell whether a 20% stock

reduction was brilliance or negligence. Data become knowledge

only when ﬁltered through an economic lens; otherwise, the exercise

is bookkeeping masquerading as insight.

Yet in day-to-day practice, the distinction between chronicle

and explanation is routinely blurred. Analysts ﬁll dashboards,

vendors publish benchmark reports, and consultants parade before-

and-after case studies—all of which recount what did happen while

remaining silent about what could have happened under diﬀerent

choices. The record tells a story; it never diagnoses the mechanics

that generated that story.

Consider a retailer that advertises a 20% inventory reduction.

Absent a theory of opportunity cost, the headline is meaningless:

52 CHAPTER 3. EPISTEMOLOGY

was working capital truly released with no loss of service, or were

shelf-outs simply postponed to a quarter yet to be reported? The

ledger contains no rows for the sales that never materialized, the

assortment expansions never attempted, or the price experiments

never run. History records only the path taken, never the adjacent

ones left unexplored.

Benchmarks magnify the problem. Ranking companies by

“days of supply” or “inventory turns” invites imitation yet ignores

strategic asymmetries such as assortment breadth, service promises,

or pricing power. Copying the metric without reproducing the

context is cargo-cult optimization: the ritual is preserved, the

economics are not.

The lesson echoes the critiques leveled at mathematics and

sociology: economic history is an auxiliary source of insight, not

a foundation. Facts collected in arrears are indispensable, but

they must be ﬁltered through a forward-looking economic lens

that weighs counterfactuals and opportunity costs. Without that

lens, history becomes numerate storytelling—comforting to read,

powerless to steer the next container. Mastery therefore starts

with theory, tests that theory against experience, and only then

lets the ledger guide future allocations.

3.5 Auxiliary sciences

Auxiliary sciences are the disciplines that supply chain routinely

relies on for facts, proofs, or tools—statistics for uncertainty, com-

puter science for automation, empirical psychology for human bias,

aeronautical engineering for fuel eﬃciency, and so on. The phrase

comes from historiography, where since the sixteenth century ﬁelds

such as paleography or dendrochronology have been called auxil-

iary because they supply evidence without writing history. The

hierarchy is epistemic, not pejorative: supply chain is subordinate

to whatever durable truths those sciences establish—if statisticians

reﬁne the meaning of a conﬁdence interval or aircraft engineers

demonstrate a cheaper ﬂight proﬁle, the practitioner must adapt

3.5. AUXILIARY SCIENCES 53

at once. What matters is that the knowledge is reliable, not the

route by which it was obtained.

Subordination, however, does not imply fusion. Supply chain is

not a multidisciplinary stew of mathematics, software engineering,

psychology, and logistics. It remains a distinct applied branch of

economics whose sole mandate is to allocate scarce resources under

variability. Auxiliary sciences are consulted only insofar as their

ﬁndings help to fulﬁll that mandate; their internal debates, jargon,

and prestige contests can be safely ignored. A practitioner needs

just enough familiarity to choose the right tool and to notice when

it fails—no more, no less.

The required familiarity depends on packaging. When an

auxiliary result is embedded in a robust artifact—a barcode scanner,

an optimization engine, an integrated circuit—the supply-chain

expert can treat it as a black box and concentrate on the economic

trade-oﬀs. When the packaging is thin—as is still the case with

most behavioral-economics research—the expert must open the

box, grasp the limits, and compensate manually.

Historians oﬀer the analogy. Dendrochronology can date a

shield to the summer of 1519 and thereby refute any claim that

it was wielded at the 1515 battle of Marignano, yet no historian

confuses the study of tree rings with the writing of history. Supply

chain should cultivate the same discipline: exploit every auxiliary

science ruthlessly, but never surrender the steering wheel.

The subordination principle is clear when microbiology sets

non-negotiable boundaries. Laboratory work shows that most

food-borne pathogens enter exponential growth once product tem-

perature rises above 5 °C for more than a few hours. Cold-chain

guidelines therefore mandate that chilled meat leave the plant below

4 °C and never exceed 7 °C before it reaches the customer. Whether

planners can recite the growth curves of Listeria is irrelevant; once

microbiologists ﬁx these thresholds, the supply-chain expert must

decide whether to pay for reefer containers, run denser delivery

routes, or shorten the distribution radius—but the temperature

constraint itself is not up for debate.

Materials science oﬀers the same lesson. Compression tests re-

54 CHAPTER 3. EPISTEMOLOGY

veal that a standard 7 mm double-wall corrugated carton collapses

once the top load exceeds roughly 8 kN. A distribution center

that stacks such cartons six pallets high has therefore capped its

per-carton weight at about 18 kg—unless it upgrades to triple-wall

packaging, lowers the stack height, or invests in racking. The

auxiliary science supplies the boundary conditions; supply chain

weighs the economic trade-oﬀs that live inside those boundaries.

Supply chain defers to its auxiliary sciences because those ﬁelds

possess faster and sharper ways to separate truth from error. In

physics or chemistry a benchtop experiment, run before lunch,

can annihilate a bad hypothesis at negligible cost; results are

quick, repeatable, and decisive. Supply-chain claims, like those in

economics, can be vetted only in the messy world of plants, trucks,

and stores. Field trials stretch over months, burn real money, and

rarely deliver a crisp yes-or-no verdict. Because its feedback loop

is slower and noisier, supply chain must borrow the rigor of its

auxiliaries while accepting that its own knowledge will accrue more

gradually.

Nonetheless, supply chain does not inherit the objectives of

its auxiliary sciences; it merely buys their ﬁndings as raw inputs.

Aircraft engineers may prove that inserting a mid-route refuel-

ing stop trims fuel burn by eight percent on an ultra-long-haul

ﬂight, yet the practitioner must still weigh that saving against

longer transit time, extra landing fees, crew overtime, and possible

customer churn. The criterion is economic payoﬀ, not technical

elegance: every resource—jet fuel, hours, capital, goodwill—carries

an opportunity cost that must be priced before a decision is made.

Auxiliary sciences deliver facts; supply chain assigns value.

Supply chain may appear multidisciplinary because its daily

practice borrows statistics, computer science, mechanical engineer-

ing, and even behavioral psychology. This impression confuses

tools with objectives. As a science, supply chain is a branch of

applied economics whose single mandate is to decide how scarce

resources should ﬂow through space and time. Auxiliary sciences

merely furnish measurements, algorithms, or machines that serve

that mandate; they do not redeﬁne it. By contrast, the natural sci-

3.5. AUXILIARY SCIENCES 55

ences bleed into one another precisely because atoms know nothing

of the departmental walls that separate physics from chemistry or

biology—their borders are pedagogical conveniences. Supply chain

is therefore not multidisciplinary in the epistemic sense—only in

the practical sense that it consumes many imported results.

Whether a practitioner must dive into an auxiliary ﬁeld depends

on two levers. Economic leverage measures how much proﬁt hinges

on the ancillary knowledge; packaging quality measures how much of

that knowledge comes embedded in reliable artifacts. The higher

the leverage and the poorer the packaging, the more personal

mastery is required.

Economic leverage is the proﬁt sensitivity tied to an auxiliary

science. If tweaking a clean-room’s airﬂow rate saves only a token

amount per batch, leverage is low and the setting can remain a

ﬁxed procedure. By contrast, shortening the residence time of a

continuous chemical reactor by just ﬁve minutes can lift hourly

throughput by six percent, yet it also depresses conversion yield and

shifts the slate of co-products: the primary grade sells at a premium,

while the secondary and tertiary streams trade at a discount or

must be reprocessed. The extra volume, the downgraded yield,

and the altered co-product mix ripple through inventories, contract

allocations, and price tiers. Because the trade-oﬀ is large and the

control logic hides inside dense process diagrams, planners must

understand the reaction kinetics well enough to weigh capacity

against margin before authorizing the change. Deep expertise is

therefore required only where high leverage meets thin packaging;

elsewhere the auxiliary ﬁeld may safely remain a sealed module.

Conversely, integrated circuits illustrate superb packaging.

They embody a century of quantum mechanics behind a ﬁve-

volt interface; planners exploit the computational power without

ever studying band theory. Jet engines hide thermodynamics and

materials science just as neatly; a fuel-burn table is all a network

optimizer needs. At the opposite end lies empirical psychology.

Findings such as anchoring, loss aversion, and hyperbolic discount-

ing reach managers mostly as prose. Because the packaging is

thin while the leverage—pricing, promotion, negotiation—is high,

56 CHAPTER 3. EPISTEMOLOGY

supply-chain leaders must grasp the concepts themselves or risk

repeating the biases the ﬁeld documents.

A practical rule of thumb follows: deep expertise is needed only

where high leverage meets thin packaging. When one of those levers

is absent, the auxiliary science can stay a sealed module.

In short, auxiliary sciences push the frontiers while supply-

chain practice decides when those frontiers matter. Whenever

statistics yields a tighter interval, materials science a lighter pallet,

or computer science a faster solver, the economic leverage of the

corresponding decision shifts—and so must managerial attention.

Conversely, once a breakthrough is sealed inside a reliable artifact,

it can be treated as infrastructure and mentally oﬄoaded. The

craft is to keep a live map of which pieces still demand scrutiny

and which have crossed the “black-box” threshold. Neglect that

map and a vacuum appears, soon ﬁlled by exaggerated vendor

pitches and consultant folklore—a pattern examined next under

the heading of epistemic corruption.

3.6 Epistemic corruption

When a knowledge system importantly loses integrity, ceas-

ing to provide the kinds of trusted knowledge expected of

it, we can label this epistemic corruption. Epistemic cor-

ruption often occurs because the system has been co-opted

for interests at odds with some of the central goals thought

to lie behind it. Epistemic Corruption, the Pharmaceutical

Industry, and the Body of Medical Science (2021), Sergio

Sismondo.

Much of what passes for supply chain knowledge today is

corrupt—it fails to improve supply chains even as it claims to do

so. Yet no grand conspiracies, evil overlords, or bribes are at work.

Authors who produce severely biased supply chain material are

rarely dishonest—more often than not, they are unaware of the

problem. The bulk of the epistemic corruption plaguing supply

chain stems from structural perverse incentives. The remainder

3.6. EPISTEMIC CORRUPTION 57

of this chapter addresses enduring misconceptions whose damage

extends far beyond supply chain.

As an applied branch of economics, supply chain knowledge

promises wealth—making companies richer, sometimes wealthy.

Every supply chain practitioner or in-house team is driven to

develop superior knowledge to outcompete rivals. Consequently,

they have little incentive to publicize their discoveries; if they

uncover a counterintuitive yet eﬀective heuristic for inventory

optimization, companies prefer to protect it as a trade secret.

The most vocal purveyors of supply chain “truths” are those

who monetize advice—academics, consultants, and software ven-

dors—each operating under incentives that skew what they choose

to publish.

First, academia: funded by private or public sources, professors

vie for students and recognition through publications—a classic

case of “publish or perish”. Academic authors seldom have the

opportunity to verify their supply chain claims in practice; even

when they engage with companies, their careers remain largely

insulated from real-world outcomes. In fact, the tendency to view

supply chain as a collection of mathematical puzzles underscores

the inﬂuence of structural incentives.

Imagining and solving mathematical puzzles requires limited

ingenuity, especially when both the new puzzle and its resolution

are mere variants of known instances. Since the validity of the

resolution is purely a matter of internal consistency (as in any

mathematical proof), the author is relieved of the burden of seeking

real-world validation. Using real-world data in synthetic bench-

marks is little more than a smokescreen; it still does not show what

outcomes deployment would have produced.

Second, consultants, whose clients are corporate managers. Un-

like academics, consultants face the real-world consequences of

their theories, and this liability often becomes an asset. When a

manager doubts a project, a consultant is brought in as a buﬀer:

success earns the manager credit, while failure falls on the consul-

tant. Aware of this dynamic, managers rarely dismiss consultants

with mediocre records. More broadly, consultants typically avoid

58 CHAPTER 3. EPISTEMOLOGY

oﬀering ﬁrm recommendations, as their role is fundamentally to

never say “no” to a client.

Third, software vendors, including the present author. Engi-

neering enterprise software is a slow and messy process. The ﬁnal

product is usually opaque and convoluted—were it otherwise, it

would be marketed as a self-service product for individuals and

small businesses. The clients of software vendors are also managers;

however, the managers who select the vendors are typically not the

ones who use the product. Managers often consult subordinates

to make a more informed decision, and it is here that adversarial

incentives enter. Most software products are intended, at least in

part, to improve productivity, which often involves reducing head-

count. Relying on subordinate opinions systematically favors the

vendor that best preserves the status quo (and thus headcount).

Since managers often lack the time to acquire the expertise to

assess a product, they defer much of the evaluation to third-party

authorities.

Because of this dynamic, the case study has become the canon-

ical promotional instrument in enterprise software. Since so much

of the ﬁeld’s “evidence” now ﬂows through this channel, it warrants

close examination in the next section.

3.6.1 Case studies

Of the channels through which these adversarial incentives distort

evidence, one dominates: the corporate case study. Case studies are

elaborate infomercials intended for a corporate audience—nothing

more, nothing less. Yet many companies operate under the dan-

gerous delusion that case studies are truthful, especially when the

cited client is reachable; the reality is otherwise. At best, a case

study clariﬁes what the vendor is trying to sell; it cannot assess

how much the quoted client beneﬁted, let alone what the vendor

would deliver to another company. Let us dispel this delusion.

A “software” case study suﬀers from two intrinsic, indepen-

dently fatal ﬂaws: perverse incentives and selection bias.

There is no doubt the vendor is heavily incentivized to embel-

3.6. EPISTEMIC CORRUPTION 59

lish—magnifying beneﬁts and suppressing issues that undermine

returns. The client’s voice is meant to provide a reality check; in

practice, the client is just as incentivized as the vendor to embel-

lish. Indeed, an enterprise sale requires one or more executives to

champion the option. Those champions are eﬀectively vouching

for the vendor

. Moreover, their claims will be backed by highly

convincing metrics. Supply chains are complex enough to guaran-

tee that, no matter how dysfunctional the initiative, a shortlist of

favorable metrics can be identiﬁed. If needed, metrics can even

be quite blatantly forged. No one—outsiders least of all—except

those with intimate knowledge of the initiative will be in a position

to contradict the case study’s claims. Corporate case studies are,

by design, not reproducible.

In the presence of such an egregious conﬂict of interest magniﬁed

by a complete lack of accountability, bias is inevitable. Even

individuals of sound character, known for their honesty in daily

aﬀairs, can succumb to distorted incentives when reputations or

livelihoods are on the line. This is not to impugn their moral

ﬁber, but rather to recognize the powerful sway that professional

pressures, peer perception, and personal ambition can exert on

otherwise admirable people. When a project’s success translates

into career advancement, speaking invitations, or intra-company

prestige, even the most ethical may ﬁnd it nearly impossible to

be neutral in their assessments. The conﬂict of interest is simply

baked in.

For those reasons, calling the client to “verify” a case study is

entirely ineﬀective. Barring the rare case of outright fabrication, the

managers involved will gladly conﬁrm everything that was written—

exactly as their incentives predict. Worse, the “human touch” lends

undue credibility to the claims, simply because most corporate

managers make a favorable impression on whoever reaches out to

Two millennia ago, Caesar had employed this exact approach with his

Commentarii de Bello Gallico (Gallic War), going as far as claiming the Romans

suﬀered no deaths in very large battles; a feat considered as radically implausible

by historians.

60 CHAPTER 3. EPISTEMOLOGY

them

Even granting, implausibly, that the client is truthful, the case

study—as a method—is still terminally ﬂawed by selection bias.

Vendors serve many clients, and case studies are not picked at

random. The vendor naturally picks the most favorable cases. This

vendor-driven selection guarantees massive bias.

Indeed, a vendor may be incompetent—most initiatives quietly

folded as failures—yet still display successes. All it takes to achieve

the optics of success in enterprise software is to take over a mess

left by another incompetent vendor.

Incompetent vendors abound, and companies that pick them

demonstrate a lack of judgment. It is therefore unsurprising that

a company that once picked an incompetent vendor ends up pick-

ing another similarly incompetent vendor once the ﬁrst failure is

discreetly acknowledged. Yet, for this second round, a few lessons

will have been learned, and some severe mistakes will be avoided.

Thus the second vendor, despite being just as incompetent as the

ﬁrst, will most likely deliver something comparatively better than

the ﬁrst. Even the crippled can clear the bar if it is set suﬃciently

low. Out of this relative success, a case study glorifying the second

vendor’s competence will follow.

It is futile to lament the biases of authors in the ﬁeld of supply

chain, just as it is futile to hope that one day an “unbiased” author

will appear. There is no such thing as a neutral observer; putting

any supply chain theory to the test requires the cooperation of

a proﬁt-driven company. Remaining neutral is tantamount to

remaining incompetent. There is no solution to be found in the

ivory tower of pure research. Adversarial incentives, and the biases

they produce, should be considered an intrinsic feature of supply

chain.

This means the methods used to produce supply chain knowl-

edge should be assessed for their capacity to mitigate, rather than

Projecting a great deal of conﬁdence, competence, and sympathy is second

nature for those who rise through the ranks of large corporations. They are

essentially prerequisites for corporate careers.

3.7. NEGATIVE KNOWLEDGE 61

amplify, those biases. Ideally, those methods should be designed

and directed to this very purpose.

3.7 Negative knowledge

Having surveyed how supply chain “knowledge” goes wrong, we

need a practical countermeasure. The most reliable ﬁrst step is

negative knowledge: a curated register of what predictably fails,

kept close at hand and enforced as a veto against attractive but

ruinous schemes. The brief that follows explains why this register

matters and how to use it.

If positive knowledge steers the mind in the right directions,

negative knowledge steers it away from the wrong ones. Nega-

tive knowledge is largely absent from modern education systems.

Teaching is seen as an exercise in imparting “valid” knowledge

to students. Even within higher education circles, lectures where

professors venture into topics such as “widespread delusions” or

“what sounds good but doesn’t work” are few and far between.

As a result, negative knowledge is largely absent from the supply

chain literature, and supply chain is unremarkable in this regard.

Supply chain is, however, remarkably suited for negative knowl-

edge. Failed supply chain initiatives are plentiful, yet too often

brushed aside when they should be treated as a precious source of

negative knowledge. Any company that has run a sizable supply

chain since the 1980s and still operates on spreadsheets rather than

genuinely automated systems has almost certainly endured half

a dozen such ﬁascos. The irony is as old as the late 1970s, when

software vendors ﬁrst pledged complete automation, raising expec-

tations they have yet to fulﬁll. Instead of letting these mishaps

fade into memory, a company should examine each one in detail,

producing post-mortems that upper management safeguards like

crown jewels. By dissecting what went wrong—whether it was

an inﬂated sales pitch, a muddled chain of command, or a stub-

born legacy process—the organization learns to spot early signs of

trouble and avoid repeating the same pattern a few years later.

62 CHAPTER 3. EPISTEMOLOGY

A ﬁrm might, for example, embark on a grand plan to imple-

ment a fully automated inventory system across dozens of ware-

houses. The initiative can fail for any number of reasons: newly

introduced software contradicting decades-old procedures, conﬂict-

ing demands from diﬀerent departments, or a vendor’s technology

proving inadequate once the shiny demos give way to real-world

conditions. Without a proper post-mortem, the ﬁrm risks sleep-

walking into another, eerily similar venture three years later. A

carefully documented failure, however painful to revisit, strength-

ens the company by exposing the pitfalls that slick promises or

sloppy leadership create. Those who do not learn from their failed

initiatives are doomed to bankroll the same mistakes again—and

in supply chain, the costs only compound as the years go by.

When a supply chain ﬁasco comes to light, it deserves special

attention and greater credence precisely because no one has an

incentive to expose it. The parties involved almost always keep

their failures hidden, so when a calamity becomes public, it is

usually too big or too costly to be swept under the rug. This

negative knowledge should be safeguarded and studied, as details

of a failure do not spread easily and are nearly impossible to ﬁnd

in books or academic papers. A manager hoping to avoid the same

expensive lesson should seize these rare disclosures and examine

them carefully, for each one reveals not just a bad outcome but

the bad idea that led to it.

Negative knowledge endures because its foundations do not rest

on passing quirks of the market. When a method or a paradigm

is defective at its core, no change in circumstances will ever ﬁx

it. Supply chain abounds in such misﬁres. Stack ranking

, for

one, might seem straightforward; but in a ﬁeld that demands close

cooperation among teams, it corrodes the very trust those teams

need to function. Likewise, imposing a normal distribution to

Stack ranking, also called forced ranking or “rank-and-yank”, is a perfor-

mance review method where employees are placed on a bell curve—from top

performers at one end to low performers at the other—compelling managers to

“reward the best” and “punish the worst,” regardless of the absolute performance

level of each individual.

3.7. NEGATIVE KNOWLEDGE 63

model demand may look neat on paper, but supply chain data

rarely follow a tidy bell curve. The demand shocks and rare events

that shape real-world operations are exactly what this model

overlooks. These ﬂaws are not products of the moment, and it is

wholly unreasonable to imagine that, with enough tweaking, stack

ranking or the normal distribution could suddenly become suitable.

Their failings are built in, and supply chain leaders waste precious

time and resources whenever they reach for these tools in the hope

of a diﬀerent result.

Negative knowledge is often far easier to grasp than its positive

counterpart. One can spend days poring over how a columnar

database functions, but it is much simpler—and more practical—

to remember that such databases are unﬁt for systems of record.

Any ﬁrm that runs a highly transactional workﬂow through a

columnar architecture quickly discovers it runs painfully slowly

and burns through computing resources, hiking hosting expenses.

A company might endure this lesson only once before scrapping the

entire setup, never repeating the misstep. In that sense, negative

knowledge spares a manager from mastering every nuance of why

a technology fails; it is enough to know it will fail, and to steer

clear of that dead end altogether.

Conversely, negative knowledge is particularly suitable for sup-

ply chain because gathering trustworthy positive knowledge is so

diﬃcult.

Experiments in supply chain are deceptively diﬃcult to conduct.

No large company wants to gamble with the performance of a vast

network whose failures can ripple through inventory, transportation,

and customer satisfaction all at once. Even a seemingly modest

trial can disrupt entire segments of the operation yet oﬀer only

a ﬂeeting sense of whether it truly helped or merely shifted the

burden onto another part of the chain. A retailer that experiments

with smaller, more frequent deliveries might reduce local stockouts

for a handful of stores, only to ﬁnd that shipping costs surge

and cause complications for distribution centers already juggling

complex schedules. This is the recurring curse of supply chain:

problems often get nudged around rather than eliminated.

64 CHAPTER 3. EPISTEMOLOGY

Moreover, any experiment in supply chain, like an experiment

in economics, is notoriously hard to replicate. No two moments in

time oﬀer identical conditions, and market pressures, organizational

constraints, and the human factor can never be reproduced in

full. What worked last year under a certain demand pattern

might fail spectacularly when consumer tastes shift, or a new

competitor enters the market. Confounding factors always lurk

in the background, obscuring cause and eﬀect. When a company

declares an experiment “successful,” it is usually interpreting local

data through a particular lens—often unaware that broader realities

have already changed. Instead of guiding us toward conclusive

truths, such trials mostly remind us how fragile and slippery those

truths can be.

So-called “best practices” cannot be taken at face value. Profes-

sors, consultants, and software vendors each push their own agenda,

and none are aligned with what a company genuinely needs. Profes-

sors focus on theories that lend themselves to research papers and

classroom grading, neither of which has much bearing on whether

those theories deliver real-world results. Consultants, for their part,

clamor for originality so they can stand out among their peers,

yet novelty alone proves nothing. By spotlighting fringe issues

and framing them as urgent, they divert top management from far

more pressing concerns. Meanwhile, software vendors relentlessly

expand their feature sets to tick every box in an RFP, yet seldom

acknowledge how these added layers push maintainability to the

brink.

Even when a bright idea does ﬁnd practical footing, it can

lose its edge the moment too many adopt it. A so-called best

practice, once widely diﬀused across an industry, no longer confers

an advantage. Rivals adapt, eroding whatever gains the practice

once yielded. This eﬀect is not peculiar to supply chain, but it

looms especially large here because so many competing players—

vendors, consultants, even academic circles—are motivated to

brand a new technique as a universal solution. Real progress lies

elsewhere, for there is no single method so robust that it survives

unchanged under the glare of mass adoption. What counts as a

3.7. NEGATIVE KNOWLEDGE 65

best practice depends on rivals not doing it, and the moment they

catch up, its value vanishes.

The map of epistemic pitfalls is now complete: fashionable

mathematics, story-driven sociology, corrupted evidence pipelines,

and the perverse incentives that keep all three alive. Escaping this

maze requires a reference frame that does not shift when fashions

do, and a guardrail that keeps us from steering back into the same

ditches. Economics supplies the frame; negative knowledge supplies

the guardrail. Only by translating every supply

chain claim into

opportunity cost, proﬁt, and loss—while actively rejecting patterns

that predictably destroy value—can we compare alternatives on

neutral ground and expose wishful thinking for what it is. With

these standards in place, the next chapter leaves epistemology for

ﬁrst principles, revisiting the elementary laws of economics and

showing how they anchor every subsequent decision rule in this

book.

66 CHAPTER 3. EPISTEMOLOGY

Chapter 4

Economics

Supply chain is an applied branch of economics, itself a branch

of praxeology, the theory of human action. Supply chains emerge

from people acting in concert at scale to improve their material

conditions.

In Chapter 1, we deﬁned supply chain as the mastery of op-

tionality under variability. Optionality reframes the “alternative

uses” of general economics through a corporate lens: every deci-

sion allocates a scarce resource to one option while excluding the

rest. Economics is not a distant backdrop—it is the grammar of

supply-chain practice.

Economics unfortunately remains a science that is both largely

disregarded and ignored. Across legacy and social media, many

self-described “economists” speak not as scientists but as political

ideologues

Conﬂicts of interest worsen matters: many economists draw

their salaries, directly or indirectly, from the very states whose

policies they comment on. Whether consciously or not, such

funding narrows the range of views that can be voiced without

As already observed by Thomas Sowell in Knowledge and Decisions (1980)

and Intellectuals and Society (2010), as well as by Ludwig von Mises in Human

Action (1949), political commentary from economists frequently stems from

ideology rather than genuine economic analysis. The author is hardly the ﬁrst

to point out this phenomenon.

68 CHAPTER 4. ECONOMICS

professional risk

. When ideology masquerades as neutral analysis,

the public’s distrust only deepens.

4.1 Essential economic principles

For readers unfamiliar with economics, we recommend Basic Eco-

nomics (2000) by Thomas Sowell. While not a prerequisite, its

insights are foundational to the study and practice of supply chain.

Like Sowell’s other work, it is exceptionally clear and accessible

to a non-technical audience. Above all, this book clariﬁes that

economics is strictly neutral with regard to politics and other ide-

ologies. The general laws of economics cannot be evaded: they

apply equally no matter how society organizes itself; whether it be

capitalism, socialism or feudalism. Laws such as supply and de-

mand, diminishing returns, and Ricardian comparative advantage

are fundamental to supply chain.

These fundamental laws govern every exchange that a supply

chain orchestrates.

Law of supply and demand. In a free market, the price of

a good rises when demand outstrips supply and falls when supply

exceeds demand. The price signal is not an arbitrary convention; it

is the device that rations scarcity. When inbound ocean containers

were in short supply during the 2021 post-lockdown rebound,

freight rates quadrupled. Shippers that valued the containers the

most accepted the premium and shipped on time; lower-value loads

waited until capacity returned. The mechanism can seem brutal,

yet it silently assigns a scarce resource to its most valuable use

while encouraging carriers to invest in extra capacity.

Law of diminishing returns. If one factor of production is

increased while all others are held constant, the additional output

One of the most basic aspects of managing a conﬂict of interest is that

the person subject to the conﬂicting position doesn’t get to decide whether his

views are biased or not. The conﬂict of interest is an a priori cause to assume

severe bias. Moreover, considering the considerable role that 21

-century states

play in the economy at large, proposing that an economist funded by the state

apparatus isn’t massively conﬂicted is quite an extravagant proposition.

4.2. MARKETS AND SUPPLY CHAINS 69

generated by each extra unit eventually declines. Double the

number of pickers in a fulﬁllment center and parcel throughput

nearly doubles—until congestion at the packing stations begins to

bind. The same curve appears when adding buﬀer stock: the ﬁrst

pallet slashes the risk of a stockout, the tenth pallet oﬀers only

a sliver of extra protection. Locating the knee of this curve is a

central engineering problem of supply chain.

Ricardian law of comparative advantage. Even when

one party is absolutely more eﬃcient at producing every good,

both parties gain from trade when each specializes in what it does

relatively best. A Swiss micromachining workshop that makes

high-precision gears and an Indonesian plant that weaves cotton

shirts both prosper by exchanging output, despite wage and skill

gaps. Supply chains furnish the infrastructure—shipping lanes,

warehouses, information systems, and contracts—that turns com-

parative advantage into tangible surplus.

These three laws are illustrative, not exhaustive. Plenty of fur-

ther concepts—opportunity cost, time preference, entrepreneurial

proﬁt and loss—enter the picture, yet this trio is enough to show

why every stocking, routing, or pricing choice ultimately reduces

to the same question: how best to direct scarce resources toward

their highest-valued use under uncertainty.

4.2 Markets and supply chains

The remarkable improvement in humanity’s material conditions

over the last three centuries can largely be attributed to the free-

market economy, which has ﬁnanced the development of nearly

all the sciences and technologies we now enjoy. In developed

countries—now covering an ever larger share of humanity—real

prices of food and manufactured goods are a fraction of their levels

a century ago.

The mastery of supply chain is one of the many innovations

driven by free markets to deliver more with fewer resources. While

supply chains rely on complementary technologies, their presence

70 CHAPTER 4. ECONOMICS

typically signals mass accessibility; without them, goods can still

be produced, but at costs aﬀordable only to a privileged few.

The superior material conditions in developed countries largely

result from eﬃcient supply chain operations. While advances in sci-

ence and technology allow us to achieve extraordinary feats—such

as sustaining life in orbit—there is a vast diﬀerence between isolated

technological stunts and the packaged, reliable miracles accessible

to the masses. This gap is largely due to robust supply chains,

which serve as the backbone of our industrial civilization.

A revealing counterexample comes from the Soviet Union. Un-

der central planning, almost every large manufacturer was forced

into extreme vertical integration because reliable partners simply

did not exist. A refrigerator plant in Leningrad ran its own timber

yard for crates, its own glass foundry for shelves, and even a dairy

farm to feed the company canteen. What looked on paper like envi-

able self-suﬃciency was, in fact, the symptom of an economy where

trust, specialization, and price signals had been extinguished.

Lacking markets that rewarded punctuality and quality, “part-

ners” were merely other ministries obliged to deliver whatever they

could, whenever they could. Factories therefore hedged by dupli-

cating upstream capacity in-house and by hoarding months—or

sometimes years—of input stock. Finished goods fared no bet-

ter: they often waited weeks for railcars, themselves hostage to

the chronic wagon shortages of Gosplan’s schedules. Lead times

that would have bankrupted a Western ﬁrm were accepted as

routine, and the waste showed in every metric. Ton-kilometers of

freight per unit of output were several times higher than in market

economies, while speciﬁc energy consumption for steel, textiles, or

bread dwarfed Western benchmarks.

This episode matters for present-day managers: supply-chain

performance is not chieﬂy a matter of clever heuristics or larger

warehouses. It is ﬁrst and foremost a matter of incentives coupled

with adequate economic calculation. When prices move freely and

contracts are enforced, specialization ﬂourishes and long, delicate

networks become not only feasible but vastly more eﬃcient than

monolithic giants. Remove those incentives or that calculation, and

4.2. MARKETS AND SUPPLY CHAINS 71

the networks collapse inward until every plant becomes a fortress

hoarding its own raw materials. Complexity does not disappear; it

merely resurfaces as waste.

Before the 1950s, air travel was an extravagant luxury reserved

for the military and the wealthy. A series of innovations then

made ﬂying safe and aﬀordable, rendering it commonplace. A

key innovation was podded engines—aircraft engines mounted

under the wings in pods—a concept pioneered by the Sud Aviation

Caravelle in 1955. These pods decoupled engine maintenance from

that of the main fuselage, substantially lowering operating costs

and paving the way for aircraft like the Boeing 707 in 1957 to

democratize air travel.

Currently, the space industry has yet to beneﬁt from modern

supply chain practices, mainly due to technological limitations.

A handful of wealthy entrepreneurs have bought ﬂights to orbit,

though the prices are still prohibitive for most. After decades

of stagnation, SpaceX is advancing technologies—most notably

reusable rockets—that could eventually support a space supply

chain. Once these technologies are mastered, established supply

chain principles will lower costs substantially.

A historical example of this dynamic is the rapid adoption

of containerization in maritime shipping during the 1950s and

1960s. The standardized intermodal container oﬀered a break-

through that drastically reduced handling costs and transit times.

Once this technology was established—requiring suitably equipped

ports, cranes, and ships—a new generation of supply chains formed

around it. Ports that failed to upgrade and integrate container

standards lagged behind, while those that embraced containeriza-

tion became major global trade hubs. Likewise, whenever a new

technology matures—whether it be reusable rockets or advanced

robotics—innovators quickly organize their supply chains to exploit

these advances, leveraging both modularity and standardization

to achieve unprecedented scale and cost eﬃciency.

More generally, technological advances expand the range of

available options, and supply chain decides how best to leverage

them for maximum economic eﬃciency.

72 CHAPTER 4. ECONOMICS

4.3 The value of supply chains

Supply chains emerged from the market economy because they are

eﬃcient. Dating the ﬁrst true supply chain is vain: the phenomenon

emerged gradually and can be traced back to antiquity. Moreover,

supply chains emerged without visionary planners deliberately

steering companies toward what we now recognize as supply chains.

Companies simply followed empirical eﬃciency, and it led them to

the supply chains that exist today. While modern companies can

now be engineered with the explicit intent of bringing a superior

supply chain to market, this perspective is fairly recent—and

largely posterior to the supply chains themselves.

Supply chains exist because they unlock a handful of structural

mechanisms that confer durable competitive advantages. The

pages ahead examine these mechanisms and show how supply-

chain design can generate more value than sheer size ever could.

These mechanisms diﬀer somewhat from the traditional concept

of economies of scale, generally understood as achieving greater

eﬃciency with increasing size. That eﬃciency typically arises from

the mix of ﬁxed and variable costs in production. Functions such as

engineering, marketing, strategic management, and compliance are

largely ﬁxed regardless of output, whereas acquiring, transporting,

and processing goods scale with volume.

Economies of scale represent a basic form of ﬂow optimization:

increasing volume reduces marginal cost. In everyday operations,

however, supply chain professionals rarely have levers to raise

volume enough for additional economies of scale; step-changes in

ﬂow belong to M&A (mergers and acquisitions), not supply chain.

Beyond economies of scale, additional value stems from how a

company shapes its ﬂow through four recurring forces: standardiza-

tion (taming innate variability), pooling (consolidating inventory

to buﬀer randomness), batching (grouping movements to cut unit-

handling costs), and versatility (designing assets to serve several

purposes). These forces pull on diﬀerent levers—information, space,

time, and capital—yet converge on the same goal: delivering more

service from the same scarce resources. The next sections examine

4.3. THE VALUE OF SUPPLY CHAINS 73

each force in turn, starting with standardization.

4.3.1 Standardization

Variability is the natural state of physical things. No two trees are

exactly alike; the same holds for stones, cows, people, and parcels

of land. Boundaries often blur: do fallen fruits still count as part

of the tree? Does the ownership of a piece of land extend from the

depths of the earth to the stars above? Our actions, too, exhibit

inherent variability. Even arranging an apple admits countless

variants; no two placements are exactly the same. The apple may

fall on the ﬂoor, be picked up again, and put back on the shelf.

Does this still count as the same action? Even if the ﬂoor is dirty?

Yet in industrial civilization we take for granted that, within the

ﬂow of physical goods, almost every “thing” and “action” can be

neatly identiﬁed and counted. This is no accident; it is the result

of centuries spent making the ﬂow manageable—a process called

standardization.

Standardization is a loose collection of practices that make

the ﬂow of goods manageable within ﬁrms and across markets.

Indeed, inherent variability in all things complicates everything.

Every item purchased or sold varies in its characteristics and,

consequently, often in price. Every manufacturing process must be

continuously adjusted to accommodate the minute diﬀerences in

its ever-changing inputs. These complications consume managerial

attention—a costly input—and blunt the company’s capacity to

achieve economies of scale. They also impose cognitive costs on

customers, who must judge whether an item suits its intended use.

To a present-day reader, this may seem abstract. The vast

majority of goods are standardized—mass-produced within nar-

row physical tolerances. Variations between units are expected

to be imperceptible; two units of the same product should be

visually indistinguishable. When such a feat is possible, we, the

customers, instinctively attribute the diﬀerence to a defect of some

74 CHAPTER 4. ECONOMICS

kind

. Even store-bought fruits and vegetables are now carefully

calibrated, reducing variability to a fraction of their natural diﬀer-

ences at harvest. In industry, quality chieﬂy means the absence of

unintended variation.

We also expect high modularity and interoperability among

assets. Containers ﬂow seamlessly from ships to trucks, thanks

to dedicated container cranes. A limited set of tire sizes covers

nearly all trucks. Standard pallet patterns optimize container

space, and forklifts—built for pallets—move them eﬃciently. At

every stage, barcodes are printed and later scanned—often by

another company. Ultimately, modularity and interoperability

stem from careful standardization, which simpliﬁes entire classes

of operations.

Yet, just a century ago, the situation was radically diﬀerent.

Most mass-produced items still exhibited perceptible variation.

Interoperability was local, not global, and modularity largely con-

ﬁned to individual companies. After World War II, standards

expanded in scope and depth alongside modern supply chains. The

movement had forceful advocates, notably W. Edwards Deming,

whose quality methods made standardization a managerial priority.

Standardization is a critical requirement for massifying the

ﬂow: without it, many processes are infeasible, brands are exposed

to defects, managers chase outliers, and most economies of scale

remain inaccessible.

As a corollary, the ﬂow has become predominantly discrete:

itemized units—counted one by one—now travel through the net-

work instead of anonymous tons or liters. Bulk modes persist for

sand, crude, and grain, but for anything with a hint of value, the

default is a barcoded, scan-ready unit.

Some products are mass produced in a way that ensures that every unit

is visually unique, typically through a pseudo-random coloring or patterning

technique. However, those variations are intentional and carefully controlled.

These products are not an exception to the standardization principle. On

the contrary, they reﬂect an even tighter mastery of production processes, as

they rely on an intentional variability without compromising any other product

qualities.

4.3. THE VALUE OF SUPPLY CHAINS 75

This discreteness supercharges optionality. A railcar of ethanol

presents one choice—ship or hold—whereas a pallet of 120 power

drills presents 120 separate choices: every drill can be split, rerouted,

repriced, kitted, or postponed. Each SKU thus functions as an

option ticket whose value depends on where, when, and to whom

the option is ultimately exercised.

The upside is obvious: ﬁne granularity lets a company redi-

rect stock to emerging pockets of demand, arbitrage tiny price

spreads, and delay commitment until the latest possible moment.

The downside is equally clear: the decision space explodes. A

planner who once scheduled two containers a month must now

orchestrate thousands of micro-moves a day. When cognitive or

computational bandwidth is lacking, management falls back on

coarse rules—MOQs, ﬁxed cycles, blanket allocations—thereby

erasing much of the ﬂexibility it paid for. Discreteness is thus a

double-edged sword: it multiplies opportunities while magnifying

the need for automation capable of scanning and ranking them.

We will return to this tension when discussing decision-making

processes.

Handling individual units is not cost-free—the carton, the

barcode, the extra touches all add pennies—but for anything

above the cheapest commodities, those pennies are dwarfed by the

upside of precise allocation. In the pages that follow, every unit

will be treated as a negotiable option, and the question will always

be which exercise maximizes long-run proﬁt. With this discrete

foundation in place, we can now turn to the second structural force:

pooling.

4.3.2 Pooling

Pooling concentrates inventory in one place—or under one corpo-

rate entity—to serve multiple demand streams. If the overhead

from concentration is negligible, pooling delivers the same service

with less capital than multiple independent inventories. In practice,

pooling is proﬁtable when the inventory cost savings exceed the

additional service overhead. For example, concentrating inventory

76 CHAPTER 4. ECONOMICS

in one place typically raises transportation costs relative to serving

goods from multiple locations.

Pooling mitigates supply-chain variability by the law of large

numbers: a central pool can ﬂexibly absorb uneven surges across

segments, reducing the total stock needed to maintain service levels.

Imagine one table holding champagne for the entire reception rather

than many smaller tables. Pooling resources reduces the risk of

any one table running dry, ensuring that everyone is served. Of

course, pooling can hinder last-mile service if it places goods too

far from consumption.

Consider a company weighing a single large distribution center

against multiple smaller warehouses. A single center pools demand

eﬃciently and reduces total stock, but it also adds overhead—longer

average hauls and more handling at the hub. Managers must weigh

these logistics costs against working-capital savings. If regional

product demands are closely correlated—due to seasonality or

nationwide campaigns—a single warehouse delivers less beneﬁt.

Correlated demand and customer ﬂexibility to switch stores or

channels erode theoretical pooling gains. Side-by-side simulation

or a pilot usually tests whether inventory savings outweigh added

expenses and delays, ensuring a positive net value.

In theory, pooling can dramatically reduce working capital; in

practice, frictions pare back the gains and add overheads. Even so,

pooling remains attractive in many contexts.

Putting transportation overheads aside, the gains are lower for

two reasons. First, the streams of demand are never independent;

on the contrary, those streams should be expected to be quite cor-

related. Markets are connected; while oﬀsetting surges and drops

can occur, the typical pattern is co-movement, not cancellation.

Correlation has many sources, often the company’s own actions.

For example, a nationwide advertisement raises demand across

many locations simultaneously. If streams are perfectly correlated,

pooling requires as much working capital as decentralization for a

given service level.

Second, when demand surges, a customer facing a local stockout

may seek service from another location, incurring the associated

4.3. THE VALUE OF SUPPLY CHAINS 77

overheads. This is common in dense retail networks where multiple

stores compete within the same city. Moreover, letting customers

check availability online ampliﬁes the eﬀect, nudging them—up

to a point—to cooperate with the company’s stocking policies. If

customers fully accept the overhead of alternative service loca-

tions, pooling brings no beneﬁt: the same inventory reduction was

achievable without consolidation.

If the future were perfectly anticipated, pooling would confer

no beneﬁt; it would only add overhead from suboptimal place-

ment. Pooling is widespread precisely because future uncertainty

is irreducible.

In terms of optionality, pooling reduces the number of options

but neither their frequency nor their diversity. Thus, pooling

somewhat simpliﬁes the supply chain challenge. However, ﬁrms

rarely adopt pooling for the simpliﬁcation alone. Empirically,

concerns about dead stock and shelf space dominate the selection

of locations suitable for storing a given product.

4.3.3 Batching

If pooling concentrates inventory in space, batching concentrates

the ﬂow in time. Batching processes items in larger lots rather

than singly, lowering per-unit processing cost but reducing the

ability to serve small quantities.

Batching is ubiquitous in logistics: once the ﬂow exceeds a

threshold, units are repackaged into larger lots. A typical progres-

sion is: group

units into a box; bundle

boxes into a pallet

layer; combine

layers into a full pallet; ﬁnally pack

pallets

into a container. Bundling straightforwardly reduces handling

costs, both within a facility (intralogistics) and between facilities.

Bundles simplify handling and counting, and their packaging often

adds physical protection.

Batching is also common in manufacturing: it typically means

running a large lot of a single product before switching or pausing.

This approach minimizes setup time and other non-productive work

(e.g., recalibration) and also facilitates post-production logistics.

78 CHAPTER 4. ECONOMICS

In process industries (e.g., chemicals), scale eﬀects such as higher

yields push toward enormous plants and correspondingly large

batches.

Batching increases the inherent variability of the ﬂow and

ampliﬁes the eﬀects of external market ﬂuctuations. Internally,

variability rises because batching ties the fate of many units to-

gether. If 100 units are shipped individually by various couriers,

it would be surprising for all to be lost. However, if all 100 units

travel on one truck, the (small) traﬃc accident risk now applies to

all at once. Formally, batching raises variance in the ﬂow.

Batching ampliﬁes external variability. The most frequent

mechanism is a longer ﬂow cycle. Batching reduces the frequency

of acquisition, transport, and production. Colloquially, this ex-

tended cycle is called “longer lead time”. In batching, lead time is

the period the company must wait before launching the next batch.

Larger or less frequent batches tie up more working capital in inven-

tory because items must be purchased or produced well in advance.

They also diminish ﬂexibility: misjudged demand turns oversized

batches into overstocks or prolonged stockouts, both costly to cor-

rect. A longer cycle forces the company to operate longer before it

can decide again. For example, with batching, misjudged demand

enlarges overstocks or lengthens stockouts, depending on whether

demand was overestimated or underestimated.

Batching lowers optionality by reducing both the frequency

and the number of decisions within a period (e.g., a month). In

the absence of adequate automation, batching is often employed

primarily to simplify the supply chain. But this trade-oﬀ leaves

the company with fewer options and less ﬂexibility to adapt to

market variation. In practice, companies too often opt for large

batches because they lack the capacity or managerial resources to

handle more frequent, detailed decisions. This reﬂects a human or

organizational limitation, not a physical necessity.

With proper automation, the overhead of frequent decisions

about physical goods becomes minimal, often negligible. As au-

tomation lowers the cost of orchestration, the risk–reward of smaller

batches improves, reducing both inventory costs and lead times.

4.3. THE VALUE OF SUPPLY CHAINS 79

Indeed, while modern computers operate on a nanosecond scale,

physical goods are limited to timescales of tenths of a second (0.1

s) – a disparity that accommodates the slower pace of physical

processes without causing damage. Consequently, to a modern

computer, the ﬂow of physical goods appears almost frozen

4.3.4 Versatility

Versatility denotes capital goods—durable assets such as machines,

vehicles, or facilities that enable the ﬂow of physical goods—that

can serve multiple distinct functions or even an entire spectrum

of operations. Yet at any moment a capital good can serve only

one purpose; thus the company must assign each capital good a

speciﬁc purpose at that time.

For example, a warehouse designed to maintain a controlled

room temperature (e.g., 15°C–25°C) might also be equipped with

devices capable of supporting a cool chain (e.g., 12°C–14°C). Even

if the cold-chain capability remains unused at ﬁrst, it can be a

sound investment to accommodate future market ﬂuctuations.

Versatility is deliberate: it enhances the company’s agility and

expands its range of options. It always entails overhead associated

with its wider capability set. All else equal, a versatile capital

good costs more than a specialized one because of its broader

capabilities. In practice, these overheads are usually oﬀset by

modularization—and its frequent companion, standardization.

Modularization breaks a process into smaller, interchangeable

units that can be easily reconﬁgured for diﬀerent tasks. Once

standardized, companies aggressively compete to produce these

units. This typically yields signiﬁcant economies of scale, leaving

Enterprise software products are frequently sluggish and unresponsive. At

the time of this writing, it remains frequent for an operation triggered by a user

to take many seconds to complete. However, considering that even entry-level

computing systems now feature gigabytes of memory, gigaﬂops of processing,

and gigabits per second of bandwidth, such events must almost always be

attributed to the incompetence of certain software vendors, not to any inherent

limitations of the computing hardware with respect to the complexity of the

operation requested by the user.

80 CHAPTER 4. ECONOMICS

companies little choice but to adopt modular capital goods to stay

competitive. A downside is increased overall ﬂow complexity as

operations are subdivided merely to ﬁt widely available modules.

Modern logistics exempliﬁes modularization in its barcodes,

loading docks, and shipping containers. Without the intense stan-

dardization of the 20

century, the beneﬁts of modularization

would be far smaller. A company can print a barcode and expect

another to read it because they share a standard. A truck can be

loaded at one site and unloaded at another because dock heights

match the truck’s design.

Modern manufacturing has likewise transformed. Companies

now leverage standardized units for most processes, resorting to

custom-made capital goods only for steps that oﬀer a unique com-

petitive edge. Often, the entire manufacturing setup consists of

standardized components, so a company’s distinctiveness stems

from its unique selection and arrangement of these units. More-

over, the steady progress of programmable controllers—in various

forms—has rendered these components not only modular but also

highly versatile.

Every versatile capital good requires its own allocation schedule.

At any given moment, the company must decide whether to reassign

an asset to a diﬀerent purpose. For some assets, this decision is

straightforward and embedded in practice—for example, truck

ﬂeets typically operate on ﬁxed routes. Conversely, some assets’

versatility remains untapped; a facility might produce a narrow

range for years even as market conditions ﬂuctuate.

The versatility of capital goods vastly inﬂates the number of

options available to the company. Not only can alternative goods be

served with the same apparatus, but the same service (transport or

transformation) can be performed many ways, as versatile capital

goods often overlap in capability. For instance, box trucks and

semi-trailers are nearly interchangeable in transporting goods; only

the heaviest loads require a semi-trailer.

Yet each asset carries distinct acquisition and operating costs.

Furthermore, every asset, whether labeled perishable or not, will

in time perish through wear and tear. Consequently, much is at

4.3. THE VALUE OF SUPPLY CHAINS 81

stake in deploying the right asset at the right time for each task

to achieve the outcome while minimizing costs.

4.3.5 From forces to policies

Viewed through the economist’s lens, these forces—standardization,

pooling, batching, and versatility—are four ways to reshape the

frontier where scarce resources meet alternative uses.

Each force corresponds to an elementary economic trade-oﬀ.

Standardization lowers marginal cost at the price of variety; pooling

frees working capital by accepting longer ﬁrst-mile hauls; batching

swaps inventory risk for lower handling cost; and versatility converts

capex into real options whose value materializes only if the decision

system can pick the right moment to exercise them. Together they

expand the option space far faster than managerial attention can

track.

Faced with a combinatorial explosion of options, companies

often adopt policies expressed as a simple allocation rule. For in-

stance, a company with two warehouses and a ﬂeet of trucks might

decide that one truck should make a daily round trip between the

facilities to rebalance inventory. This policy is adopted with the

implicit intent to use the rigid schedule for inventory rebalanc-

ing. The goods to be transported are unspeciﬁed, but the tacit

understanding is that managers will use the daily allotted capacity

to keep inventory balanced. However, this "one truck" policy is

simplistic; some days, two trucks would be preferable, while on

others, minimal inventory imbalances would justify postponing the

round trip. Such rule-based policies may be implemented through

software to notify the right employees at the right time, but a clerk

could have otherwise supervised them with little eﬀort.

However, governing the supply chain through simple rule-based

policies is largely vestigial. It reﬂects constraints that ceased to

be relevant with the advent of digital supply chains. Sticking

to simple rule-based policies was essential when a clerk was in

charge of executing the policy. However, if the policy is delegated

to a computer, then it can be made much more dynamic while

82 CHAPTER 4. ECONOMICS

keeping the processing overhead negligible. If this added complexity

improves the supply chain, a far superior allocation of resources

follows. Revisiting the example, a more advanced policy should

not only consider the amount of inventory to rebalance, but also

compare the beneﬁts of rebalancing with the alternative uses of the

trucks, such as serving customer orders. If trucks are the bottleneck

and the inventory imbalances between the two warehouses are

tolerable, all trucks should be allotted to customer deliveries.

Unlike pooling and batching, which reduce the number of

options, the versatility of capital goods inﬂates them. There is no

a priori reason to think that all those options are economically

equivalent. On the contrary, many options are poor, and one is

best under a given economic model of the supply chain. Software

supported by modern computing hardware makes it relatively

straightforward to search through an extremely large number of

options. The ﬁne print of this process is unimportant here; what

matters is that versatility is profoundly consequential for supply

chains.

Because every policy is ultimately an allocation rule, its merit

is economic, not procedural: it must raise the risk-adjusted rate of

return of the resources it steers. Static heuristics were defensible

when humans were the computational bottleneck, but once a

computer owns the rule, the benchmark becomes opportunity cost,

not clerical convenience. Automation therefore matters only insofar

as it lets the price signals—expressed inside the model as valuations

and discount rates—propagate to every micro-decision. In the end,

a policy is justiﬁed exactly to the extent that it generates proﬁt.

In practice, the combinatorial explosion caused by versatile

capital goods can appear daunting and often easily outstrips human

oversight. Yet modern supply-chain software does not simply

attempt to brute-force every possibility. As will be discussed in

Chapter Decisions, advanced analytics—supported by solvers and

heuristic algorithms—can render this complexity tractable.

4.4. THE GOAL OF SUPPLY CHAIN 83

4.4 The goal of supply chain

Supply chain was deﬁned in the ﬁrst chapter as the mastery of

optionality in the presence of variability when managing the ﬂow

of physical goods. Mastery here is not an abstract virtue: it is the

concrete ability to turn optionality into money while shielding the

company from the downside of uncertainty. In a market economy,

the yardstick for such mastery is unambiguous—long-run proﬁt

expressed in hard currency. The practical goal of supply chain

practice is therefore to raise the ﬁrm’s risk-adjusted rate of return

on every scarce resource it touches—capital, capacity, time, good-

will—by continually improving both the range and the quality of

the options that can be exercised.

This goal subsumes every secondary intention a practitioner

might entertain. Higher service levels, shorter lead times, leaner

inventories, greener transportation, happier suppliers, safer working

conditions—all matter, but only insofar as they contribute, directly

or through risk mitigation, to the net present value of the business.

Supply chain is not moral philosophy; it is an applied branch of

economics that survives or perishes by the ledger. Pursuing proﬁt

must not be confused with myopic cost cutting or the caricature of

a “naked” proﬁt motive; it is the disciplined quest for the allocation

that maximizes long-run surplus while respecting both variability

and optionality.

The remainder of this section unpacks that proposition. First,

we revisit proﬁt and loss as the indispensable feedback loop that

tells a company whether its supply-chain decisions create or destroy

value. Next, we explain why objective valuations are mandatory

inside the ﬁrm even though all valuations are subjective at the indi-

vidual level, and how the notion of rate of return makes disparate

decisions comparable across time. Finally, we address frequent

objections—moralistic, apocalyptic, or institutional—to proﬁt seek-

ing and show why none of them exempts a supply chain from the

laws of economics.

84 CHAPTER 4. ECONOMICS

4.4.1 Proﬁt and loss

Proﬁt and loss is not an accounting gadget; it is the scoreboard

of a society built on consent rather than coercion. Whenever

two parties trade, each expects to be better oﬀ—otherwise no

deal occurs. Proﬁts, therefore, arise only within the domain of

voluntary exchange. Conversely, whenever allocation is imposed by

force—or by regulation that mimics it—the very possibility of proﬁt

vanishes—replaced by tribute, tax, or plunder

. In economic terms,

human relations lie on a continuum from hegemonic command to

unfettered trade. Proﬁts arise only on the latter side.

Yet a vocal strand of public opinion still portrays proﬁt as

intrinsically exploitative

. This view conﬂates proﬁt with plunder:

the latter is taken by force, whereas the former can arise only

when both parties consent and deem themselves better oﬀ after

the exchange. Seen this way, proﬁt has a moral edge: it is earned

solely by serving others. Losses play the mirror role. They warn

entrepreneurs that customers prefer other uses for their scarce

resources. Far from being damaging, losses act as the market’s

immune system; they halt further waste and redeploy labor, capital,

and ingenuity to more urgent purposes.

History makes the contrast vivid: before the commercial rev-

olution of the 17

century, fortunes were typically amassed by

conquering a province, taxing the peasantry, or courting royal

favors. Since the rise of open markets, the road to wealth has

inverted: Rockefeller reﬁned kerosene more cheaply than anyone,

Akio Morita miniaturized electronics for the masses, Ingvar Kam-

prad taught ﬂat-pack furniture to a frugal planet. Where proﬁts

are contestable, eﬃciency soars; where allocation is decided by

Government interventions in the economy lead to crony capitalism, where

corporate success depends on collecting handouts rather than satisfying cus-

tomers. The term “proﬁt” ought therefore to be reserved for gains achieved

in an unhampered market. Wealth acquired through coercion—even when the

coercer is a government—diﬀers categorically from the surplus earned through

voluntary exchange, although conventional P&L statements blur the distinction.

In France, for decades, the slogan of left-leaning parties has been “Nos vies

valent plus que leurs proﬁts” (our lives are worth more than their proﬁts).

4.4. THE GOAL OF SUPPLY CHAIN 85

decree, queues, shortages, and rust follow.

Supply chains translate this principle into pallets, containers,

and transport lanes. Standardization shrinks unit cost, pooling

frees working capital, batching arbitrates handling expenses against

responsiveness, and versatility turns ﬁxed assets into spring-loaded

options. Yet none of these levers tells you when to stop. The only

impartial referee is the proﬁt and loss statement: does the extra

SKU, the larger batch, or the reinforced crate pay for itself once

variability and opportunity costs are tallied? If the answer is yes,

the ﬂow expands; if the answer is no, the ﬂow contracts and capital

migrates to better uses.

Practitioners, therefore, miss the point when they chase “service

level”, “inventory turn”, or any other local metric in isolation. Such

indicators matter only insofar as they raise the ﬁrm’s long-run rate

of return. The objective never changes: maximize the gap between

what customers willingly pay and what supply chain irrevocably

consumes, subject to the risks imposed by uncertainty.

The sections that follow dismantle the stock objections to the

proﬁt motive and show how a clear P&L perspective inoculates

supply-chain design against both spreadsheet sophistry and pious

slogans.

4.4.2 Objective valuations

Economics begins with a simple observation: only individuals can

feel, think, and act; therefore, only individuals can ascribe value

to anything. These valuations are subjective and ordinal—one

can say he prefers coﬀee to tea, but there is no arithmetic to add,

subtract, or average the two preferences. Worse for the analyst,

the ranking can ﬂip tomorrow without warning. No spreadsheet

can pin down an “inherent” price for the ﬁrst sip of water, the

hundredth, or the thousandth; the paradox of value remains a

feature, not a bug, of human action.

Statistical models can, of course, learn to predict how much a

person of average means will pay for an espresso or how likely he is

to abandon a shopping cart, yet the forecast does not turn a private

86 CHAPTER 4. ECONOMICS

preference into an objective quantity. It merely characterizes the

subjectivity as a probability distribution, useful in commerce but

silent on worth.

This point torpedoes the labor theory of value, which tried to

anchor prices to the number of work hours “embodied” in a good.

Diamonds are dear not because miners toil, but because buyers

prize the sparkle. If tomorrow everyone decided that quartz is

prettier, diamond mines would shutter despite unchanged labor

inputs.

In 2019, when a massive ﬁre broke out in Notre-Dame, the

public was swiftly evacuated. While no human lives were at stake

anymore, some ﬁreﬁghters put their lives at great risk to save what

they considered timeless works of art. In economic terms, they

exercised a value judgment, placing mere artifacts above their own

lives. Most men, in the same situation, would not have acted

likewise and would have stopped risking their lives once it was

clear that no further lives were endangered. It is pointless to

assess how much those artifacts are “worth” in hours of labor, and

how much it would cost to produce near-perfect replicas. From

the perspective of those men, the historical and artistic value of

those artifacts was categorically superior to any later attempts

at replacing them. Through their actions, free individuals can

exercise entirely arbitrary value judgments. The role of economics

is not to assess whether such value judgments are good or bad; it

merely recognizes their irreducible subjectivity.

Companies, however, cannot indulge such idiosyncrasies. Inside

a ﬁrm, the CEO may love truﬄes and the CFO may hate them,

yet the only admissible question is: “Do customers pay enough,

soon enough, to cover our costs?” Individual preferences are not

additive; they cancel out. What remains is the impersonal verdict

of the market, recorded as money in, money out.

If an employee decides that his company must value certain

options disregarding the proﬁt motive—“we should stock this

eco-friendly widget even if it never sells”—this employee is not

being good or caring; he is just deluding himself by projecting

his own value judgment onto the company. By doing this, he

4.4. THE GOAL OF SUPPLY CHAIN 87

is doing a disfavor to the company, and to the market, because

he is acting against the wants of the consumers, favoring instead

his own pet perspective of the day. Naturally, if the employee

disagrees with the general mission of the company, then it is his

duty, as a free individual, to resign and seek employment elsewhere.

Disagreements with the company’s stated purpose are not to be

settled by sabotaging operations from the inside.

A ﬁrm is therefore an instrument of social cooperation: it

aggregates capital, talent, and information to satisfy the most

urgent wants that other people express through their purchases.

Proﬁts and losses turn those dispersed signals into an objective

yardstick that every planner can read: more coins earned than spent

means resources ﬂow toward higher-ranked wants; the opposite

means they are wasted. Supply-chain practice must anchor every

valuation to that yardstick—never to personal taste—because only

prices let millions of subjective scales collapse into one objective

measure.

The next question, then, is how to compare two proﬁtable

options that diﬀer in timing—as with rates of return.

4.4.3 Rate of return

Whenever a decision separates committing a resource from its

payoﬀ, plain proﬁt arithmetic no longer suﬃces. Economists call

this universal phenomenon time preference: identical goods are

worth more the sooner they are available. A cup of coﬀee served

now is worth more than the same cup promised next week; a

supplier usually demands a premium for payment deferred by

ninety days.

Time preference is ﬁrst an individual trait. Each person pri-

vately discounts future beneﬁts according to circumstance, and

the market aggregates those discounts into the interest rate. The

interest rate therefore quantiﬁes, at the scale of society, how much

present goods are preferred to future goods.

Corporations inherit the same logic. Cash received today can

be reinvested, used to settle liabilities, or serve as a buﬀer against

88 CHAPTER 4. ECONOMICS

uncertainty; cash received next year cannot. Firms therefore embed

an explicit cost of capital—their collective time preference—into

every investment test. However appealing in isolation, any proposal

that fails to clear that hurdle erodes value once the opportunity

cost of waiting is recognized.

Once time preference is acknowledged, optimizing proﬁt re-

quires attention to time; speciﬁcally, the company should pick the

options with the highest rate of return. Conceptually—setting

aside secondary complications—the rate is computed as

RoR =

revenues − spending

spending × period

where period is the time required to realize the associated revenues

and spending. The rate of return (RoR) is expressed as a percentage

per unit of time. If the RoR is greater than zero then the company

operates proﬁtably; if the RoR is less than zero then the company

operates at a loss. When comparing two options, prefer the higher

rate—provided both are normalized to the same period.

For example, let’s consider a retail store that can choose be-

tween two products to be put on display on a given shelf. Both

products are sold at 3 coins with an average demand of 1 unit

per day. The ﬁrst product costs 1 coin per unit, while the second

product costs 2 coins per unit. Storage costs, setting shelf space

aside, are considered negligible. Also, the shelf is large enough to

hold enough units to make stockouts negligible. In this situation,

the ﬁrst product has a RoR of 200% per day, against 50% per day

for the second product. Thus, the ﬁrst product should be preferred

because it has the higher RoR. This result is unsurprising: the

products are identical except that the ﬁrst has twice the gross mar-

gin of the second. The RoR simply provides the ranking criterion

that formalizes this choice.

In typical supply chain settings, maximizing RoR tends to

allocate more capacity, stock, and shelf space to products with

higher volumes or faster rotations. However, RoR—not arbitrary

noneconomic metrics such as service levels—must be optimized.

Many supply chain authors mistake the loose correlation between

4.4. THE GOAL OF SUPPLY CHAIN 89

higher RoR and higher volumes or rotations for causation. As a

result, inventory classiﬁcation methods are frequently proposed

to segment products into classes

reﬂecting their volumes and/or

their erraticity and adjust the resource allocations according to this

segmentation. These methods confuse correlation with causation:

RoR must be optimized. Mimicking its loose consequences is a

categorical error that yields diminished returns.

In practice, the rate of return presents a series of complications:

Calculating RoR depends on revenue and spending attribu-

tion—a diﬃcult problem.

Revenues and spending do not occur at the same time; a

shared period rarely exists.

Revenues should occur as early as possible and spending as

late as possible; cash ﬂows must be discounted.

The lag between future spending and future revenues must

respect the ﬁrm’s liquidity constraint.

Dependencies between options make RoR path-dependent

on the picking order.

Both future revenues and spending are uncertain, and this

uncertainty must be modeled.

None of these complications prove insurmountable in practice, even

if they all entail some degree of approximation.

For example, from an inventory replenishment perspective, the method

referred to as “ABC analysis” proposes to organize products in 3 to 5 classes

depending on their respective volumes. Each class is assigned its own target

service level used to dimension the corresponding safety stocks. The ABC/XYZ

is a variant that segments products based on projected demand and projected

erraticity. Those noneconomic methods betray a lack of understanding of the

nature of supply chain.

90 CHAPTER 4. ECONOMICS

Revenue and spending attribution

RoR reﬂects an idealized view of physical ﬂows. However, con-

straints require nontrivial economic modeling. As a result, in

order to compute the RoR of a given option, the option must be

disentangled from the ﬁrm, typically through economic attribution.

This point is discussed below; for now note there is no one-to-one

mapping between revenues, spending, and options. As a result,

RoR is a modeling perspective on supply chain, not a self-evident

truth.

For example, consider a retail store with two alternative prod-

ucts. The ﬁrst product requires 100 coins of inventory—renewed

daily—to generate 140 coins of daily revenue. The second prod-

uct requires 150 coins of inventory to generate 200 coins of daily

revenue. The two products take exactly the same shelf space in

the store. In this case, the RoR of the ﬁrst product is 40% per

day while the RoR of the second is 33%. Thus, by the principle

of picking the options yielding the highest RoR, the store owner

should pick the ﬁrst product. However, this choice is surprising:

it selects the product that will ultimately generate less absolute

gross proﬁt over time. For a store dominated by ﬁxed costs, this

seems incorrect.

Indeed, the naive model above ignored the store’s ﬁxed costs.

Let’s reﬁne the model by attributing a portion of ﬁxed costs to

the products. An attributed daily ﬁxed cost is 40 coins. Thus, the

daily cost for the ﬁrst product becomes 100 + 40 = 140 coins, and

for the second 150 + 40 = 190 coins. Under this revised model,

the RoR of the ﬁrst product becomes 0% per day while the second

becomes 5.3%. Therefore, under this reﬁned model, the second

product should be favored.

This situation is not exceptional: what qualiﬁes as best depends

on the adopted economic model for the supply chain. Unfortunately,

no single, straightforward criterion decides which model is superior;

adequacy hinges on how well each captures the economic reality

it is meant to inform. This challenge is a problem of general

intelligence. That does not mean anything goes; we revisit this

4.4. THE GOAL OF SUPPLY CHAIN 91

class of challenges later on.

Aperiodic rate of return

In ﬁnance textbooks the rate of return (RoR) is almost always

tied to a ﬁxed horizon—a month, a quarter, a year—because that

is the cadence of accounting and taxation. At the micro level of

supply-chain execution, however, such calendars are an awkward

ﬁt. Raising a purchase order, launching a markdown, allocating

a pallet, or scheduling a truck are atomic decisions whose cash

outﬂows and inﬂows trickle in at irregular intervals. A twelve-

month yardstick blurs the very reason they exist: to recycle capital

as fast as possible.

The aperiodic RoR removes the calendar straitjacket. Instead

of forcing every option into an arbitrary window, it asks a single

question: after how long will the option have repaid the cash it

tied up and started compounding its surplus? That self-selected

delay becomes the implicit period in the RoR formula, yielding

a compound rate comparable across decisions, even though each

chooses its own clock.

Because the computation is performed at the level of individual

options, the spread of results is wide—diﬀerences of one and even

two orders of magnitude are routine. A fast-moving, high-margin

reﬁll can clear 1,000% per annum once expressed in aperiodic

terms, while the replenishment of a slow-seller in the next aisle

may struggle to reach 10%. Such dispersion is not a modeling ﬂaw;

it is the signal that tells capital where it is urgently needed and

where it quietly dies.

Operationally, the aperiodic RoR slides a cursor along the

option’s cash-ﬂow timeline, computing at each date the rate that

would prevail if the position were closed out then. It keeps the

maximum value; the date that yields this peak becomes the option’s

implicit horizon. In one number, the metric captures both the

scale of the gain and the fastest pace at which the tied-up capital

can compound. The exact rule is spelled out in the annex.

92 CHAPTER 4. ECONOMICS

Discounted cash ﬂows

The previous subsection introduced the rate of return as a yardstick

for how fast committed capital compounds. Speed, however, is

only half the temporal story. A coin received today is worth more

than the same coin received next quarter, because the earlier coin

can be reinvested, held as a buﬀer, or simply earn interest in the

meantime. Discounted cash ﬂow is the numerical translation of

the time preference: each future inﬂow or outﬂow is multiplied by

a discount factor that shrinks with time, so every cash movement

is expressed in “today-coins”. The discount factor derives from the

ﬁrm’s cost of capital—the interest eﬀectively paid for liquidity. For

the precise formula, see the annex.

Put diﬀerently, discounted cash ﬂow measures the absolute

amount earned (or lost) while accounting for the time value of

money. From the supply chain perspective, the interest rate repre-

sents the company’s ﬁnancing cost. In ﬁnance, this rate often also

proxies uncertainty associated with delays, but in supply chain

use cases uncertainty is better handled through other mechanisms

detailed later.

A practical illustration of discounted cash ﬂows in shipping

and stock decisions appears in fashion, which often faces long lead

times and cash constraints. Imagine a mid-tier fashion brand that

must place orders for its new collection six months in advance of

the season. The brand negotiates terms requiring a substantial

deposit upfront, with the remainder due upon shipment. Even if

borrowed funds cost only, say, 10% per year, the brand’s eﬀective

rate of return on these early orders can dwarf that ﬁgure. This

is because the brand’s capital is tied up long before revenues

materialize, and any misjudgment in sizing, color, or style inﬂates

the eﬀective cost of the goods. When the season arrives, the

brand may discount slow-moving items, adding to the opportunity

cost of money that could have been invested in a more promising

line. Conversely, a well-placed order that aligns with demand can

rotate quickly at full price—delivering a rate of return that far

surpasses the cost of capital. Thus, the interplay of long lead times,

4.4. THE GOAL OF SUPPLY CHAIN 93

extended payment terms, and market uncertainty often outweighs

the ambient interest rate in determining the real proﬁtability of

each supply chain decision.

In practice, for supply chain decisions, the ambient interest rate

matters less than the rate of return, largely driven by inventory

rotations. This is not to deny the importance of interest rates.

Yet a unit of inventory yielding 10% per rotation and turning ten

times per year compounds to 1

≈

59, i.e. 159% per year. A

single company often sells products whose rates of return range

from 1% to 1,000% per year. This wide spectrum stems largely

from inventory rotations that range from daily to annual. As a

result of such extremes, the proﬁt-maximizing quality of service

varies greatly from one product to the next. Stocking strategies

diverge accordingly.

An annual rate of return of 1,000%, while not atypical for the

ﬂow of an isolated product within supply chain, exceeds by more

than an order of magnitude what ﬁnance deems “plausible”. Natu-

rally, this calculation ignores ﬁxed costs—above all, infrastructure.

Once those costs are accounted for, eﬀective rates of return in

supply chains align with those in the general economy.

Cash ﬂow and working capital

Interest rates set the ﬂoor for the cost of liquidity; they therefore

matter to every supply chain. In practice, however, they usually

hover within a relatively narrow corridor—say roughly 10–20% per

annum—whereas inventory-cycle returns routinely span two orders

of magnitude. Consequently, what most often constrains planners

is not the precise price of money but its availability. A buyer who

can draw cash at 12% instead of 8% will still proceed if the stock

he ﬁnances compounds at 300% per year; conversely, a nominal

2% loan is useless once the credit line is exhausted. Cash-ﬂow

stewardship is therefore critical—especially in businesses that must

wire funds months before the goods clear customs or reach the

shelf; fashion collections, holiday toys, and high-tech gadgets are

everyday examples.

94 CHAPTER 4. ECONOMICS

Take, for instance, a ﬁrm whose bank extends a revolving

credit facility capped at 25% of the company’s average turnover

over the past three years, charged at a ﬂat 10% per annum. Up

to that limit, cash can be drawn overnight; the instant the cap

is reached, however, every extra coin requires a fresh negotiation

and is subject to refusal. Alternative lifelines—equity injections,

mezzanine debt, supplier factoring—certainly exist, but none can

be secured overnight, and all carry signiﬁcantly higher explicit and

implicit costs. Liquidity is therefore priced by a piecewise curve

rather than a single number, and any model that assumes one

uniform interest rate ignores precisely the nonlinearity that most

often bites in practice.

The nonlinearities associated with access to liquidity explain

why payment terms are often of prime importance in supply chain.

Those payment terms critically shape the amount of working capital

required by the company. In the long run, lower working-capital

requirements improve proﬁtability by reducing interest paid to

lenders; in the short run, they often make the diﬀerence between a

company that operates smoothly and one forced into inferior choices

because better options are foreclosed by insuﬃcient liquidity.

For example, a fashion brand may have to place purchase

orders for pieces in its new collection more than six months before

the season starts. Overseas suppliers may require substantial up-

front payments. If the fashion brand anticipates stronger sales for

this collection, it may issue larger purchase orders. However, the

company may be blocked by an insuﬃcient credit line. In this

case, smaller purchase orders follow, possibly causing substantial

stockouts downstream. Naturally, from the lender’s perspective,

the cap is not arbitrary: the lender simply does not share the

brand’s optimism about the collection’s outsized success.

The ﬂat-interest-rate assumption rarely holds, though there are

situations where it is an acceptable approximation. The simplest

case is when the company has access to liquidity that dwarfs

proﬁtable supply chain opportunities. A slightly more subtle case

is when the company has negative working-capital requirements.

To illustrate, consider a retailer that keeps store inventory turn-

4.4. THE GOAL OF SUPPLY CHAIN 95

ing monthly with negligible supplier lead time. Assume suppliers

are paid two months after delivery, and products are sold in the

store with a 20% gross margin. Under those conditions, for every

four coins’ worth of goods held in store, the retailer has −2 coins

of working-capital requirement. Indeed, by the time the goods

must be paid, the inventory will have rotated twice on average,

yielding two coins of gross proﬁt. Here the retailer has negative

working-capital requirements—at least from the perspective of cash

ﬂow alone.

This example must not be confused with negative interest rates;

the retailer remains subject to market rates.

Returning to the more typical case with a short-run cap on

working capital, one needs a numerical method to compare the

options. One can, conceptually, pick a feasible set of options that,

in aggregate, enforce the working-capital constraint—an approach

akin to invoking a general solver. However, simpler heuristics can

be used, such as introducing a time-dependent component for the

cost of money.

Dependent options

The rate-of-return metric singles out the best next use of capital,

yet the options themselves rarely stand alone. Most tap the same

ﬁnite pools—cash, shelf space, vehicle capacity—and therefore

interact. The interactions can be competitive, when two SKUs vie

for the same slot; complementary, when coﬀee and sugar lift each

other’s sales; or sequential, when the second pallet only makes sense

after the ﬁrst has been allocated. Recognizing these couplings is a

prerequisite for translating the metric into sound decisions.

A practical heuristic for dependent options is to let the rate

of return drive a rolling, one-by-one selection. Pick the option

with the highest current rate, update every remaining rate to re-

ﬂect the resources just committed (or released), pick again, and

repeat until all constraints are binding. Computer scientists call

this a greedy algorithm; its outcome is a prioritized list. Because

each refresh propagates current constraints, the list need not stay

96 CHAPTER 4. ECONOMICS

in perfect descending order—most rates drift downward as com-

petition stiﬀens; a few may even rise when a complementarity

is unlocked—yet every step remains the best move available at

that moment. Where couplings are mild, this greedy pass suﬃces;

where they are tight—vehicle routing or production sequencing—a

heavier solver that explores many combinatorial paths outperforms

the heuristic while resting on the same valuation bedrock.

For example, consider the challenge of replenishing a retail

store. Based on the rate of return, select one unit to add to the

store—the one that oﬀers the highest rate. Once this one unit

has been, conceptually, added to the store, the rates of return

are adjusted. The next unit of the same product has a lower

rate of return, as piling up inventory in the same store exhibits

diminishing returns. A second unit is then picked based on the

revised rates of return. Repeat the process until the store’s shelf

capacity is reached.

Greedy optimization oﬀers several major beneﬁts. First, it

requires nothing more than establishing the options’ rates of return.

Second, the process is simple and observable: the prioritized list

provides a complete picture of the algorithm’s progression. Third,

when properly implemented, it is typically orders of magnitude

faster than non-greedy alternatives.

A naive implementation of this greedy optimization logic incurs

quadratic computational cost relative to the number of compet-

ing options. For most supply chain situations with thousands

of options, quadratic cost is unacceptably high. However, meth-

ods—beyond the scope of this discussion—mitigate this overhead,

reducing complexity to quasi-linear time.

Most supply chain challenges are well suited to greedy or quasi-

greedy methods because the options are loosely coupled. This is

no accident: companies deliberately avoid systems that resemble

cryptographic puzzles; what is diﬃcult for a computer is also

challenging for the human mind.

For instance, a specialized retailer selling three technical prod-

ucts—A, B, and C—typically purchased together in a ﬁxed ratio

(e.g., two units of A, ﬁve of B, seven of C) to form a kit can simplify

4.4. THE GOAL OF SUPPLY CHAIN 97

inventory management. Instead of tracking three separate items,

the retailer treats the preassembled kit

as a single inventory item,

while maintaining a small surplus of individual components for

exceptions. This consolidation reduces complexity and streamlines

replenishment.

However, greedy optimization is not suited to every supply-

chain scenario—vehicle routing and production scheduling are

notable exceptions. More generally, problems involving sequences

of highly interdependent decisions do not lend themselves to the

greedy method. That said, many—if not most—supply-chain

situations can be treated quasi-greedily with minor twists. For

example, when placing simultaneous purchase orders to multiple

suppliers—each with its own minimum order quantity (MOQ)—a

greedy strategy may yield infeasible results, such as a series of

incomplete orders that fail to meet thresholds under a limited

budget. Yet a minor heuristic suﬃces to address this limitation.

One can iteratively drop suppliers—starting with those yielding the

lowest returns—and redistribute the budget among the remaining

suppliers.

While the complications arising from dependent options do not

undermine the principle of selecting options with the highest rate

of return, they may render naive selection methods ineﬀective. To

handle these complexities, dedicated optimization software—often

called solvers—is required. Notably, these solvers optimize against

a wide array of loss functions (the quantity to be minimized), a

topic addressed later in Chapter Decisions.

Uncertain rate of return

Interdependencies were the ﬁrst hurdle; uncertainty, the second.

Even after mapping how options compete for cash, capacity, or

shelf space, we still do not know which numbers will materialize

when those options unfold. Demand, lead times, yields, and spot

prices all waver outside the planner’s control. Yet the guiding

principle endures: choose the option with the greatest economic

return—measured not at a point, but in expectation. In practice,

98 CHAPTER 4. ECONOMICS

replace every deterministic payoﬀ with its probability-weighted

counterpart: list plausible scenarios, assign each a likelihood, apply

the deterministic formula within each branch, then average the

results. This produces an expected rate of return that can be

compared across options as before.

While conceptually straightforward, the probabilistic perspec-

tive presents its own challenges. First, probabilities must be esti-

mated—this is the essence of probabilistic forecasting. Second, the

future hinges on decisions not yet made: an option’s rate of return

depends on how future options will be selected. Choosing present

and future options under uncertainty is the essence of stochastic

optimization.

Probabilistic forecasting and stochastic optimization are techni-

cal subjects that will be discussed in Chapter The Future. Yet the

probabilistic view of the rate of return is no mere theoretical curios-

ity. With proper software, real-world supply chain options can and

should be evaluated by this method. Naturally, approximations

are unavoidable—the number of potential futures is inﬁnite—but

this does not diminish the approach’s practical value.

4.4.4 Cronyism and regulatory capture

The proﬁt motive is often caricatured as a cold-blooded quest for

“more at any cost”. In reality, within a competitive market, proﬁt

is simply the applause earned for satisfying the most urgent wants

of strangers while spending the fewest scarce resources. Proﬁt is

therefore an outcome, not a creed.

Behind the single word “proﬁt” lie three radically diﬀerent

behaviors. Genuine proﬁt seeking earns margins by oﬀering a

superior mix of availability, quality, and price. Long-run proﬁt

therefore depends on nurturing goodwill with customers, suppliers,

and employees. Slash-and-burn tactics that delight today’s spread-

sheet but erode tomorrow’s options prove self-defeating once their

full rate of return is computed.

A second behavior is the “naked” proﬁt motive popular culture

loves to condemn: dumping waste in rivers, bullying suppliers,

4.4. THE GOAL OF SUPPLY CHAIN 99

or bait-and-switching customers. Such conduct is not the logical

outcome of proﬁt seeking; it is the outcome of faulty economic

calculation. When legal ﬁnes, lawsuits, talent ﬂight, and brand

damage are brought back to present value, the mirage vanishes.

Modern supply chain analytics exists precisely to surface those

latent costs by turning them into shadow valuations that discipline

every decision.

The third behavior is crony capitalism and regulatory capture.

A ﬁrm that lobbies for tariﬀs, exclusive licenses, or mandatory

standards does not earn proﬁt; it extracts rent. No customer is

served better—resources are merely diverted by political privilege.

Rents distort every supply chain signal: optionality shrinks, in-

novation stalls, and the apparent rate of return is propped up by

statute rather than performance.

The discipline presented in this book targets the ﬁrst path

while inoculating the ﬁrm against the latter two. All subsequent

techniques assume an open market: they benchmark each option

against what a vigilant competitor could do tomorrow, not against

a privilege secured in parliament yesterday. Restore genuine compe-

tition and the same optimization machinery becomes an engine of

wealth creation; let rents accumulate and no amount of forecasting

ﬁnesse can salvage the economics.

4.4.5 Supra-economic goals

A recurrent objection to proﬁt-seeking ﬁrms is the appeal to supra-

economic goals—ends that allegedly outrank mere monetary con-

siderations and thus justify overriding the discipline of prices, costs,

and opportunity costs. These calls usually come in two ﬂavors.

The ﬁrst is moralistic: a conviction that the ﬁrm should advance

some ethical or social agenda beyond serving customers. The

second is apocalyptic: a prediction of looming catastrophe that

The sharp fall in U.S. freight rates after the deregulation of trucking in 1980

(Motor Carrier Act of 1980, Public Law 96-296.) shows how quickly eﬃciency

returns once artiﬁcial moats are removed.

100 CHAPTER 4. ECONOMICS

demands immediate sacriﬁce of proﬁtability to avert disaster. Both

categories will be examined in the pages that follow.

For companies that operate in open markets, invoking a higher

purpose does not dissolve scarcity; it simply relabels trade-oﬀs.

Every pallet, man-hour, and coin devoted to one objective is un-

avoidably withheld from another. Economic calculation is therefore

not an optional worldview but the only coherent way to compare

alternatives. Institutions that rely on coercion—armies, for ex-

ample—sit outside the scope of this book precisely because their

resources are not allocated through voluntary exchange. For all

other organizations, the alleged supremacy of supra-economic goals

is an illusion; in practice, they must still decide whether diverting

resources from their core mission creates or destroys long-run value.

Moralistic concerns

In supply chain terms, a moralistic concern asserts that people

should consume less—or more—of something; hence the company

should help steer the desired social change. Details vary by time

and place.

For example, in Ruritania, a dominant religion might declare

that milk consumption by adults is a sign of depravity, and that

milk should be reserved for children. In response, a Ruritanian

company might voluntarily reduce both the quantity and variety of

milk bottles marketed to adults, thereby proactively enacting the

seemingly desirable social change. Yet such a move will scarcely

aﬀect the market. Contrary to the popular trope of “economic

power”, a company cannot force customers to buy or forgo its

products. If the company arbitrarily cuts production of adult-

branded milk bottles, a single competitor will raise output to keep

demand equally satisﬁed.

If, however, public sentiment in Ruritania against bottled milk

for adults is so strong that many customers boycott other unrelated

products, it may be reasonable to divest that line entirely—perhaps

by selling the unit if its production can be separated from other

lines. This approach diﬀers fundamentally from merely lowering

4.4. THE GOAL OF SUPPLY CHAIN 101

supply in the misguided hope of lowering overall market demand.

Typical moralistic concerns include foods or beverages deemed

inappropriate, either for alleged harm to individuals or for harms

incurred during production. Veganism is, for example, one of the

more recent creeds in an otherwise extremely diverse set of creeds.

These concerns also extend to country of origin, often grounded in

incorrect mercantilist

views of trade (e.g., “we are at economic

war with Ruritania”) or in perceptions of work conditions in those

countries (e.g., “Ruritania lets its companies unduly exploit its

citizens”).

Supply chain is concerned with moralistic views only insofar as

serving a type of good, or using a particular factor of production,

might compromise its general capacity to operate. Brand damage

can alienate customers or diminish the company’s appeal to po-

tential employees. These concerns are real and can be quantiﬁed

just like any other risk. Economic calculations may indicate that

such risks are excessive, suggesting that the company should exit

a market segment to minimize future losses. However, these calcu-

lations should not be mistaken for the personal value judgments

of supply chain employees.

What makes the challenge thornier, however, is that employ-

ees

have perverse incentives when it comes to moralistic concerns.

Mercantilism, which was the dominant economic view from the 16th to the

century in Europe, presents countries as economic adversaries. Exports are

seen as favorable, while imports are seen as unfavorable. Local businesses are

frequently eager to promote a mercantile perspective as it is a proven method

to have the political apparatus block, or at least hinder, foreign competition.

However, while countries can be at war in a geopolitical sense, there is no such

thing as “economic warfare”. Free markets are based on voluntary transactions

that only happen if both parties view the transaction as beneﬁcial. Mercantilism

is another long-disproven economic perspective that still remains popular to the

present day.

The fate of the company and the fate of its employees are distinct, and

employees are fully aware of that. Even if a loyalty program is in place through

stock options or similar means, getting an internal promotion is almost invariably

more impactful for the employee than whatever reward the loyalty program

might oﬀer. If an employee has to choose between a promotion and the reward

of the loyalty program, it is relatively safe to bet that the promotion will be

102 CHAPTER 4. ECONOMICS

By publicly pressing a moralistic concern, in a process referred to

as virtue signaling, an employee presents himself as particularly

virtuous—more so than his peers. Virtue signaling was and re-

mains eﬀective because corporate politics tend to reward those

who conform most to prevailing social norms rather than solely

those who drive the company’s proﬁtability.

However, within a supply chain, it is not the virtue-signaling

individual who bears the cost of the revised course of action, but

the company itself—and, to some extent, the market, which will

be less adequately served until a competitor steps in to ﬁll the gap.

Thus, corporate executives must remain vigilant against such virtue

signaling. Indeed, large companies are especially susceptible to

this class of antisocial behavior, which undermines the authority of

the nominal hierarchy and its ability to steer the company toward

serving customers.

Apocalyptic concerns

Throughout history, a fascination with annihilation has gripped

a signiﬁcant portion of humanity. Exploring these morbid fasci-

nations and understanding why they have endured for millennia

is a matter for psychology, sociology, and history. Supply chains

are concerned only insofar as these fascinations interfere with the

conduct of business. History yields two observations: ﬁrst, the

magnitude and impact of apocalyptic concerns have been vast;

second, their speciﬁcs have varied dramatically from decade to

decade. Let us review a short list of modern apocalypses once in

fashion.

•

Overpopulation (18

–19

century): The rapid popu-

lation growth during the ﬁrst industrial revolution led nu-

merous intellectuals to believe that population growth would

inevitably outstrip food production, leading to widespread

famine and societal collapse. (See An Essay on the Principle

of Population (1798) by Thomas Malthus.)

chosen.

4.4. THE GOAL OF SUPPLY CHAIN 103

•

Degeneracy of the Race (1920s–1930s): Fears of racial

and genetic degeneration, inﬂuenced by eugenics and pseudo-

scientiﬁc racial theories, predicted the decline of civilizations

due to the mixing of races. (See The Passing of the Great

Race (1916) by Madison Grant.)

•

Communist Takeover and Red Scare (1950s): The

rise of the Soviet Union and the spread of communism led

to fears of a global communist takeover, resulting in the

Red Scare and McCarthyism in the United States. (See The

Naked Communist (1958) by W. Cleon Skousen.)

•

Chemical Agriculture (1960s): The detrimental eﬀects

of pesticides, particularly DDT, on the environment and

public health highlighted broader concerns about industrial

pollution and ecological collapse. (See Silent Spring (1962)

by Rachel Carson.)

•

Nuclear Winter and Global Cooling (1970s): Amid

the Cold War, fears of a nuclear winter—an aftermath of

nuclear war causing global cooling—merged with concerns

about a potential natural ice age. (See The Cooling: Has the

Next Ice Age Already Begun? (1976) by Lowell Ponte.)

•

Resource Depletion (1970s–1980s): Numerous predic-

tions were made that humanity was running out of essential

resources like oil and minerals, leading to societal collapse.

(See The Limits to Growth (1972) by Meadows et al.)

•

Access to Fresh Water (1980s–1990s): The depletion

and pollution of freshwater resources were predicted to lead

to severe shortages and conﬂicts over water. (See Cadillac

Desert: The American West and Its Disappearing Water

(1986) by Marc Reisner.)

•

Acid Rain (1990s): Industrial emissions causing acid rain

were feared to be devastating forests, lakes, and buildings,

leading to widespread ecological damage. (See Acid Rain:

104 CHAPTER 4. ECONOMICS

Its Causes and its Eﬀects on Inland Waters (1992) by B. J.

Mason.)

•

Ozone Depletion (1990s): The depletion of the ozone

layer due to refrigerant gases, leading to increased ultraviolet

radiation, skin cancer, and ecological disruptions. (See Ozone

Diplomacy: New Directions in Safeguarding the Planet (1991)

by Richard Elliot Benedick.)

•

Global Warming (2000s): Rising levels of “greenhouse”

gases were predicted to cause catastrophic climate changes,

including sea level rise, extreme weather, and loss of biodi-

versity. (See An Inconvenient Truth: The Crisis of Global

Warming (2006) by Al Gore.)

•

Climate Change (2010s–2020s): Building on the global

warming narrative, climate change encompasses broader con-

cerns about unpredictable and severe environmental impacts,

threatening the sustainability of human civilization. (See

The Uninhabitable Earth: Life After Warming (2019) by

David Wallace-Wells.)

This list spans the past 150 years, though it could extend back

millennia. Its purpose is not to validate or refute these threats—

such judgments lie outside supply chain and within specialized

sciences. Rather, it illustrates the phenomenon’s magnitude and

the inconsistency of the threats’ particulars.

Apocalyptic concerns are supra-economic because the view is

that, if unaddressed, the world—and thus the market economy—

will end or be left in an irretrievably degraded state. By deﬁnition,

an apocalyptic concern overrides any individual’s particular want.

After all, no individual can be anything but insigniﬁcant when

weighed against the possibility of oblivion for a sizable portion of

mankind, and potentially all of it.

For apocalyptic fears that have fallen out of fashion, it is evident

that companies which historically allocated resources to support

4.4. THE GOAL OF SUPPLY CHAIN 105

those causes were severely misguided, resulting in signiﬁcant self-

inﬂicted harm. Few managers would still argue that a hiring policy

explicitly discriminating against a minority religion reﬂects the

best economic interests of the company. Similarly, few managers

would nowadays argue that generous donations to charities and

political movements advocating eugenics genuinely contribute to

the betterment of mankind. Yet, not so long ago, both policies

were widely practiced by educated and well-intentioned people,

operating under what was considered ﬁrm “scientiﬁc” consensus

These apocalyptic concerns disrupt supply chains because their

supra-economic claim invalidates economic calculation. Companies,

however, still must allocate scarce resources. Apocalypse does not

abolish scarcity. There is no alternative to economic calculation

when it comes to satisfying customers. If a concern outranks

satisfying customers, why allocate anything less than all resources

to it?

As King Richard cries in Richard III, “A horse, a horse! My

kingdom for a horse!”

More generally, the notion that any concern over allocating

scarce resources enjoys supra-economic status, categorically super-

seding mundane concerns, reﬂects a profound misunderstanding of

economics. There is no alternative to proﬁts and losses.

Non-proﬁt organizations

In most Western countries, tax laws grant special exemptions to or-

ganizations styled “non-proﬁt”, unlike those presumed “for-proﬁt”.

Whether such tax exemptions and regulatory relief

beneﬁt soci-

For an extensive survey of disastrous ideas that enjoyed massive popularity

among intellectuals in their time, see Intellectuals and Society (2010) by Thomas

Sowell.

Anticapitalism has been dominant among intellectuals for the last 150 years

or so. The pursuit of proﬁts is seen as one of the most serious defects of

capitalism. The non-proﬁt status supposedly ﬁxes this defect and thus must be

rewarded with some sort of tax exemption and/or regulatory relief with respect

to obligations imposed on regular for-proﬁt businesses. Whether the pursuit of

proﬁts is generally good or bad for society as a whole is a concern of praxeology,

106 CHAPTER 4. ECONOMICS

ety at large lies beyond supply chain. However, as far as general

economic understanding goes, this label is very unfortunate and

generates immense confusion among the general public. Worse,

many authors of supply chain books share the confusion and as-

cribe quasi-mystical qualities to non-proﬁts, echoing popular but

misguided beliefs. These authors mistakenly believe that associa-

tions (i.e., so-called “non-proﬁt” organizations) are exempt from

the principles of proﬁts and losses. This is patently false. Associa-

tions operate under the same economic laws and, insofar as some

operate a supply chain, obey the same principles. Consequently,

a technique meant to improve supply chains is equally valid—or

equally invalid—regardless of taxation status.

A few facts suﬃce. Both kinds of organizations are driven by

entrepreneurs. Some entrepreneurs who run for-proﬁts fail and end

poorer than they started. Conversely, some who run non-proﬁts

succeed and command salaries far exceeding the national average.

Some proﬁtable companies distribute dividends, while others do not.

Both companies and associations see corruption scandals. Both

require customers to fund them

. Customers have expectations,

and in both cases, if a competing organization delivers more for

less, customers favor the more eﬃcient organization. Both expand

when they collect more than they spend; both go bankrupt when

they spend more than they collect.

Calling an association “non-proﬁt” is a misnomer: proﬁt and

loss are the basic facts of economics. The organization collects and

spends resources. If collections exceed outlays, there is proﬁt. If

outlays exceed collections, there is loss. Proﬁt and loss exist even

without an accountant. When an accountant keeps a ledger, the

situation is clariﬁed; he creates neither proﬁt nor loss, he merely

the science of human action, not of supply chain. For an extended discussion,

see Human Action (1949) by Ludwig von Mises.

Associations are frequently funded, at least in part, through taxation in

the form of government subsidies. This only makes the government one of

their customers. Associations routinely have enterprise customers as well. The

speciﬁcs of the customers are a technicality that has no bearing on the general

economic operating principles governing an association.

4.4. THE GOAL OF SUPPLY CHAIN 107

reveals them.

In the market economy, all actors strive to address customers’

most pressing wants. This is true for companies and associations

alike. For example, if customers are concerned with deforestation,

they may spend to plant trees, or have them planted. By giving to

an association dedicated to reforestation, customers make a value

judgment: they buy future forests; a world with more forests is their

reward. If presented with several equally dedicated organizations,

and one can grow twice as much forest per coin, customers will

favor it. If customers later discover that the association spent all

it collected on advertising and planted no trees, they will rightly

feel defrauded and likely abstain from further donations.

The absence of a direct correspondence between a customer’s

payment and the service rendered by an association is merely a

technicality. The same pattern appears in business: urban waste

collection is usually paid via an annual lump sum rather than per

service rendered, yet many such providers are for-proﬁts.

Calling an organization “non-proﬁt” is as nonsensical as calling

it “gravity-free”. Natural laws admit no objection; those that govern

economics are no exception. How proﬁts are allocated—whether

distributed to shareholders, reinvested in further capital goods,

or used to raise employee wages—is a technical detail. Taxation

schemes materially aﬀect these technicalities, but not in a way

categorically diﬀerent from, say, subsidizing a sector such as solar

energy. It is merely one of many approaches by which governments

partition the market in distributing subsidies. Subsidies confer no

special economic power on associations.

Ultimately, entrepreneurs’ inner motivations remain unknow-

able to external observers, whether their organizations are for-proﬁt

or non-proﬁt. They may be genuinely moved by the improvement

of their fellow men, or merely chase vanity and the publicity that

attends a successful association. Men can lie; they can also change.

What starts in selﬁsh intent may, over time, become genuine altru-

ism; the converse is also possible.

Thus, supply chain cannot categorically distinguish alleged non-

proﬁts from for-proﬁts. This distinction belongs to tax codes, not to

108 CHAPTER 4. ECONOMICS

supply chain. Authors who claim that associations possess unique

supply chain properties are either engaging in virtue-signaling—

posturing with noble causes—or are misled by ideology, attributing

quasi-mystical qualities to these organizations.

Armed forces

Non-proﬁt organizations illustrate how proﬁt and loss remain

inescapable even under altruistic pretenses. Armed forces oﬀer a

distinct case: an institution squarely within economics yet lacking

the usual for-proﬁt feedback loops. Armies do not earn revenue

by selling services to a clientele; rather, they are funded through

taxation raised by a sovereign power. This reliance on state coercion

does not exempt them from scarcity; it only changes how resources

are allocated. Economic laws do not evaporate in the presence of

arms and uniforms: every bullet and barrel of fuel meets the same

constraints that govern the rest of the economy.

From an economic perspective, a “military” is among the most

extensive central-planning structures in modern societies. The

armed forces must maintain equipment, procure supplies, support

troops, and project power across various geographies. However,

these activities cannot be assessed using the proﬁt-and-loss frame-

work applied to market actors. While a private company constantly

balances expenditures against customer payments, the military is

funded without voluntary exchange, and thus lacks direct market

discipline. The invisible hand that fosters eﬃciency in private

ventures is replaced by top-down directives meant to integrate

strategy rather than short-run ﬁnancial returns.

Yet the absence of proﬁt signals does not spare the armed forces

from the problems of economic calculation. As Ludwig von Mises

demonstrated in Socialism (1922) and Human Action (1949), when

a central planner attempts to guide resource allocation outside the

pricing process, there is no reliable gauge to separate proﬁtable

from unproﬁtable uses. Without the discipline enforced by free

markets, misallocation is almost guaranteed. Such misallocations

remain hidden in peacetime—when the budget appears as a sin-

4.4. THE GOAL OF SUPPLY CHAIN 109

gle line item—but become evident when the armed forces face

shortages of critical gear or surpluses of obsolete equipment. The

same challenges aﬄict large companies running complex supply

chains, but armies face them on a grander scale, with life-or-death

consequences.

In modern times, defense budgets running into the billions

reﬂect complex trade-oﬀs made at the highest political level. The

classical “guns versus butter” analogy captures how each unit

devoted to the military is one not spent on civilian goods—schools,

hospitals, roads, or other productive ventures. The armed forces

do not “proﬁt” when they fulﬁll their mission; rather, they receive

continued political support to do more of the same. When a

new generation of ﬁghter jets or warships is commissioned, there

is no queue of customers whose willingness to pay would justify

the spending. Instead, the justiﬁcation emerges from strategic

imperatives, real or perceived, that fall outside the marketplace’s

regular accountability mechanisms.

Naturally, militaries try to mitigate the pitfalls of central plan-

ning by resorting to surrogate metrics: “readiness”, “mission ca-

pability”, “force projection”, and other strategic or bureaucratic

indicators. While useful, these indicators are no substitute for the

immediacy of proﬁt and loss. A victorious campaign produces no

revenue surplus for the armed forces, just as a protracted conﬂict

does not book directly as a loss. Extended conﬂicts can yield

larger defense budgets and expanded infrastructure, absent any

clear demonstration of eﬀective economic use. Decision-making

reverts to politics—what the public or its representatives deem

justiﬁable—rather than to trade-oﬀs shaped by voluntary demand.

These constraints generate unique supply chain challenges for

the armed forces. Demand is highly uncertain and driven by

geopolitical events rather than consumer preferences. Large stock-

piles are maintained to hedge against crises, and lead times can

be extraordinarily long for specialized equipment such as nuclear

submarines or stealth aircraft. When an army lacks suﬃcient

capacity to produce critical components itself, it must turn to

private defense contractors. At that juncture, a portion of the

110 CHAPTER 4. ECONOMICS

supply chain reenters the realm of market competition, at least

on paper: suppliers are paid according to negotiated prices, and

nations often try to pit multiple contractors against each other to

drive costs down. However, political interference and regulatory

complexities often distort these processes, leading to “cost-plus”

contracts and other arrangements that too often degenerate into

crony capitalism. As a result, while a veneer of market discipline

can exist, it is usually partial and overshadowed by bureaucratic

politics.

In the same vein, Hayek’s knowledge problem in The Use

of Knowledge in Society (1945) applies to the military as well.

Information relevant to resource allocation is widely dispersed.

Local units have up-to-date insights into their logistics and strategic

constraints, but higher-level commands make the grand allocation

decisions. Strict top-down hierarchies suppress bottom-up signals,

depriving upper echelons of the local knowledge needed to make

informed choices about procurement, deployment, or maintenance.

De facto, the arms industry acts as a closed microcosm of central

planning: no matter how competent or well-intentioned the oﬃcers

and bureaucrats, critical information is lost in transit, heightening

the risk of misallocation.

Nonetheless, all relevant resources remain scarce. Missing sup-

plies can sink entire campaigns, and, conversely, excessive stockpiles

of obsolete gear can hamper the military’s ability to ﬁnance mod-

ernizations. Moreover, as with any large-scale bureaucracy, routine

improvement eﬀorts add layers of process: new committees, new

oversight boards, new planning steps. Over time, these layers

develop inertia of their own, making course corrections harder.

Without a proﬁt test to cull waste, entire procurement lines may

survive long past their useful life. Politically favored programs

persist even when they yield little advantage on the battleﬁeld.

The result, economically, is structural inability to optimize resource

use. Militaries are not free to shed these constraints: they cannot

become agile forces guided by proﬁt. They fulﬁll functions outside

voluntary collaboration, and the price of the arrangement is the

absence of direct market feedback.

4.4. THE GOAL OF SUPPLY CHAIN 111

A ﬁnal peculiarity of the armed forces is that they are subject

to abrupt reorientation, typically following changes in political

leadership. Rebudgeting can be drastic, upward or downward,

depending on the perceived threat. Shifting alliances, new treaties,

or war-weariness can all reshuﬄe military priorities. While private

businesses in a free market also adjust to sudden shifts, they can

rely on proﬁt signals to guide them toward better allocations. By

contrast, armies must redesign their supply chains according to top-

down instructions, incurring the overhead of a large bureaucracy

that must simultaneously produce new doctrines, scrap old ones,

and realign entire ﬂeets of vehicles and equipment.

Thus, armed forces exemplify an extreme manifestation of the

same planning conundrums faced by large integrated enterprises.

The state does not measure the success of its armies by how

eﬀectively they satisfy consumers’ wants; no direct channel lets

consumer dissatisfaction act on the military budget. Yet resources

remain scarce, and the need to distribute them persists, so the logic

of economics still applies. The absence of proﬁt-and-loss signals

can be partly oﬀset by surrogate metrics, but such substitutes oﬀer

nothing close to the breadth and reﬁnement provided by market

prices. The upshot is that, even if armies operate in a non-market

context, every conﬂict, every peacetime mobilization, and every

behind-the-scenes logistical preparation exists under the iron law

of scarcity. While the state of war is often treated as pure politics

or strategy, at its heart it is also an elaborate allocation problem

no decree can escape.

4.4.6 Solutions and trade-oﬀs

There are no solutions. There are only trade-oﬀs.

A Conﬂict of Visions (1987), Thomas Sowell.

Supply chains are strictly bound by the laws of economics.

Each allocation of a resource forecloses all alternative uses that

were readily available. A decision is correct only if there is no

better use for the resource; proﬁtability is insuﬃcient—it must be

maximal.

112 CHAPTER 4. ECONOMICS

Any inventory allocation carries the opportunity cost of the

best alternative. In a retail network, once a warehouse unit is

dispatched to a store, it is no longer available to serve other stores.

If it was the last unit and it goes to a slow store while others sell

briskly, that allocation was likely a mistake. Better to route the

last unit to the store with the most urgent demand. Either way,

the unit will likely sell. Yet sending it where demand peaks aligns

the network with the customers’ most urgent wants. If the unit at

the mediocre store lingers, it may be returned to the warehouse

and redispatched to a more suitable store. However, this incurs

extra transportation costs, and while in transit the unit serves no

one.

Selling now trades oﬀ against the chance to sell more proﬁtably

later. The later, more proﬁtable outcome may not require a higher

price. For example, a screw bought at 1 coin and sold at 2 yields

1 coin of gross margin. However, if this screw—the last one in

inventory—belongs to a bundle (say, a machine sold for 100 coins

with component costs of 50), then selling the screw alone yields 1

coin now but forfeits 50 coins should a bundle buyer arrive before

more screws can be secured. If the ﬁrm expects a prompt bundle

buyer, selling the screw alone is the wrong supply chain decision

despite being proﬁtable. The correct move is to keep the screw in

stock to preserve the bundle sale.

Resources are varied, and the resulting trade-oﬀs are even more

so. Yet many supply chain authors reduce decisions to inventory

costs vs stockouts, cost vs cash vs service, or supply vs demand.

These low-dimensional perspectives are misguided. By elevating

a few engineering trade-oﬀs—rooted more in bookkeeping than

in economics—to ﬁrst principles, they obscure the only universal

yardstick: economic calculation.

Across any industry, supply chain is a continuous walk along a

tightrope of mutually exclusive improvements. Drop the ticket price

and volumes usually spike, yet every percentage point conceded

bites straight into the gross margin—and, in luxury segments, can

cheapen the brand so severely that unsold units end up destroyed

rather than discounted. Broaden the assortment with extra colors

4.4. THE GOAL OF SUPPLY CHAIN 113

or sizes and customers applaud, but procurement, storage, and

quality-control costs climb just as fast. Add backup suppliers to

hedge late deliveries, and the resilience is bought with weaker

purchasing leverage. Keep stores open longer and extra footfall

appears—along with a fatter wage bill. Hire temporary pickers to

clear a seasonal spike and throughput improves while errors and

shrinkage rise. Switch to a boutique component maker and the

ﬁnished good feels premium—provided the small plant does not

miss its slot. Relax the return policy to nurture loyalty and reverse-

logistics expense balloons. Rotate perishables daily to maximize

freshness and the shelf looks immaculate, yet deliberately thinner

inventory guarantees more stockouts. Every lever carries this dual

character; planners juggle dozens of such dilemmas at any moment.

This list could be extended almost endlessly across every ver-

tical and its speciﬁc trade-oﬀs. Moreover, nearly every named

resource can be further decomposed. The label is handy but

ultimately an approximation. For example, let’s say that the ware-

house capacity is 10,000

. In reality, not all of those

are

equal. Some sit near loading docks or conveyor endpoints. Ca-

pacity may be reﬁned to 2,000 “premium”

and 8,000 “regular”

One can always impose an arbitrary taxonomy on resources

and trade-oﬀs with as many classes as one fancies. Within a given

company, such a taxonomy may ease communication. Across the

market, such exercises are largely pointless. It yields no better

results than the common tongue.

The only practical approach to these trade-oﬀs is economic

calculation. Economic calculation balances these contradictions to

reach an adequate supply chain decision. It also makes clear that

no decision is an absolute good—a pure source of proﬁt—but a

mix of expected gains and losses.

114 CHAPTER 4. ECONOMICS

4.5 Valuation concerns

Supply-chain decisions are, at bottom, wagers: each one stakes a

scarce resource today for a stream of uncertain consequences to-

morrow. Trying to evaluate all those wagers in a single, monolithic

calculation is hopeless—the decision space is far too large and

changes too quickly. The practical remedy is to slice the grand cal-

culation into bite-sized fragments—one fragment per option—and

to equip every fragment with a valuation. A valuation is not an ac-

counting curiosity but an instrument: a money-denominated score

that renders options commensurable, so they can be compared,

ranked, and picked piece-by-piece rather than all at once. Good

valuations expose opportunity costs and power the optimization

engines introduced earlier; ﬂawed ones quietly steer the ﬁrm toward

systematic misallocation.

Seen in this light, valuations form the indispensable bridge

between the concrete world of pallets and trucks and the ab-

stract realm of economic calculation. They translate heterogeneous

events—an extra day of shelf life, an irritated customer, a late ven-

dor payment, a congested pick aisle—into one scale that software

can manipulate at millisecond pace. Building this bridge always

follows the same route.

First, the company frames an explicit economic model that

makes clear which ﬂows matter and how they connect to proﬁt and

loss. Second, it attributes every observed coin to the decisions that

generated it, so each micro-choice bears its fair share of shared

costs and beneﬁts. Third, it projects those cash ﬂows forward,

embracing uncertainty and adding shadow adjustments where a

bare ledger would miss material risks or beneﬁts such as goodwill

or brand damage.

The next subsections examine these three steps in turn. They

show how an explicit economic model anchors the calculation,

why attribution is both unavoidable and contentious, and how

projections—augmented by shadow and private valuations—turn

yesterday’s data into tomorrow’s actionable insight.

4.5. VALUATION CONCERNS 115

4.5.1 The economic model

The economic calculation starts with an explicit economic model:

a concise yet transparent mapping that links every operational

choice—buy, make, move, hold, price—to its projected ﬁnancial

footprint. In practice, the model translates the ﬂow of atoms into

ﬂows of coins by specifying four elements: the unit of analysis

(SKU, pallet, machine-hour, etc.), the time horizon over which con-

sequences unfold, the attribution rules that assign shared costs and

revenues, and the discounting convention that reconciles diﬀerent

dates. Its purpose is not prophetic accuracy; it is to surface the

otherwise invisible trade-oﬀs so that mutually exclusive options

can be ranked on a single yardstick—risk-adjusted return. Because

supply chains operate on several intertwined time-scales, multiple

sub-models usually coexist: a short-cycle replenishment model

centered on inventory rotations, a procurement model encoding

supplier rebates and MOQs, a capacity model showing how pro-

duction constraints convert into cash. Each is a facet of the same

discipline: turning physical decisions into discounted cash ﬂows.

For example, one of the simplest economic models is that of the

pure trader. The pure trader merely buys and sells goods without

transporting them, often leaving custody with the seller for an

agreed period. The strategy of the trader consists of buying low

and selling high. The relevant model reduces to projected gross

margin: per-unit sell price minus per-unit buy price. Variability

comes from the sell price, unknown at purchase time. Pure traders

serve various functions in the market, such as protecting sellers

against price variability or sparing them interactions with numerous

smaller clients.

While the pure trader is an extreme simpliﬁcation, real-world

supply chains invariably entail numerous subtleties. The economic

model is always approximate. Some approximations are intentional,

as they help to keep the model manageable. For example, all goods

are perishable: given enough time, even so-called stainless steel

rusts. Yet for many goods, shelf-life is so long that perishability is

negligible. In such cases, omitting perishability from the model is

116 CHAPTER 4. ECONOMICS

reasonable.

Other approximations are unintentional, reﬂecting operational

uncertainty. Knowledge can be reﬁned, but it will remain in-

complete. For example, stock may harbor defects noticed only

by customers, some of whom later request refunds or exchanges.

Quality control may push defects to near-negligible levels, yet the

possibility remains and knowledge never becomes absolute.

Designing the model—including its structure and intentional

approximations—is a problem of general intelligence. Those prob-

lems do not lend themselves to “mechanistic” resolutions. The

adequacy of the model is always a matter of high-level judgment.

One can empirically falsify an economic model—or at least quan-

tify its approximations and judge them excessive—but designing

the model itself requires higher forms of reason. Guidance and

principles can be provided, but no concise formal rulebook will

accomplish this exercise satisfactorily.

Yet while the model’s design remains a matter of general in-

telligence, a great deal of automation can still be accomplished.

The model’s evaluation and its use in decision-making can be fully

automated. The model must be revised as the company and the

market evolve; however, this process is usually slow compared

with the ﬂuctuations in the ﬂow of goods and materials. For most

companies, a relevant economic model lasts months if not years; a

ﬂow datum—such as a stock level—lasts hours.

4.5.2 Implicit valuations

Many companies sidestep the problem of economic calculation

for supply chain by resorting to percentage-based policies and

similar noneconomic methods. Those policies govern decision-

making in supply chain. They typically mix manual and automated

operations without explicitly adopting an economic perspective.

The sole redeeming quality of this approach is avoiding the overhead

associated with economic calculation itself. For the rest, no matter

how much success the company enjoys from factors independent

of its supply chain—such as superior branding—this approach can

4.5. VALUATION CONCERNS 117

no longer be considered anything but backward.

For a microbusiness—a single grocery, a family-run workshop,

a corner repair shop—the absence of formal valuations is not

backward; it is pragmatic. The owner’s scarcest resource is personal

time: every hour spent assembling a spreadsheet competes with

wiping down shelves, talking to customers, or simply keeping the

lights on. When two hours of arithmetic can at best trim a handful

of coins from next week’s purchase order, those hours deliver a

higher, safer return when invested in upkeep or direct selling. In

that setting, simple rules of thumb—“re-order when only two units

remain”, “never let the bread rack go empty”—approximate the

optimum well enough. Once volumes, products, and locations grow,

the arithmetic ﬂips: the cost of misallocation quickly outstrips

clerical overhead. Beyond that threshold, clinging to implicit

valuations is no longer frugality; it is self-sabotage.

With this caveat settled, let us return to the main thread of

our argument. Safety stocks, min/max reorder points, and time-

based buﬀers are archetypal noneconomic policies. Each spits out

a number—percentage, unit count, or “days of demand”—that

triggers an order, yet none carries a valuation expressed in coin.

They are calibrated to hit engineering targets—ﬁll rate, cycle time,

visual comfort—without ever asking what the next unit is worth.

Capital cost, margin variance, and opportunity cost are nowhere

in the formulas. A company can execute such policies ﬂawlessly

and still misallocate resources, because the yardstick they optimize

is disconnected from proﬁt and loss.

However much these policies ignore economic calculation, prof-

its and losses still occur; the calculation can be evaded—its conse-

quences cannot. For example, there is a common misconception

that percentages should be steered toward 100%, as with service

levels steering safety stocks. There are obvious diminishing returns

to increasing quantities held in stock. At some point, write-oﬀ costs

from excess inventory equal stockout costs; beyond that, further in-

creases in stock levels generate losses. When noneconomic policies

are used, there is no reason to believe their internal parameters

are close to the settings that would maximize returns.

118 CHAPTER 4. ECONOMICS

As a silver lining, those settings are unlikely to be catastrophic:

if they were, the company would have ceased operations. Survivor-

ship bias looms large: only companies that avoided bankruptcy

remain observable; the rest, by deﬁnition, are gone. Thus, to some

extent, a noneconomic policy that has kept a company solvent over

the years demonstrates some merit.

Conceptually, every noneconomic policy can be rewritten as

an economic one. Introduce a series of valuations chosen not for

their adequacy to real proﬁts and losses, but for their capacity to

numerically yield the same decisions. This exercise clariﬁes the

fundamental defect of noneconomic policies: they are economic

policies built on ﬂawed valuations.

Thus, any noneconomic policy can be improved by ﬁrst rewrit-

ing it explicitly as an economic policy, then bringing the valuations

closer to their real values. Such a roundabout course is mostly

of theoretical interest; in practice, there is rarely any justiﬁcation

for addressing the situation so convolutedly. It may occasionally

be warranted if the legacy transactional system is inﬂexible and

can operate only on safety stocks, for example. In that case, if

the company will not or cannot upgrade its system, it is forced

into the tedious process of making implicit valuations explicit and

readjusting them to more sensible values. This process involves

substantial reverse engineering, is prone to implementation errors,

inﬂates maintenance costs, and must remain an option of last

resort.

With noneconomic ﬂow policies now reframed as crude—often

faulty—valuations, two practical hurdles still stand between book-

keeping and actionable micro-decisions. Attribution asks: “Which

past coins belong to which past choices?”—it untangles the causal

threads that weave costs and revenues together. Projection then

asks: “Given those threads, what pattern of coins will tomorrow

most likely bring?”—it pushes the picture forward under uncer-

tainty. The next two subsections tackle these twin challenges in

that order.

4.5. VALUATION CONCERNS 119

4.5.3 Spending and revenue attribution

Attribution reconnects every coin that ﬂows through the ledger

to the operational choice that precipitated it. In the world of the

pure trader, the mapping is trivial: one purchase, one sale, one

gross margin. The moment a company stores, routes, transforms,

or promotes goods, this one-to-one symmetry vanishes. A single

inbound container replenishes hundreds of SKUs; one markdown

lifts a whole category; a missing gasket halts an entire assembly

line. Unless those braided eﬀects are disentangled, the “value” of

shipping one extra pallet or shaving a day oﬀ lead time is anyone’s

guess. Attribution therefore provides the quantitative causality

that turns raw ledger records into decision-ready data and makes

a microanalytical, option-by-option evaluation possible.

Let us consider the problem of establishing the per-unit pur-

chase price for a series of products that can all be acquired from

the same supplier. This per-unit purchase price is critical for

deciding whether units should be acquired from this supplier or

from a competitor. In our example, the supplier puts unit prices

on display; however, those tag prices come with strings attached.

Indeed, the supplier charges a ﬂat delivery fee, reﬂecting the cost

of dispatching a truck. The client company faces a trade-oﬀ: small

orders with low inventory costs and high delivery fees; large orders

with high inventory costs and low delivery fees. Whatever the size

and frequency of purchase orders, the supplier’s invoices include

a mix of item lines and delivery lines. The attribution problem

consists of deciding how those lines should be allocated to the

calculation of per-unit purchase prices.

For clarity, assume the client orders two products: one in

large volume and the other in small volume. In practice, the only

“economical” way to order the second product is to piggyback on

an order that includes many units of the ﬁrst product. The second

product “tags along” in the order stream with the ﬁrst one. In this

situation, it is tempting to allocate delivery fees to both products

in proportion to spend. Thus, nearly all the delivery fees would

be allocated to the ﬁrst product, leaving only a small portion for

120 CHAPTER 4. ECONOMICS

the second. Based on this attribution, both products will see their

“adjusted” purchase price inﬂated by a ﬁxed percentage. However,

this line of reasoning ignores the coupling of the two products. If

the company switches the ﬁrst product to a cheaper supplier, it

might end up worse oﬀ, because savings on the ﬁrst product are

oﬀset by delivery fees still incurred to order the second product.

Such a decision eﬀectively inﬂates the per-unit price of the second

product.

The attribution problem is not, however, restricted to spend-

ing; it also impacts revenues. For example, let us consider a store

oﬀering a substantial discount on a given product. Revenue for

this product spikes as more units are sold, albeit with a lower gross

margin; the promotional eﬀect does not stop there. Customers

also purchase complementary products, prompted by their initial

interest in the discounted product. Thus, revenues for several

complementary products rise as well. It may appear that these rev-

enues were generated at full price, but this perspective is misleading.

Without the discount on the “ﬂagship” product, those other prod-

ucts would not have been purchased to the same extent. Thus,

instead of naively assigning the entire revenue contraction—caused

by the discount—to the ﬁrst product, it is more correct to spread

this contraction across all complementary products in proportion

to their indirect promotion.

More generally, in supply chain, couplings are ubiquitous: items

are jointly purchased, transported, stored, transformed, and sold.

Each of these operations involves ﬁxed or shared costs. Conversely,

nearly all mechanisms generating revenues come with spillover

eﬀects: brand awareness and loyalty, impulse buying beyond the

original intent, etc. Attribution reorganizes spending and revenues

in ways more closely aligned with decision-making. As such, at-

tribution is one of the key mechanisms to perform the economic

calculations needed to identify the best options available to the

company. To a large degree, attribution deﬁnes the very structure

of the economic calculation.

Attribution cannot be mathematically proven correct; it can

only be shown incorrect if it conjures spending or revenues out of

4.5. VALUATION CONCERNS 121

thin air, or if the underlying arithmetic is faulty. However, those

issues are relatively trivial, and even ﬂawed attributions may still

meet such a basic standard of “correctness”.

In most countries, there exist a great many rules, usually of

the accounting variety, to perform such attributions. For example,

those rules may aim to prevent “selling at a loss”—according

to some semi-arbitrary deﬁnition of “losses”

. Those rules are

intended to fulﬁll taxation or regulatory purposes. Their validity

does not extend any further than their original goal. It would be a

grave mistake to recycle those rules for analytical purposes. Supply

chain must abide by regulatory requirements, but that is all.

Every attribution scheme comes with its own distortions. From

an informational perspective, attribution is a dimension-reduction

technique. Back to the high-volume/low-volume example above,

we have two item prices and one delivery fee. By attributing

the delivery fee to the products themselves, we project a three-

dimensional situation into a two-dimensional space. This projection

simpliﬁes the decision-making process, as each product gets a

shadow price reﬂecting the delivery fee. However, it may lead to

misguided decisions if those shadow prices misrepresent the original

situation. In general, folding dimensions cannot perfectly preserve

all properties of the original space.

Optimization techniques capable of operating in high dimen-

sions may alleviate, or even eliminate, the attribution problem.

For example, if we have a numerical recipe capable of directly

handling the six dimensions of a pair of suppliers competing for

the same two products (i.e., four per-unit prices and two delivery

fees), then no attribution is needed: the recipe embraces the higher

dimensionality of the problem.

Attribution belongs to economics rather than economic history,

in that it takes the accuracy and completeness of records as given. It

simpliﬁes the problem space—through dimension reduction—while

Those rules invariably neglect spending on research and development, hence

underestimating the break-even point, and they invariably neglect revenues from

complementary products or enhanced brand awareness, hence overestimating it.

In short, those rules are nonsense.

122 CHAPTER 4. ECONOMICS

maintaining a relevant economic model, i.e. one that reliably

represents spending and revenue baselines for valuing options.

Once those baselines are established, one must proceed to the

second mechanism: projection.

4.5.4 Projected spending and revenues

Economic history must not be confused with economics proper.

While past spending and revenues can be made certain—aside from

occasional clerical errors—future spending and revenues are always

uncertain. Their uncertainty is irreducible. No matter how recent

or numerous the transactional observations, they can never tell

the complete story of what will come to pass, because the future

depends on decisions that have yet to be made. Some of those

decisions will be made by the company itself; others will be made

by third parties—customers and suppliers, for example.

As supply chain options are picked for their assessed economic

returns, they ultimately rely on projected (and therefore inherently

imperfect) valuations. There are situations where valuation dis-

crepancies between the recent past and the near future are deemed

insigniﬁcant, but those situations are rare. The opposite dominates

supply chain. Let us brieﬂy illustrate this proposition.

When a retailer decides to add one more unit—tagged at one

coin—to the shelf, two complementary projections must be made.

First come the projected outﬂows. From the instant the item

enters the store until the day it leaves, it ties up capital and

absorbs carrying costs: rent for the ﬂoor space it occupies, wages

for staﬀ who move or count it, utilities, shrinkage, insurance, and

the opportunity cost of cash immobilized in the unit itself. These

expenses accrue day after day, and their expected value is strictly

positive; were they truly negligible, the rational strategy would be

to expand the sales area without bound—a reductio ad absurdum

that conﬁrms the costs are real and material.

Second come the projected inﬂows. The one-coin label is only a

candidate price: if demand lags, the unit may have to be discounted;

if it degrades, it may be written oﬀ; if competitors undercut, the

4.5. VALUATION CONCERNS 123

store may follow; and, in the opposite direction, a sudden demand

surge or general inﬂation can push the realized price above the tag.

Thus the expected cash recovered at sale is almost never exactly

one coin—most often lower, occasionally higher. Only after both

streams are discounted to present value can the retailer decide

whether stocking that extra unit raises or lowers long-run proﬁt.

More generally, the projection problem is as thorny as the

attribution one. While the example above is straightforward—the

measurement is merely delayed by one inventory cycle—many

concerns are more elusive empirically. Yet, those concerns can also

prove to be critically important. There is no correlation between

ease of measurement and criticality for the business.

Let us consider a fashion brand discounting at season’s end to

liquidate the current collection. There are two distinct forces at

play. First, the discount increases the sales volume, but at the

expense of the gross margin. Second, the discount trains customers

to expect future comparable discounts, eroding willingness to pay:

some will delay purchases until the sales periods. This erosion might

slow; yet, given enough time, its eﬀect on the brand’s revenues is

considerable. It may well dwarf the ﬁrst one for many, if not most,

brands. Yet, while quantifying such erosion is not impossible, it is

far from being straightforward.

Every valuation involves a projection—a numerical statement

concerning the future, a “forecast” not restricted to demand alone.

Historical spending, revenues, and their attributions provide the

baselines on which to compute these projections. However, histor-

ical baselines should not be equated with their projections. The

future is contingent on decisions that have not been made. Any

projection that appears accurate can become completely inaccurate

if subsequent decisions are revised. This problem is fundamental

and cannot be eliminated by statistical methods alone, however

sophisticated; it can be mitigated by methods that acknowledge

inherent uncertainty.

The economic performance of a supply chain decision largely

depends on the quality and reliability of its underlying projections.

While addressing this challenge typically involves a great deal of

124 CHAPTER 4. ECONOMICS

statistics, it is an error to treat it as a purely statistical problem.

Indeed, a projection must, ﬁrst and foremost, be assessed on

its economic relevance—a criterion more demanding than mere

statistical accuracy.

The remainder of this section zooms in on two special categories

of projections that deserve dedicated treatment. Shadow valuations

come ﬁrst: they are numbers that do not appear in any ledger

line yet must be conjured into existence to capture elusive but

economically decisive eﬀects. We then turn to private valuations,

the internal transfer prices applied to intermediate components

that never face the outside market yet still compete for cash, space,

and attention inside the ﬁrm. Both families follow the general

logic just established, yet each brings its own pitfalls and practical

techniques.

Shadow valuations

The economic impact of a decision is usually tallied by projecting

the cash spent today and the cash earned tomorrow. Yet many

second-order eﬀects—lost goodwill, supplier distrust, employee

fatigue—never hit the ledger, even though they later translate into

hard cash. Ignoring them yields a caricature of a short-sighted,

spreadsheet-driven accountant who “optimizes” this quarter while

quietly destroying next year. To escape that trap, practitioners

must introduce shadow valuations—explicit, money-denominated

estimates that render invisible consequences tangible. These ﬁgures

are assumptions—there is no historical baseline for them—but

without such adjustments, the calculation collapses into naive

cost-cutting that sacriﬁces long-term optionality and, ultimately,

proﬁt.

Consider a retail example: if a customer does not ﬁnd what he

expected in a store, his purchase is at least delayed—and may even

be canceled if he loses interest. He may instead visit a competitor,

ﬁnd the product, and eventually prefer that store. Such service

failures occur silently, leaving no trace in transaction records, yet

they have real consequences—both short- and long-term revenues

4.5. VALUATION CONCERNS 125

suﬀer.

If a retailer values its inventory solely on observed sales, it

overlooks how inadequate service erodes its customer base. In

other words, by undervaluing inventory—and setting aside shelf-

capacity constraints—the store ends up holding suboptimal stock.

Recognizing this, the company may introduce a “stockout penalty”,

a shadow valuation designed to capture the long-term cost of

lost sales. When incorporated into the economic calculation, this

penalty raises the perceived value of holding inventory, encouraging

the store to carry more.

More generally, the goodwill

that a company enjoys with

its customers, suppliers, and employees is best captured through

shadow valuations. Although speciﬁcs vary across organizations,

most supply chain decisions aﬀect goodwill. Therefore, incorpo-

rating shadow valuations better accounts for these broader conse-

quences when evaluating options.

For example, suppliers may be harmed by erratic purchasing—

ﬂuctuating order sizes and inconsistent frequencies. By smoothing

order ﬂow and making demand more predictable, companies en-

courage suppliers to lower their prices. In this context, a shadow

valuation that penalizes order variability steers purchasing behavior

in the desired direction.

Similarly, employees suﬀer under excessive workloads. For

instance, if a grocery store’s staﬀ receives more items in a day

than can be shelved during regular hours, they may be forced

into unplanned overtime. Moreover, inventory left scattered rather

than properly arranged frustrates both employees and customers.

A shadow valuation that penalizes excessive delivery volumes helps

smooth warehouse dispatches.

In practice, shadow valuations are often rough estimates—and

that is acceptable; being approximately right is preferable to being

exactly wrong. Overlooking elusive yet consequential factors is

In this context, “goodwill” refers to the common meaning—friendly, helpful,

or cooperative attitudes—rather than the accounting deﬁnition of an intangible

asset. The shadow valuations discussed here are inherently linked to decision-

making.

126 CHAPTER 4. ECONOMICS

misguided. This oversight leads to “idiot savant” solutions, which

may exhibit great technical sophistication while still missing the

point. Although we will later discuss how experimental optimiza-

tion oﬀers a more robust solution for valuations, for now informed

approximation is preferable to ignoring signiﬁcant factors.

Private valuations

A fundamental feature of the market economy is the establishment

of prices. For ﬁnished or “sellable” goods, companies must operate

within the constraints set by market prices. A company can

steer its prices downward—by innovations that slash costs—or

upward—through careful branding, as with luxury goods. However,

market prices always apply. Even a company with a patent-backed

monopoly must adjust its prices with regard to alternatives in the

market. Market prices do not require perfect substitutes.

In contrast, non-sellable goods do not directly beneﬁt from

market prices. Such goods are common in manufacturing, where

production often requires numerous intermediate components—

some assembled from subcomponents sourced externally. These

intermediates typically exist only within the company’s production

process and lack the complements (technical documentation, estab-

lished sales channels, marketing materials, compliance procedures)

that would render them sellable. However, not all intermediate

goods are non-sellable; for instance, a manufacturer may produce

both pumps and air-conditioning units, where the former is a part

of the latter yet is also sold independently.

Non-sellable goods require investments in both production

capacity and suﬃcient inventory. When a non-sellable component

is used exclusively in one ﬁnished product, its valuation is relatively

straightforward

—the cost of the ﬁnished product can be allocated

proportionally across its components. However, when a component

serves multiple ﬁnished products, valuing each additional unit in

Even for a component serving a single ﬁnished product, pitfalls remain:

the unavailability of a low-value part may jeopardize the ﬂow of a high-value

product.

4.5. VALUATION CONCERNS 127

stock becomes more challenging, as it depends on the probability

of its use across products.

For example, suppose a manufacturer uses a component in two

products sold in equal volumes: Product A yields a 10-coin gross

margin and Product B only 1 coin. The value of producing an extra

unit of this component depends on which product it supports. If

ample stock of Product A exists while Product B is out of stock, the

next unit is more valuable for Product B. Conversely, if Product A

is out of stock and Product B is well supplied, the additional unit is

more valuable for Product A, which oﬀers a higher margin. Thus,

the extra component’s value depends critically on downstream

inventory levels and product margins.

In practice, a manufacturer may handle numerous products and

components. Production cycles often diﬀer, and demand ﬂuctuates

in both volume and price. Each product typically maintains its own

stock, often distributed across multiple locations, and production

may occur in batches of varying size. These conditions complicate

the valuation of component stocks.

The rate of return of any investment related to non-sellable

goods—whether acquiring capital equipment or using input re-

sources to produce extra units—must be evaluated by its pro-

jected impact on the company’s revenue and expenses. Like the

attribution introduced previously, private valuations serve as a

dimension-reduction technique, summarizing complex, multidimen-

sional eﬀects into an expected return.

In larger, vertically integrated companies, private valuations

grow in prevalence and importance. In such ﬁrms, non-sellable

goods often outnumber ﬁnished products and account for a higher

share of inventory value. Consequently, valuing these assets be-

comes critical.

The role of the market

Private valuations are inherently diﬃcult. As companies grow

larger and more vertically integrated, their supply chain issues in-

creasingly resemble the resource-allocation problems governments

128 CHAPTER 4. ECONOMICS

face under socialism and its central planning. Indeed, socialism

has been implemented many times, in many places; as leading

economists predicted in the ﬁrst half of the 20

century, all those

attempts failed and invariably led to general impoverishment. So-

cialism, with its corollary of central planning, fails because it tries

to solve the problem of valuing scarce resources—a challenge that

only the free market can resolve at scale. Similarly, although com-

panies can perform accurate private valuations at small scale, large

ﬁrms often encounter signiﬁcant diseconomies of scale—ﬂawed

private valuations being a key factor—that limit growth.

In a free market, prices signal to economic actors how resources

should be allocated. When demand exceeds supply, prices rise,

prompting investments in the scarce product. Conversely, an

oversupply drives prices down, encouraging a shift toward more

proﬁtable opportunities. When a supply chain is relatively simple—

with few products, minimal intermediate goods, and short lead

times—translating market prices into resource-allocation decisions

is straightforward. In these cases, market prices themselves resolve

a critical piece of the supply chain challenge without the need for

deep insights or advanced analytics

As large companies expand their supply chains in depth and

complexity, the link between planning and market-price feedback

weakens, and challenges multiply. This issue was well known to

the entrepreneurs of the 19

century, but in his Testament of a

Furniture Dealer (1976), the founder of IKEA, Ingvar Kamprad,

puts it plainly, stating:

But do not forget that exaggerated planning is the most

common cause of corporate death.

Indeed, much like governments attempting socialism, large

companies have no alternative but to appoint a caste of experts—

The eﬀectiveness of the free market has long baﬄed intellectuals. The idea

that an unintelligent mechanism—ad hoc voluntary exchanges—can produce

better—smarter, even—solutions than those experts devise is profoundly sur-

prising. Despite the resounding successes of free markets over the last three

centuries, and the disasters of planned economies, most intellectuals remain

skeptical. See also The Anti-Capitalistic Mentality (1956) by Ludwig von Mises.

4.5. VALUATION CONCERNS 129

such as supply and demand planners, category managers, demand

forecasters, and inventory managers—to address the allocation of

resources. Although these roles diﬀer only slightly in scope, the

overall challenge is immense: companies must account for hundreds

of economic factors and establish thousands of private valuations,

all while facing inherent future uncertainties. Flawed valuations

beget unproﬁtable decisions.

As an organization scales, incentives shift: middle managers

increasingly prefer avoiding blame to chasing incremental gains,

and risk appetite shrinks with ﬁrm size. Consequently, they rely

on established processes, often adhering to them rigidly regard-

less of economic merit. When these processes repeatedly yield

unproﬁtable outcomes, the bureaucracy responds by adding more

rules, extending checklists, and incorporating additional workﬂow

steps. In essence, the bureaucracy expands regardless of economic

justiﬁcation.

Diseconomies of scale—not a shortage of ambition—usually put

the brakes on very large, vertically integrated ﬁrms. As organiza-

tional layers multiply, private valuations drift, and every new tier

magniﬁes the resulting misallocations. To regain agility, companies

typically shed peripheral activities—through outsourcing, spin-oﬀs,

or joint ventures—and redouble their eﬀorts on segments where

their comparative advantage is obvious and where external market

prices oﬀer a rapid, unambiguous feedback loop.

130 CHAPTER 4. ECONOMICS

Chapter 5

Information

Every supply-chain decision is a bet under uncertainty; to be any

good, it must be informed. Somewhat unexpectedly for the lay

reader, “informed” is not merely a metaphor: since the 1940s,

information theory has given the word a precise, quantitative

meaning. This chapter clariﬁes what “being informed” entails for

a modern supply chain.

Two facts motivate what follows. First, contemporary supply

chains are irreducibly digital objects. Their states and histories

live inside business systems; calling a supply chain “digital” is

redundant, because no sizable chain can operate without soft-

ware. Second, mainstream supply-chain theory routinely collapses

data into information: it treats the quantities it aims to decide

upon—demand, lead times, service levels—as directly observable.

In reality, the ﬁrm only holds crude traces (sales orders, ship-

ments, returns, stock corrections) from which those quantities

must be inferred. Ignoring this distinction is precisely why elegant

formulas so often fail when confronted with the raw exhaust of

enterprise systems. This mismatch between theory and practice

drives many failed initiatives: ﬁrms try to graft pre-computer ideas

onto post-computer infrastructures.

To navigate this landscape, we must separate three notions

that everyday language blends: data, information, and knowledge.

131

132 CHAPTER 5. INFORMATION

Data are the recorded symbols—bytes in tables, log lines, barcodes.

Information is the capacity of those symbols to resolve uncertainty,

a quantity Shannon taught us how to measure. Knowledge is the

causal structure that lets a mind—human or machine—turn infor-

mation into decisions that improve the ﬁrm’s returns. Confusing

these notions breeds naive recipes that look sophisticated on paper

yet underperform crude heuristics in production.

Digitalization has already displaced humans from the custody

of data; the same drift now erodes their custody of information

and knowledge. As volumes and refresh rates explode, the ar-

chitecture of the systems—not the personal cleverness of their

users—determines what the ﬁrm can or cannot decide well. Hence,

understanding where information truly lives inside the company

becomes strategic.

Since the 1970s, three software classes have structured that

“where”: systems of records (run workﬂows, store transactions),

systems of reports (describe what happened), and systems of intel-

ligence (decide what should happen next). Most corporate disap-

pointments come from conﬂating these roles—expecting ledgers to

think, or analytics layers to serve as sources of truth.

This chapter proceeds in stages: we ﬁrst disentangle data,

information, and knowledge, anchoring “information” in Shannon’s

theory. We then examine how these notions appear—and are

routinely distorted—inside the three dominant enterprise

software

classes. With that frame, we confront the messy semantics of

business data, the folklore of “bad data”, and the incentives that

bend records away from the ﬁrm’s interest. Finally, we revisit the

tacit, so

called mundane knowledge that still sits in people’s heads

and show how it, too, can be progressively codiﬁed. This sets the

stage for the subsequent chapter on intelligence and for the full

mechanization of decision-making.

5.1. DATA, INFORMATION, AND KNOWLEDGE 133

5.1 Data, information, and knowledge

Data, information, and knowledge are three words that presenta-

tion slides and vendor brochures happily interchange; in practice,

confusing them wrecks architectures and policies. Treating raw

records (data) as if they already resolved uncertainty (informa-

tion), or treating reduced uncertainty as if it already carried a

causal model that can steer action (knowledge), is not a minor

semantic slip. It is the root cause of dashboards that never decide,

“planning” modules that never plan, and post

mortems that keep

blaming “bad data” for failures that are, in fact, methodological.

Data are symbols captured by systems—bytes in tables, log

lines, barcodes; by themselves, they say nothing about how un-

certain we remain. Information is what those symbols buy us

against uncertainty; it can be quantiﬁed and budgeted. Knowl-

edge is the structure—explicit or tacit—that lets an intelligence

turn information into decisions that systematically improve the

ﬁrm’s returns. Collapsing these layers invites two symmetric errors.

First, ﬁrms hoard ever more data and compute, convinced that

volume substitutes for information; they end up with petabytes of

low-grade exhaust that barely moves the needle on the uncertainty

that matters. Second, they mistake information for knowledge

and expect reports or KPIs to “drive” the business; people then

compensate manually, injecting ad

hoc rules and folklore that never

get formalized, versioned, nor tested.

For a modern supply chain, the distinctions are operational.

If you cannot say where information sits, you cannot say which

system is authoritative; if you cannot say where knowledge sits,

you cannot say who—or what—is accountable for the decisions.

Once we look through this lens, “systems of records”, “systems of

reports”, and “systems of intelligence” become essential architec-

tural constraints: records hold (most of) the data, reports expose

fragments of information, intelligence operationalizes knowledge

to issue orders. Expecting any one of these layers to do the job of

the others is wishful thinking—and a reliable way to burn both

budgets and credibility.

134 CHAPTER 5. INFORMATION

What follows, therefore, does not rehash deﬁnitions for the sake

of taxonomy. We start from the mechanical nature of data—mere

symbols—and the engineering realities that govern their storage

and retrieval. We then anchor “information” in Shannon’s quan-

titative treatment of uncertainty, showing why many seemingly

“rich” datasets carry little informational weight for the decisions

at hand. Finally, we turn to knowledge, the least tractable of

the three, and tie it back to the company’s code, recipes, and

people—where it actually lives, how it should be curated, and how

it can be progressively mechanized. Only by keeping these three

tiers distinct can a ﬁrm hope to build decision systems that are

both informed and reliable.

5.1.1 Data storage

Data are recorded symbols. They can be numbers, characters,

pixels, or any other discrete marks that a system can reliably store

and retrieve. On their own, these symbols carry no meaning; only

an agreed

upon interpretation (a schema, a codebook, a protocol)

turns them into something a ﬁrm can reason about. A barcode, a

customer identiﬁer, a purchase order line, or a temperature probe

reading are all “data” in this strict, mechanical sense.

On computers, these symbols are ultimately encoded as binary

digits and grouped into bytes (8 binary digits per byte)

. Modern

storage devices, weighing just a few grams

, can hold over 1 TB,

or 10

bytes. A terabyte already lies beyond intuitive grasp. The

1768–1880 edition of Encyclopaedia Britannica spans 143 volumes,

with its text content totaling 336 MB—about 3,000 times smaller

than 1 TB. Even with high

resolution images, the encyclopedia

totals just 23.5 GB, or roughly 400 times smaller than 1 TB. With

eﬃcient representations, 1 TB can store over a decade of retail

These binary digits are commonly called bits. We will later reuse the word

“bit” in Shannon’s information

theoretic sense; context will make it clear whether

we mean a binary digit or a unit of information.

As of August 2024, some 1 TB solid-state drives weigh about 8 grams and

sell for roughly the cost of four Paris haircuts.

5.1. DATA, INFORMATION, AND KNOWLEDGE 135

transactions for a large network.

From a supply chain perspective, large data storage capacities

enable the collection of extensive electronic records. These records

capture both the current state of the supply chain and the detailed

sequence of events that led to it, including the ﬂow of physical

goods. They can also include market data, such as competitor

pricing. All of these data are stored in the company’s systems of

records, which will be discussed further.

The storage capacity of modern hardware is so vast that the

numbers are hard to internalize—and this gap will only widen

as technology progresses. Modern digital stacks routinely span

10 to 15 orders of magnitude: from nanosecond CPU cycles to

day

long batch jobs, from bytes on embedded devices to petabytes

in data warehouses. By contrast, the span between a one

person

shop and the largest ﬁrms on Earth is “only” about seven orders of

magnitude (1 to 10 million employees). The quantities are perfectly

measurable; what is diﬃcult is building an intuition for phenomena

that diﬀer by factors of a trillion or more.

As capacities rise, retrieving and processing what we store also

become harder. Yet market pressure generally pushes the rest

of the stack—bandwidth, RAM, CPU—forward enough to keep

the extra storage economically usable. When storage outpaces its

complements for too long, investment in ever

denser media simply

stalls until the rest of the hardware catches up, at which point

further gains again make business sense.

A gram

sized storage chip now holds more bits than a single

mind—or a thousand of them—could ever keep in working memory.

This sheer capacity is why, decades ago, business systems became

the de facto custodians of almost all relevant supply-chain informa-

tion. Consequently, a supply

chain decision

making process is truly

informed only if it taps into the vast troves held by the company’s

systems of records.

The vastness of modern data storage comes with two distinct

pitfalls. Supply chain practitioners who are not well-versed in

information technology often underestimate how much data can

be stored and processed for supply chain purposes. As a result,

136 CHAPTER 5. INFORMATION

vital data are left unrecorded, deleted too soon, or never collected.

If these data were available, they could have been used to further

reﬁne decision-making processes. The two most common mistakes

are failing to preserve all events aﬀecting the business systems and

discarding historical data after three to ﬁve years, even though the

entire history could be preserved almost indeﬁnitely

For example, while all inventory management systems include

manual stock-level overrides to correct discrepancies between phys-

ical inventory and electronic records, some do not record those

overrides. Yet the full trail of overrides is essential for tracing the

root causes of inventory errors. Ideally, every event that ends up

modifying the state of the business system should be recorded

Conversely, software engineers, who are well aware of computing

hardware capabilities, often take advantage of this by favoring easy-

to-implement solutions over resource-eﬃcient ones. As a result,

software often becomes ineﬃcient in its use of hardware, especially

in enterprise software compared to consumer applications. In

consumer software, the buyer and user are typically the same

person, and the app’s responsiveness is a key factor in its adoption

Engineers therefore spend much of their eﬀort squeezing every cycle

out of the app.

By contrast, the buyer of enterprise software is seldom the

user, and demos usually run on mock datasets far smaller than

Some countries regulate how long personal data can be stored. Unless a

supply chain software vendor mishandles personal data within its system, these

regulations generally have little impact on supply chain operations. Personal

data oﬀers limited value for optimizing the ﬂow of goods and is more of a liability

than an asset in this context.

Event sourcing requires all events aﬀecting the system to be stored in an

event store, replacing the more common relational database. Unlike relational

databases, where unlogged state changes can occur, event sourcing ensures by

design that every change is logged, capturing all information that led to the

system’s current state.

Since mobile devices far outnumber other consumer computing devices,

making apps resource-eﬃcient also helps preserve battery life, a highly valued

feature for users. While storing more data doesn’t inherently consume more

energy, the more data that must be stored, retrieved, and processed, the more

energy the device will use.

5.1. DATA, INFORMATION, AND KNOWLEDGE 137

those in production, so real

world performance gets little scrutiny.

Moreover, despite ineﬃcient use of computing resources, the total

cost of ownership of the app usually dwarfs its hardware costs. As

a result, when facing a performance issue, it is common to throw

more hardware at the problem instead of improving the underlying

eﬃciency of the software. More generally, most enterprise software

vendors have only modest incentives to invest in making their

technology resource-eﬃcient.

Resource eﬃciency matters because the very same piece of

information can be encoded in many ways, some far leaner than

others. While a 1 GB (10

bytes) dataset may seem richer and

more informative than a 1 KB (10

bytes) dataset, more bytes do

not inherently mean more information. For example, the number

123 can be stored as a single byte or as a million bytes with a

million leading zeroes.

Using a million bytes to represent a small number is an obvi-

ous misuse of computing resources, yet enterprise software often

creates redundant copies of the same data in its databases. In

relational databases, enterprise software typically inﬂates data

storage by a factor of ten—and sometimes a hundred—due to

redundant data representations. Various technical reasons justify

these redundant data representations. Those reasons lie outside

our scope here, but they raise an important question: how does

the chosen representation relate to the information itself?

5.1.2 Information theory

Company records can be stored as data. Yet while data are conven-

tionally measured in bytes, that unit does not indicate how much

information they contain. The same record admits many represen-

tations, some more storage-eﬃcient than others. The underlying

information is invariant to its notation. For example, 1776 equals

MDCCLXXVI in Roman numerals; the information is unchanged.

Information theory studies what information is—and how it is

measured, stored, and transmitted.

Claude Shannon founded modern information theory in the

138 CHAPTER 5. INFORMATION

1940s. It is a foundational auxiliary science of supply chain. This

theory is grounded in probability and deﬁnes information as the

capacity to resolve uncertainty. To illustrate the link between

uncertainty and information, consider two stores, each stocking

100 products. We focus on stock levels—100 numbers in total—and

compare the information involved in managing two very diﬀerent

stores.

First, a luxury watch store. For each watch in the assortment,

at most one unit is kept. When a watch sells, a replacement is

promptly shipped to keep the assortment complete. Second, a

suburban depot for bulk construction materials. Replenishments

are infrequent and involve large batches, typically a full truckload.

In the watch store, a diligent manager likely knows the exact

inventory at all times. Typically, the stock level is 1 for all watches;

for those just sold, it temporarily drops to 0 until replenishment.

Because most watches are at 1 and only a few brieﬂy at 0, the

manager can easily recall levels. In contrast, stock levels at the

suburban depot range from 0 to 10,000 units. A manager might

recall which products are out of stock or recently replenished, but

even a diligent one cannot memorize the exact levels across all 100

products. Both stores track 100 items, yet the watch shop carries

far less information than the depot.

Information theory makes this contrast precise. The notion of

entropy quantiﬁes information; it is measured in bits (sometimes

“Shannons”). Returning to the example suﬃces to illustrate entropy.

Assume the watch store has a 95% service level: each watch is

out of stock with probability 5%, otherwise at 1. Assume further

that the suburban store’s stock levels are uniformly distributed

between 0 and 9999. Although a uniform distribution is unrealistic,

it suﬃces for this illustration.

The entropy will be approximately 29 bits for the luxury watch

store and 1328 bits for the suburban depot (the detailed calculation

appears in the annex). The suburban depot’s entropy is nearly

that of the watch store, which is why even a diligent manager

cannot mentally track its inventory.

From a supply chain perspective, information theory clariﬁes

5.1. DATA, INFORMATION, AND KNOWLEDGE 139

what complex means in a supply-chain system. A supply chain

resists succinct description: millions of bits are needed to represent

even an average one, and billions for a large one. This vast re-

quirement is not an artifact of representation; it is a fundamental

property of supply chain. The information required to resolve the

state of a supply chain far exceeds what any group of people could

memorize, let alone track its transitions over years.

Thus, generating informed supply-chain decisions daily requires

millions of bits as input to reﬂect state, and millions more as

output to represent the decisions. Hence software is fundamental

to modern supply chains: without it, operating them would be

impossible. Before business systems spread in the early 1970s,

supply chains were far less complex than today. This surge in

complexity stems directly from advances in computing hardware.

Companies now manage more SKUs, suppliers, sites, price schemes,

and channels than ever, leveraging modern computing hardware

to handle this increased complexity.

Although supply chains have grown more complex, many sources

of accidental complexity have been tamed. For physical goods,

standardization, strict tolerances, and formal quality control be-

came increasingly common in the ﬁrst half of the 20

century.

The digital turn of the 1970s pushed standardization even further.

The modern concept of SKU (stock-keeping unit) assumes that all

units in stock are physically identical.

The content of an SKU

can be summarized by a single number: the stock on hand.

Modern readers may ﬁnd it hard to believe that even mass-

produced items in the 19

century varied markedly from unit to

unit. In such supply chains, an inventory management system—had

it existed—would have needed to track details for every unit to

reﬂect inventory accurately. In practice the problem was handled

diﬀerently: salesmen discounted sub-par units on the spot.

Information theory provides fundamental insights for supply

Perishable goods, which have expiration dates, and serialized inventory,

where each item has a unique serial number, are two notable exceptions to the

usual SKU perspective. In these cases, stock on hand alone doesn’t adequately

represent the inventory, so a more complex model is needed.

140 CHAPTER 5. INFORMATION

chain, especially when judging whether a data source can sharpen

decisions.

For example, this perspective clariﬁes why ﬁne-grained manual

overrides—such as adjusting forecasts or stocking policies—are

futile. If demand planners rely on historical data to make those

corrections, they add no bits of information to the system. They

merely palliate defects in the existing numerical recipes—built on

that very same data. Thus, for such a manual process to make the

system more informed, it must introduce “fresh” bits of information.

Suppose a number based on intuition or prior knowledge carries

fewer than 10 bits of information. Even if an employee supplies 100

meaningful numbers daily, this totals only 1000 bits—assuming,

unrealistically, complete independence. Adding 1000 bits to a

system that processes millions is unlikely to make it materially more

informed. While an employee may occasionally supply information

with outsized impact, this raises questions about the source of such

insights and the odds of repeating them consistently.

More generally, the informational lens helps assess how far

a data source can inform supply-chain decisions. As the earlier

example showed, processing data adds no information—it can only

uncover what was already there in the original data. It also clariﬁes

that data volume may not correlate with information content.

Yet turning information into informed decisions still requires

one more ingredient: knowledge.

5.1.3 Knowledge

Knowledge is the theoretical and practical understanding of an

object. It encompasses the insights, rules, and principles that

shape a mind’s causal model of the object

. This causal model lets

a mind deduce why an object is as it is and where it is headed.

By mind, we refer to an entity capable of general intelligence, putting aside

all the concerns of consciousness and soul which have no bearing on supply

chains. Such entities were once exclusively the province of man; however, recent

advances in software and hardware are increasingly blurring that line.

5.1. DATA, INFORMATION, AND KNOWLEDGE 141

Knowledge turns observations into actions that steer the object

toward a better state.

For supply-chain purposes, we draw on only a tiny fraction of

mankind’s knowledge. Our concern is solely with supply chains; the

scope of knowledge is limited to this purpose. Knowledge counts

as supply

chain knowledge only when it improves the company’s

decisions. Supply-chain knowledge, in the broadest sense, spans a

wide range of subjects. However, as the earlier Chapter Epistemol-

ogy explained, we must distinguish between the core supply-chain

domain and its auxiliary sciences, as their ways of organizing and

fostering knowledge diﬀer markedly.

Unlike information—fully characterizable mathematically and

an unintelligent mechanical aﬀair whose storage medium is irrel-

evant—knowledge is elusive and inseparable from the mind that

produces it.

Give two students the same physics manual: one may extract

insights far beyond its examples, while the other may never connect

examples to principles. What a reader gains is subjective: a book’s

knowledge remains latent until someone reads it. Until then, it

is merely a data storage device. The book’s content remains the

same regardless of paper quality—or even of being printed at all;

the electronic copy is identical in content.

Because knowledge and general intelligence are intertwined,

fully understanding one requires fully understanding the other.

Deﬁning one without the other is inevitably incomplete. Gen-

eral intelligence remains a subject of active research. Progress

in this area has accelerated over the past seven decades, yielding

remarkable results. Yet we have also realized how much we under-

estimated our ignorance on this subject. We will revisit this topic

in Chapter Intelligence.

Equally important is the locus of knowledge—where it actually

lives inside the company—a crucial question when applying knowl-

edge for supply chain purposes. Two common but simplistic views

miss the mark. The ﬁrst and most common misconception is that

knowledge resides exclusively with experts and their supporting

materials—papers, books, and courses. Academia often feeds this

142 CHAPTER 5. INFORMATION

view; many professors see themselves as the pinnacle of expertise.

The second is the notion that knowledge is spread evenly across

the company. This belief often stems from a misguided desire to

involve all employees in the company’s supply chain eﬀorts. To

clarify the locus of knowledge, we must further reﬁne what we

mean by knowledge.

In his landmark 1945 essay

, The Use of Knowledge in Society,

Friedrich Hayek separates ‘special’ from ‘mundane’ knowledge.

Special knowledge covers textbooks, formulas, source code, policies,

and regulations. The fundamental error, he argues, is to believe

that special knowledge is the only type.

Hayek adds a second, crucial kind—mundane knowledge. Mun-

dane knowledge covers the trivia bound by the circumstances of

a given time and place. A truck driver, for instance, knows the

healthy hum of an engine and books repairs before trouble starts.

This knowledge is sharpened when the driver regularly operates

the same truck, developing an understanding of its unique quirks

over time. This knowledge is informal and undocumented, yet

often essential for getting things done reliably.

Thus, supply chain mixes special knowledge, held by a small

body, and mundane knowledge, held by a much larger one. In

either case, the holders of that knowledge may be employees—or

not. For example, bits of special knowledge can be delegated to

an enterprise software vendor, and bits of mundane knowledge to

a third-party repair company.

For a century, ﬁrms have worked to replace mundane know

how

with codiﬁed rules. Mundane knowledge still matters—what you

don’t know can hurt you—but its informality creates friction for

large ﬁrms. Large companies aim to make their processes as

replicable as possible to reduce accidental variance in quality, cost,

or delays in their physical-goods ﬂow. From this perspective,

mundane knowledge hinders the standardization of processes.

This 13-page essay is very well written and largely accessible to a general

audience without any particular prerequisite. Free copies can easily be found

online; we strongly recommend this essay to the curious reader.

5.1. DATA, INFORMATION, AND KNOWLEDGE 143

The rise of enterprise software over the last ﬁve decades has am-

pliﬁed this trend: software embodies special knowledge and accel-

erates the shift. Software mechanizes the application of knowledge,

yielding signiﬁcant productivity gains when successfully deployed.

However, software is expensive to set up and even more costly to

maintain, especially in complex enterprise settings. Codifying a

process yields two gains: easier replication and tighter software

control.

In a modern supply chain, the source code that implements

the ﬁrm’s numerical recipes—together with its supporting artifacts

(tests, notebooks, conﬁguration, documentation)—is the company’s

most valuable special knowledge asset. Unlike slideware or tribal

lore, this asset is actionable: it turns information into decisions

without waiting for a meeting. Crucially, this codebase is not

a static library of formulas; it is a living corpus that must be

curated, versioned, and continuously challenged against proﬁt-and-

loss feedback.

Because the codebase is information, it can itself be analyzed

by software. Static analysis, type systems, linters, proﬁlers, for-

mal tests, and simulation harnesses make the knowledge both

inspectable and improvable at machine speed. Conversely, because

this knowledge is inseparable from the minds that evolve it, the

engineers who maintain and extend the codebase are part of the

asset: without stewardship, rot sets in, semantics drift, and the

ﬁrm quietly slides back toward folklore.

This observation closes the loop. Data are symbols. Infor-

mation is their power to shrink uncertainty. Knowledge is the

causal structure—captured increasingly as code—that systemati-

cally converts that reduced uncertainty into superior allocations of

scarce resources. For supply chain, “owning the knowledge” means

owning and operating the code that embodies it.

144 CHAPTER 5. INFORMATION

5.2 Classes of enterprise software

Most of the information a modern supply chain needs resides in

enterprise software. Yet these applications play distinct epistemic

roles. The previous pages separated data, information, and knowl-

edge; this section mirrors that tripartition in software. What the

ﬁrm records (data), reports (information), and decides (operational-

ized knowledge) live in three distinct classes of systems. Confusing

them—asking ledgers to think or dashboards to be sources of

truth—is among the costliest mistakes companies make.

Since the 1970s, three classes have dominated enterprise stacks:

systems of records, systems of reports, and systems of intelligence.

Dozens of specialized tools exist (e.g., CAD for engineering, virtual-

ization for IT), but in budget and supply-chain relevance they are

dwarfed by these three. What follows names each class precisely,

explains why their responsibilities must remain disjoint, and shows

how mixing them corrupts performance and accountability.

Deﬁnition (System of records).

Software that embodies operational workﬂows and cap-

tures every transaction and state change, acting as the ﬁrm’s

authoritative ledger.

Its primary virtues are reliability, traceability, and consistency.

“Intelligence” is neither required nor desired. A system of records

digitizes and streamlines tasks otherwise done with pen and pa-

per. Almost all transactional systems—ERP, CRM, WMS, PLM,

MRP—belong to this class. Architecturally, they are CRUD-ﬁrst

(create, read, update, delete) and backed by a relational database.

Concurrency control, auditability, and strong consistency dominate

the design. Heavy analytics and optimization do not belong here:

they starve shared resources, degrade the user experience, and

muddy the accountability ledgers must preserve.

5.2. CLASSES OF ENTERPRISE SOFTWARE 145

Deﬁnition (System of reports).

Software that re-projects what the system of records al-

ready contains—through aggregation, reconciliation, and vi-

sualization—so humans can inspect, audit, and narrate what

happened.

It adds no information; it merely reshapes what exists. Systems

of reports automate what clerks and analysts once did by hand:

totals, averages, pivots, dashboards. They sit on top of systems

of records and are intrinsically descriptive. For modern supply-

chain practice, their usefulness is limited: they remain helpful

for compliance, forensics, and management oversight, but not as

inputs to optimization engines. Running decision-making on a

reporting layer introduces information loss, semantic drift, and

extra bias for no economic gain.

Deﬁnition (System of intelligence).

Software that transforms the ﬁrm’s records (and a few

well-chosen external feeds) into unattended, auditable deci-

sions—and writes those decisions back to the system of records.

It does so through a narrow API and exposes logs precise

enough to be challenged against proﬁt-and-loss feedback. It owns

almost no master data and very little user interface. A system

of intelligence replaces the manual, spreadsheet

mediated steps

that typically sit between reports and execution. It consumes

authoritative data, computes options, arbitrates them economically,

and commits orders, allocations, prices, or schedules to the ledger.

Its engineering culture, runtime proﬁle, and accountability model

diﬀer fundamentally from those of systems of records and reports.

This class will be examined in greater depth in Chapter Decisions.

Inside a ﬁrm, systems of records come ﬁrst, reports second,

intelligence last. Systems of reports and systems of intelligence

are intended to be overlaid on systems of records—possibly aided

146 CHAPTER 5. INFORMATION

by data lakes or warehouses. Companies often err by blending

the classes, expecting a single product to deliver value beyond

its natural boundaries; those attempts invariably fail. Systems of

records make terrible systems of intelligence, and vice-versa.

The second mistake is attempting to run a system of intelligence

on top of a system of reports. A system of reports is a secondary

source of information. The original data are invariably altered

to make it more amenable to reporting. These transformations

not only cause information loss but also introduce complications

and bias. While those transformations are justiﬁed—within the

context of the system of reports—the data no longer accurately

reﬂect what actually happened.

For example, weekly sales reported by business intelligence may

be net of returns. For items often sent back, those ﬁgures can

understate the inventory required to maintain a given service level.

The idea of running a system of intelligence on top of a system

of reports is tempting because, to human eyes, the data as presented

by the system of reports looks clearer and better organized than

its original counterpart in the system of records. Thus, it feels

as if this would help the system of intelligence. Yet this is false.

The information perspective explains why: the system of reports

not only contains less information but also adds its own layer of

complications.

A clear grasp of these three software classes is essential for

modern supply-chain practice to take root in a company.

5.2.1 The place of software

Supply chain is now software-bounded: what the ﬁrm can observe,

remember, and ultimately decide is constrained less by forklifts and

bays than by schemas, APIs, and runtimes. Misplacing software

in the overall architecture is not a pedantic IT concern; it is an

economic error that quietly taxes every decision the company takes.

If the wrong system is asked to think, or the right one is starved

of the data it needs, optionality shrinks, variability bites harder,

and working capital gets trapped for reasons that never show on a

5.2. CLASSES OF ENTERPRISE SOFTWARE 147

P&L.

The previous pages separated data, information, and knowledge,

and mapped them to three software classes: systems of records,

systems of reports, and systems of intelligence. This section clariﬁes

why that separation must be enforced in practice, not merely

acknowledged on slides. Before examining how most writers get

this wrong, we must make explicit what is at stake: authority over

the facts (records), over their narration (reports), and over the

levers that actually move cash and atoms (intelligence). Confusing

these roles scrambles accountability, bloats costs through resource

contention, and ensures that “bad data” will be blamed for what

is, in truth, a design failure.

With this frame in mind, we can look at how supply-chain

discourse routinely miscasts software—either as an irrelevant detail

that magically provides whatever numbers a formula demands, or

as a glossy org chart of interchangeable modules that supposedly

add up to “best practice”.

Supply

chain writers typically fall into two camps. Those

with an academic bent treat enterprise software as an abstraction,

assuming that ﬁgures such as sales or stock levels are simply “there”.

Where the numbers come from is seldom discussed; obtaining them

is treated as casually as a contractor choosing a shovel brand. To

widen their scope, they oﬀer a menu of models—some that rely

on, others that sidestep, inputs such as stock-out records—leaving

ﬁrms to pick whichever ﬁts the data they actually hold.

Writers from consulting ﬁrms lean on fashionable buzzwords,

unfurling ornate diagrams and maturity matrices that treat soft-

ware modules as puzzle pieces. They prescribe phased “adoption

journeys”, but the stages seldom get more than slide-deck gloss.

Executive sound

bites abound; hard technical substance is scarce.

Both viewpoints fall short of a rigorous supply

chain treatment.

Treating “sales data” or “stock data” as self-evident betrays igno-

rance of enterprise software realities; describing tools without their

underlying mechanics or markets is no better. Too often the result

is little more than a sales pitch in scholarly dress.

Software is an indispensable auxiliary science of supply chain,

148 CHAPTER 5. INFORMATION

and its mastery cannot be shunted to specialists—no more than a

modern physician can outsource chemistry. A ﬁrm must understand

its own software terrain to run its supply chain well.

5.3 Systems of records

Systems of records are a company’s ledgers: they embody oper-

ational workﬂows, capture every transaction and state change,

and, by construction, become the authoritative memory of goods

in motion. In modern supply chains, they are the only scalable

custodian of information: what the ﬁrm can observe and remember

sits there—not in people’s heads.

This section treats systems of records as they are: indispensable,

yet epistemically and technically bounded. They are engineered

for reliability, traceability, and concurrency control. They are not

built to report, let alone decide. Confusing them with systems of

reports or systems of intelligence is among the most common—and

most expensive—corporate mistakes.

We proceed in ﬁve steps: ﬁrst, we recall the historical path that

produced today’s ERPs, CRMs, WMSs, and their cousins. Second,

we dissect their CRUD design and the ensuing commoditization.

Third, we examine the technological trajectory that makes every

system of records swell over time, creating functional overlaps and

synchronization nightmares. Fourth, we state the hard functional

limits that preclude pushing heavy analytics or optimization inside

the ledger. Fifth, we close with the vendor’s conundrum—the

structural incentive to market ledgers as “intelligent” platforms

despite their intrinsic bounds.

With this frame in mind, the rest of the section can be read as

a ﬁeld guide: how to exploit systems of records ruthlessly for what

they do well, and how to keep them out of the decision seat they

were never designed to occupy.

5.3. SYSTEMS OF RECORDS 149

5.3.1 The origin of enterprise software

Systems of records appeared in the late 1970s and spread rapidly

through the 1980s and 1990s. Their role was to convert paper-

heavy clerical work—especially accounting and stock keeping—into

electronic workﬂows. By capturing every transaction and stock

movement, they eliminated whole classes of clerical error and, once

barcode scanners arrived, lifted productivity. By the late 1990s,

most large ﬁrms had deployed several such systems to automate

their routine back-oﬃce tasks.

Falling hardware costs underpinned this rise. During 1970–2000,

Moore’s Law held: transistor counts doubled roughly every two

years while prices barely moved. Memory, storage, bandwidth,

and I/O followed similar curves, so by the late 1990s storing

transactions digitally was orders of magnitude cheaper than paper

alternatives.

Amid the ﬂood of record

keeping products, two labels came to

dominate: ERP (enterprise resource planning) and CRM (customer

relationship management). Strip away the marketing—both are

the same animal: a CRUD ledger wrapped in workﬂows. The only

real diﬀerence is the entry point—ERP is organized around the

product (and its bills of materials, routings, inventories), CRM

around the customer (and the sales pipeline). Once a vendor

secures a foothold, both families grow the same way: bolt on

adjacent entities, bundle ever more modules, and acquire niche

rivals. By the late 1990s, most ERPs and CRMs had swollen into

sprawling monoliths, setting the stage for the technical problems

discussed next.

One root cause was technical: packet-switched networking

was conceived in the 1960s, but true distributed business systems

stayed niche until the late 1990s. Firms therefore ran centralized

mainframes, and cross

vendor integration was either ruinous or

impossible. Expanding the resident system was cheaper than wiring

in a new one.

But building and running ever

larger code bases is expensive.

During the 2000s commodity integration tools—open

source ETLs

150 CHAPTER 5. INFORMATION

and web APIs—made it cheaper to stitch systems together, so

specialized record

keeping apps resurfaced and clawed work back

from the monoliths.

Yet the old monoliths never shrank, and the new niche tools

soon ballooned too: every system of records keeps growing along

the same lines. Hence the alphabet soup of WMS, OMS, PIM,

PLM, SRM, and countless cousins—diﬀerent starting points; much

the same architecture and growth path.

In supply-chain work, the ERP monolith is typically the prime

data source because it holds the purchase and sales ledgers. The

label Enterprise Resource Planning is a marketing artifact: an

ERP does not plan in the economic sense of arbitrating options

under uncertainty; it executes and records resource workﬂows (or-

ders, inventories, invoices) and, at best, sequences clerical steps

(approvals, calendarized MRP explosions, status ﬂips) according

to user

entered parameters.

Calling this “planning” conﬂates

bookkeeping with decision-making and fuels the recurring myth

that a system of records can double as a system of intelligence.

5.3.2 The CRUD design

Nearly every current record system follows the CRUD (create, read,

update, delete) pattern born with relational databases in the late

1970s

. Front

ends have evolved from green screens to glossy web

apps, yet back

end principles have scarcely budged. Tooling, by

Classical MRP explosions, MOQ/EOQ “optimizations”, and time

bucketed

reorder cycles embedded in ERPs are deterministic, parameter

driven recipes.

They neither price optionality nor balance opportunity costs across the ﬁrm;

hence they do not qualify as planning under the deﬁnition advanced in this

book.

A more recent architectural pattern, event sourcing, records every state

change as an immutable stream of events instead of persisting only the latest

state. Event

sourced systems deliver superior traceability, replayability, and au-

ditability, yet the pattern has been fully commoditized since the mid

2010s: main-

stream databases, frameworks, and cloud services all provide oﬀ

the

shelf sup-

port. Consequently, every conclusion drawn in this subsection for CRUD—about

commoditization, resource contention, and cultural mismatch with systems of

intelligence—applies verbatim to event-sourced systems as well.

5.3. SYSTEMS OF RECORDS 151

contrast, has advanced so far that, since the mid-2000s, systems of

records have been little more than commodities.

In practice “the database” means a relational (SQL) engine:

a set of tables, each with typed columns (numbers, text, dates,

. . . ) and rows of data. Other designs exist but matter far less

in enterprise software. Relational databases link tables through

primary

and foreign

key pairs. An

Orders

row, for instance,

can carry a

ProductID

that points to the matching

ProductID

Products

; the engine forbids an order that references a product

that does not exist.

Vendor dialects diﬀer, but the SQL

92 (published in 1992) core

works everywhere, and most CRUD apps rarely need more than

that despite later, fancier extensions. The ﬁne print of SQL is

beyond the present scope; however, a basic level of SQL should be

considered part of the foundational knowledge needed by a modern

supply chain practitioner.

CRUD design begins by deﬁning entities—groups of tables

mirroring the business concepts the app manages. Those concepts

can be products, clients, inventories, suppliers, contracts, employ-

ees, payments, etc. Moreover, most business concepts do not ﬁt

within a single table. For example, a client order may require

one table

Orders

with one transaction per line, and another table

OrderDetails listing all items included in each order.

Each entity gets screens through which users create, read, up-

date, or delete its records, following the database’s underlying

paradigm. Those screens are frequently accompanied by bits of

business logic to automate database changes—mechanical conse-

quences of the user’s entries—and typically follow a workﬂow of

some sort.

A CRUD app’s complexity almost always creeps upward: new

entities appear; existing ones expand. New screens proliferate;

old screens accrete options. Workﬂows grow more expressive by

adding options that ﬁne-tune the automation that unfolds after

data entry. In theory, complexity could fall; in practice, ambient

incentives prevent it. We return to this point next.

CRUD apps are, as the name suggests, crude. Putting aside

152 CHAPTER 5. INFORMATION

the foundational layer itself (database included), there is nothing

remotely “smart” in either the design or the execution of a CRUD

app. The software principles supporting the rules and behaviors

implemented in a CRUD are fundamentally accessible to anyone

who has completed middle school. It boils down to arithmetic

operations interleaved with IF-THEN statements, and little more.

Naturally, the business itself may be intricate, with rules and be-

haviors well beyond what a middle-school student can comprehend.

In such instances, the CRUD paradigm oﬀers only a laborious

decomposition into elementary processing steps.

CRUD’s simplicity made it the default business

app pattern for

decades, and since the mid

2010s open

source stacks have turned

it into a commodity. Those tools make software engineers far more

productive when developing CRUD apps than when developing

almost any other kind of software. Notably, CRUD apps are the

poster child in most programming-environment demonstrations.

Moreover, while hiring software engineers remains notoriously

challenging, CRUD development is largely accessible to junior

developers (and to mediocre seniors) and represents, by far, the

easiest kind of software to build.

Many ﬁrms still overpay for CRUD record systems, underes-

timating how thoroughly open

source tooling has commoditized

them. Vendors exploit that lag to keep prices high. No matter how

useful and mission-critical a system of records might be, this sort of

product can now be developed both quickly and cheaply. Yet, out

of entrenched habits, many companies still pay software vendors

annual fees beyond what it would cost to redevelop such systems

in-house. Naturally, vendors beneﬁt from this confusion and have

become masters at delaying the inevitable: lowering prices on the

ledgers they sell. This situation also follows from the technological

trajectory of systems of records.

5.3.3 Technological trajectory

Systems of records, unlike the spreadsheets they replaced, are rigid

and less expressive. Even an extremely capable system of records

5.3. SYSTEMS OF RECORDS 153

today covers only a fraction of what a loose collection of spread-

sheets supports. With loose spreadsheets, employees can always

deviate from formalized processes—so long as their colleagues agree.

Steps or requirements can be skipped; exceptions can be made to

any rule; or, conversely, an arbitrary judgment call can reject a

request that “blind” policies would deem acceptable. Naturally,

this freedom costs reliability and productivity, as spreadsheets

resist substantial automation.

Vendors typically launch a system of records with a small

set of entities plus the screens and workﬂows needed to handle

them. Technological resources at an emerging vendor are limited,

and there is no point in further development if the product can

be sold as is despite its limitations. Furthermore, developing a

business app without detailed feedback is futile before customers

have embedded it in their processes. Thus, while the initial core

may rest on an a priori view of the market, extensions are almost

invariably built in close collaboration with a few customers.

Business markets lack the depth of consumer markets. As a re-

sult, once a product is sold to a business client, the vendor naturally

leverages that position to sell anew to the same client—upselling

Selling extra modules to an existing customer is far cheaper than

winning a new one, since the latter entails clearing all corporate

hurdles of a new line of spending—such as going through a tender

against rivals.

Systems of records always oﬀer another entity to add—or en-

rich—so the vendor can justify the next sale. No matter the

product’s feature coverage, there are always more business con-

cepts and workﬂow variations to add. Nothing in the technology

of systems of records imposes a consistency imperative that would

restrain growth. For example, an inventory management system

can be expanded to manage customers; conversely, a customer-

relationship-management system can be expanded to manage in-

ventory. In fact, most large ERP vendors feature CRM capabilities;

Upselling also works with subscriptions. In this case, when the vendor sells

an extra, the subscription fee is increased.

154 CHAPTER 5. INFORMATION

conversely, most large CRM vendors feature ERP capabilities. It

boils down to adding more tables, more screens, and more business

logic, ad inﬁnitum. Setting aside per-client bespoke customization,

the system of records becomes the sum of the quirks of its entire

customer base.

Every commercially

maintained system of records keeps grow-

ing until the vendor goes bankrupt or retires the product line

entirely. As engineering cost is a superlinear function of the num-

ber of entities—i.e., implementing twice as many entities more

than doubles the costs—the product’s growth rate (in features

and capabilities) tends to slow over time, even if the vendor keeps

increasing its engineering headcount as the company expands. Yet,

despite those superlinear costs hindering an otherwise unlimited

complexity growth, many systems of records end up as feature

mastodons after two decades of continuous development. Numerous

popular systems of records, including those for smaller businesses,

boast over 10,000 tables, most with dozens of ﬁelds.

The path of relentless complexity growth is eﬀectively irre-

sistible for vendors selling systems of records. There is simply no

upside in ditching infrequently used entities: it antagonizes the few

client companies that use them—however small that number may

be—while leaving all other clients indiﬀerent. Unfortunately, this

path leads to a predictable bastardization of the product. Indeed,

while collaborating with customers is an essential ingredient of

business software development, some customers are misguided in

their requests. Others reﬂect narrow niches that did not warrant

bespoke capabilities. Still others impose terminologies or concepts

inconsistent with those adopted by the vendor. As a result, over

time, every actively developed system of records becomes a Love-

craftian construct endangering the sanity of those who contemplate

it for too long.

In A Deepness in the Sky (1999), American science-ﬁction writer

Vernor Vinge oﬀers an uncannily accurate picture of long-running

software initiatives:

There were programs here that had been written ﬁve thou-

sand years ago, before Humankind ever left Earth. The

5.3. SYSTEMS OF RECORDS 155

wonder of it—the horror of it, Sura said—was that unlike

the useless wrecks of Canberra’s past, these programs still

worked! And via a million circuitous threads of inheritance,

many of the oldest programs still ran in the bowels of the

Qeng Ho system. Take the Traders’ method of timekeeping.

The frame corrections were incredibly complex—and down

at the very bottom of it was a little program that ran a

counter. Second by second, the Qeng Ho counted from

the instant that a human had ﬁrst set foot on Old Earth’s

moon. But if you looked at it still more closely. . .the

starting instant was actually some hundred million seconds

later, the 0-second of one of Humankind’s ﬁrst computer

operating systems.

So behind all the top-level interfaces was layer under layer of

support. Some of that software had been designed for wildly

diﬀerent situations. Every so often, the inconsistencies

caused fatal accidents. Despite the romance of spaceﬂight,

the most common accidents were simply caused by ancient,

misused programs ﬁnally getting their revenge.

“We should rewrite it all,” said Pham.

“It’s been tried,” corrected Bret, just back from the freezers.

“But even the top levels of ﬂeet system code are enormous.

You and a thousand of your friends would have to work

for a century or so to reproduce it.” Trinli grinned evilly.

“And guess what—even if you did, by the time you ﬁnished,

you’d have your own set of inconsistencies. And you still

wouldn’t be consistent with all the applications that might

be needed now and then.”

Yet this spiral of accretion is not an iron law for the buyer.

Several large organizations have kept their ledgers lean by pushing

ancillary workﬂows into detachable sidecars—small services or plug-

ins that interface with the core through an API fence. Because

the plug

ins are owned, versioned, and—crucially—disposed of by

the client at will, cruft accumulates at the edge where it can be

pruned without touching the authoritative data store.

A more radical lever is phased retirement of functions. Contrary

to the folk wisdom that “nobody rewrites an ERP”, many ﬁrms

156 CHAPTER 5. INFORMATION

do—just not in a single heroic blast. The proven pattern is the

strangler: place a thin façade in front of the monolith, route a single,

self-contained capability (pricing engine, returns authorization, tax

module . . . ) to a greenﬁeld service, freeze the corresponding

tables, then physically drop them after a sunset period. Repeat

every quarter and the impossible big

bang becomes a sequence of

manageable surgeries.

These tactics do not ﬁx the vendor’s incentive to sell yet more

expansion packs; they merely restore agency on the client side.

Nothing forces a company to install the next glossy add

on when the

carrying cost of complexity exceeds the cost of a lean replacement.

In practice, the lowest long

term cost of ownership comes from

treating the central ledger as sacred ground and everything else

as disposable code. Complexity growth is the default, not the

destiny—provided the organization cultivates the discipline to keep

pruning its own garden.

5.3.4 Functional overlaps

Master data

is the ﬁrst casualty of functional overlaps. As soon as

two systems of records coexist, no 1-to-1 mapping of entities can be

assumed. Attributes that look identical (“product weight”, “order

date”, “supplier code”) routinely diverge in meaning, unit, timing,

and governance. What the ERP calls

product_family

may only

approximate what the PIM stores as

category

;

lead_time

in the

MRP may be a procurement promise while the WMS records an

empirical average. The so

called “golden record” is a managerial

ﬁction: in reality, each system is master for some attributes, some

of the time, for some use-cases.

This fragmentation is not a mere clerical nuisance; it is the

Colloquially: the supposedly “single source of truth” for core business

entities such as products, locations, suppliers, and customers. In practice, there

is almost never a single source, only competing partial authorities. Large “MDM”

suites promise a golden record through central hubs, survivorship rules, and

stewardship workﬂows. In practice, they often become yet another overlapped

ledger whose governance drifts as fast as the others.

5.3. SYSTEMS OF RECORDS 157

primary driver of semantic drift (see Section Semantics of the

data). When overlapping ledgers do not share isomorphic schemas,

information about a single business fact gets smeared across ta-

bles, systems, and teams, with no canonical reconciliation rule.

Master

data

management (MDM) programs attempt to paper over

this reality with central hubs and survivorship rules, but—absent

a clear, per

attribute authority and an auditable decision sys-

tem—they merely add another overlapped system and another

layer of synchronization to maintain.

Functional overlaps between third-party systems of records

are the direct consequence of their technological trajectory, and

they hit master data the hardest. Indeed, as software vendors

keep pushing their respective functional envelopes, collisions are

inevitable; only their timings remain uncertain. Moreover, larger

vendors are especially prone, having grown by continually widening

their functional coverage. This problem is speciﬁc to third-party

systems; even modest internal coordination usually prevents such

overlaps from arising in in-house developments.

In practice, overlapping systems require data-synchronization

processes. Master-data synchronization is particularly pernicious:

schemas are rarely isomorphic, attributes carry diﬀerent semantics,

and per

attribute authority (which system is master for what, and

when) is seldom spelled out. Such synchronizations are invariably

ad hoc software projects. Indeed, the enterprise

software market is

too fragmented to justify developing plug

and

play synchronization

tools.

A two-way synchronization between systems of records—where

edits can be made on both systems and each edit in one must

be propagated to the other—typically requires at least an order

of magnitude more eﬀort than one-way integrations, where one

system is deemed “master” and the other “slave”. A two-way data

synchronization must address conﬂicting edits and their resolution,

which is far trickier than simply copying and overwriting all edits

from the master into the slave. For master data, where multiple

departments continuously edit diﬀerent subsets of attributes, this

conﬂict surface is the rule, not the exception. Despite the existence

158 CHAPTER 5. INFORMATION

of exceedingly clever algorithms dedicated to the case, N-way

data synchronization, with N greater than 2, should generally be

regarded as an abomination.

Even when IT carefully identiﬁes which system is master for

each area, most large systems end up both master and slave—depending

on the area (e.g., the ERP may be authoritative for price and the

PIM for descriptive content on the same SKU). For example, the

CRM may be master for “customers” yet slave to the ERP for

“suppliers”.

These overlaps pose a massive, ongoing hurdle that companies

consistently underestimate. In particular, when it comes to picking

the next software vendor, companies often prize impressive feature

lists, as illustrated by the typical RFQ (Request For Quotation)

process that frequently includes hundreds of requirements. As

a result, companies tend to pick large vendors with needlessly

complex products, following the common-sense intuition qui peut

le plus, peut le moins (he who can do more can do less). Yet this

misguided intuition invariably leads companies to pick vendors

with vast functional overlaps. Those overlaps prove extremely

costly.

These overlaps are not just an IT tax; they directly corrupt

meaning. We return to this point in Section Semantics of the data,

where we show how elusive, row

dependent semantics quietly derail

optimization eﬀorts.

First, for the IT department, resolving synchronization issues

is sharply superlinear—the eﬀort grows far faster than the number

of entities. Soon enough, the Tokyo subway map looks simpler

than the synchronization graph that IT has to maintain. It is now

common to see IT divisions go eﬀectively bankrupt: they can no

longer keep up with maintaining the synchronization graph amid

the never-ending stream of updates to their third-party systems of

records.

Second, for employees entering data, these overlaps create an

ongoing source of confusion, as the same entities can be viewed and

edited in more than one place. Software vendors have no incentive

to facilitate the “enslavement” of parts of their products to rivals

5.3. SYSTEMS OF RECORDS 159

in order to mitigate this confusion. From their perspective, even

an unused feature keeps a foot in the door for the next upsell.

Furthermore, the idea that the company ought to pay for having

features removed from a system simply goes against the entire

paradigm governing the enterprise software market.

Third, all attempts at exploiting the data, either as part of a

system of reports or a system of intelligence, confront a hall of

mirrors: the same information appears in multiple places. However,

secondary data, as generated by synchronization processes, should

not be used for those purposes. Secondary data might be outdated,

degraded, or subtly altered, wreaking havoc in downstream pro-

cesses. Thus, for every piece of information, one must ensure he

reads the canonical master record.

While the history of enterprise software is short, in-house de-

velopment of systems of records—despite their ﬂaws—is the only

viable way to avoid the relentless costs induced by functional over-

laps between third-party systems of records. Fortunately, as noted

above, developing such systems of records has, for all practical

purposes, been commoditized.

5.3.5 Functional limits

While systems of records deliver value to the ﬁrms that run them,

what they can deliver is strictly limited. Systems of records are, at

best, improved ledgers. In particular, they are not eﬀective systems

of reports nor systems of intelligence; those aims are technologically

antagonistic to the basic functions they must serve.

Much of a system of records’ value derives from its database—its

transactional core. The system’s design is fundamentally central-

ized—precisely what the business wants. For example, if the system

holds the stock level of a given SKU, every user or agent querying

it must see the same value. Furthermore, if someone claims 1 unit,

that unit must not be granted to a competing claim. Maintain-

ing electronic-stock integrity requires the system to coordinate

competing concurrent claims on the same datum—a unit in stock.

More generally, this class of issues is resource contention: con-

160 CHAPTER 5. INFORMATION

ﬂict over access to a shared resource such as RAM, disk, or other

hardware. Resource contention is notoriously hard, as it often

boils down to reducing latency. Yet latency progress largely stalled

over a decade ago—over two for many speciﬁc challenges. To a

large extent, latency is now bounded by the speed of light itself.

For example, data packets over the Internet propagate at roughly

two

thirds of the speed of light in vacuum. This leaves little room

for further improvement

. Another example: in about 0.3 nanosec-

onds—roughly a single CPU clock cycle—light can only travel back

and forth about 5 cm.

Techniques exist to mitigate resource contention on compu-

tationally heavy processes—horizontal partitioning (sharding),

read/write splitting, asynchronous queues, CQRS

/event sourcing,

distributed locks, saga/orchestration patterns, and more. Each,

however, trades the very virtues a system of records must maxi-

mize—simplicity, traceability, strong consistency—for operational

and semantic complexity: eventual consistency to explain down-

stream, duplicated read/write paths, elaborate retry/compensation

logic, brittle distributed transactions, heavier observability bur-

dens, slower schema evolution, and harder audits. In short, a ledger

can be made to scale with such patterns, but the price is ambigu-

ity and fragility precisely where the ﬁrm needs an authoritative,

crystal-clear truth. Consequently, anything computationally “fat”

should be expelled from the ledger and delegated to a system of

reports or of intelligence.

Resource contention largely explains why business apps are not

fundamentally snappier today than in the early 2000s. Editing

an entity still incurs a user-noticeable delay—tens of milliseconds.

Lags of a second or more for seemingly basic operations remain

Hollow-core optical ﬁbers (HCF) guide light mostly through air rather than

silica, bringing propagation speeds closer to

and yielding, on long links, single-

digit to low-double-digit percentage latency cuts. Even if HCF were widespread,

switching, buﬀering, routing detours, and protocol handshakes would keep end-

to-end decision latencies largely bounded; the macro-argument—that latencies

are now a hard constraint for enterprise systems—still stands.

Command Query Responsibility Segregation.

5.3. SYSTEMS OF RECORDS 161

common. Such lags seem surprising given that, at equal cost,

hardware is now orders of magnitude more capable than two

decades ago. Yet the stagnation reﬂects ﬂat latency progress;

resource contention does not improve either.

Inside the system of records, because resources are shared,

every operation must be as light as possible. Any heavy operation

risks starving the system, degrading the quality of service for other

concurrent operations. Hence decision-making processes must be

scrupulously avoided within a system of records. Decisions require

weighing numerous options and entail orders-of-magnitude more

compute than basic arithmetic or logical rules.

Consider a procurement optimizer that needs 10 seconds of

CPU to compute the order quantity of a single SKU. In a system

of intelligence, run oﬄine or on a dedicated cluster, that cost is

trivial—and even 100 seconds to squeeze a few extra basis points

may be rational. Inside a system of records, however, the same

computation is catastrophic: with tens of thousands of SKUs

per day, 10 seconds per SKU becomes dozens of CPU-hours that

contend with transactional workloads, stall screens, and degrade

user experience. Even at 1 second per SKU, it would still create

unpredictable latencies where the ledger must guarantee low, stable

response times. Heavy optimization therefore belongs outside the

ledger: the system of records should persist compact outcomes

(e.g., purchase-order lines), not perform the combinatorial search

that produced them.

In theory, alternatives to CRUD exist to interleave heavyweight

with lightweight operations. In practice, however, those alternatives

undermine the very reasons CRUD is attractive. The simpler, more

practical approach is to segregate all the “fat” operations away from

the system of records. Descriptive statistics belong in systems of

reports; decision-making belongs in systems of intelligence. Keep

the three classes strictly decoupled, and resource contention is

largely mitigated.

Beyond low-level contention lies a weightier obstacle: the en-

gineering cultures of the three software classes are fundamentally

incompatible. From afar every class appears similar—each involves

162 CHAPTER 5. INFORMATION

writing code—but moving between them is as diﬀerent as house

painting is from ﬁne art. Both trades use paint; they share little

else. Detailing those nuances is outside our scope; suﬃce it to

say that for ﬁve decades vendors have strayed beyond their home

class—most often with dismal results, especially when systems-of-

records specialists expand into systems of intelligence. Yet vendors

keep trying, oblivious to the predictable failures that follow.

5.3.6 Vendor’s conundrum

The value-add of a system of records is fundamentally limited.

Once the system has ample coverage and proves reliable, no further

beneﬁts accrue. Software vendors selling systems of records have

always been painfully aware of this limitation. Hence, since the

late 1970s, nearly every successful vendor of systems of records has

tried to venture into systems of intelligence.

To understand why, let’s consider the case of an accountant.

There is little tangible beneﬁt in a great accountant over a merely

competent one. Hence companies do not hire “superstar” accoun-

tants at hundred-fold salaries; accounting delivers only bounded

value. Marketing, by contrast, can add value without limit. A

marketing genius can revitalize a brand in seemingly impossible

ways, creating fortunes for shareholders. As a result, companies

routinely seek “superstar” creative minds and pay them lavishly.

A system of records contributes like an accountant. Once the

job is done properly and diligently, nothing more remains to expect.

By contrast, the value-add of a system of intelligence—the decision-

making engine—is akin to that of marketing talent. For practical

purposes, there is no upper bound on the quality of a business

decision.

Consequently, willingness to pay for newer, better systems of

records is limited. This fundamental insight isn’t novel and was

already perceived as such by software vendors in the 1970s. Until

the early 1990s, successful vendors had little engineering capacity

beyond adding features and keeping up with steady hardware

progress. This changed in the 1990s and led many to rebrand as

5.3. SYSTEMS OF RECORDS 163

“ERP” vendors, the novel “P” for planning signaling an intent to

be systems of intelligence. Similar moves occurred across other

variants of systems of records. Yet, as noted, those attempts have

largely failed—and still fail—sometimes spectacularly.

As a side eﬀect, vendor marketing often confuses executives. As

the distinction between systems of records, systems of reports, and

systems of intelligence remains relatively unknown to the general

public, vendors invariably tout the promise of better decisions. In

short, every enterprise software product is pitched as a system

of intelligence. For instance, a system that merely logs stock

movements while clerks handle replenishment should not promise

fewer overstocks or stockouts—yet such claims are ubiquitous.

From the buyer’s angle, three simple heuristics cut through the

confusion.

First, a bona ﬁde system of intelligence ships with almost no

data-entry screens. Intelligence consumes authoritative records; it

does not solicit daily re-keying or “conﬁrmation” by users. Each

additional form is a tell-tale sign of a ledger in disguise, and it

muddies accountability: when outcomes are poor, the vendor can

always claim that “the user typed the wrong number”. A genuine

system of intelligence owns its decisions end-to-end: its inputs

are the ﬁrm’s records; its outputs—orders, allocations, prices,

schedules—are auditable against proﬁt and loss. No daily clerical

escape hatch remains for either party.

Second, the engine must not pretend to be a standalone solution.

Beyond its own code, conﬁguration, and audit logs, it keeps no

durable business state. Any working tables or caches are disposable

by design. It reads the authoritative system of records, computes

recommendations, and writes them back through a narrow, ver-

sioned API. The moment master

data tables, workﬂow builders,

or user

management modules appear, what is on oﬀer is a system

of records masquerading as “AI”.

Third, the commercial model must align fees with the economic

quality of the decisions delivered, not with head

count, storage

volume, or page views. Seat-based, page-view-based, or gigabyte-

based pricing creates a perverse incentive to keep more people in the

164 CHAPTER 5. INFORMATION

loop, proliferate dashboards, and hoard data, since vendor revenue

then scales with organizational ineﬃciency. A genuine system

of intelligence should, on the contrary, make most clerical seats

disappear. Contracts should therefore index payment to decision

throughput and/or to a measured share of the value created (e.g.,

risk-adjusted working-capital reductions, gross-margin uplift, or

quality-of-service improvements at constant capital employed),

with explicit caps to bound risk for both parties.

Taken together, these negative tests discard most “intelligent

platforms” and let the practitioner focus his attention—and bud-

get—on the few oﬀers with a credible shot at improving supply-

chain decisions.

A common misstep is upgrading a system of records simply

because it “looks old”. Executives see IT progressing by leaps

and bounds and infer the newer system must be much better.

Furthermore, the look and feel of the latest versions proposed by

vendors is usually more attractive than that of the old system. The

inference is misleading. Progress in software technologies does not

imply that systems of records themselves are progressing.

On the contrary, the core technologies and concepts underpin-

ning systems of records have barely progressed since the 1980s.

Current progress focuses almost exclusively on cutting development

costs for technologies already heavily commoditized. Thus, for

systems of records, the primary goal when upgrading should be to

reduce fees paid to vendors. Similarly, the beneﬁts of a better look

and feel are usually overestimated: if the old system was snappy,

fancier screens rarely make employees more productive. Highly

productive interfaces are often perceived as hostile to newcomers

precisely because they lack the hand-holding features that hinder

productivity once employees know the system.

Collectively, without collusion or even coordination, vendors

of systems of records have fostered a persistent state of confusion

in the general public. In the early 2020s, although systems of

records had been commoditized for over a decade, most companies

were paying more for them than two decades prior. This state of

aﬀairs is broader than our supply-chain concerns, but supply-chain

5.4. DATA 165

software may be the most telling example. This again illustrates

the adversarial nature of supply chain challenges, where incentives

are only partially aligned.

Thus, systems of records are indispensable—and tightly bounded.

They are the ﬁrm’s authoritative ledgers, not its calculators or its

strategists. Whenever they are asked to report heavily, optimize, or

“plan”, resource contention, semantic drift, and accountability fog

follow. The right architectural stance is austere: keep the ledger

thin, central, and boring; push descriptive analytics to systems

of reports; push decision-making to systems of intelligence; fence

every interface with a narrow, versioned API; prune functional

overlaps relentlessly, even if vendors resist.

The corollary is as managerial as it is technical. Treat the

ERP (and its cousins) as sacred ground: it remembers facts and

enforces workﬂows—nothing more. Every extra feature that creeps

in—KPIs, forecasts, optimizers, “what

ifs”—quietly taxes perfor-

mance, muddies semantics, and diﬀuses responsibility. If something

is computationally fat, probabilistic, or economically arbitrated, it

belongs outside the ledger.

With the role and limits of systems of records made explicit,

we can now turn to what lives inside them: data. The next

section examines how those raw symbols become information, why

semantics—not missing rows—is the true hard part, why “bad

data” is the wrong scapegoat, and how to extract, document, and

harden what the decision engines will need.

5.4 Data

In digital form, business data are by far the most important sources

of information for supply chain decisions. These data are collected

primarily through the company’s systems of records. Other sources

exist, but they matter far less. From a supply chain perspective,

the relevant information lies within this data; extracting that

information proves harder than generally assumed. Furthermore,

the data’s complexity is likewise underappreciated.

166 CHAPTER 5. INFORMATION

Data are critical because supply chains are not directly ob-

servable; one sees them only through the company’s records. One

could argue that supply chains ceased to be directly observable—

by the senses in any meaningful way—well before the advent of

computers. Indeed, by the end of the 19

century, the rise of the

giant corporation brought armies of bookkeeping clerks. Thus,

even then, the supply chain was seen only through ledgers kept

by those clerks. Many objects in human knowledge, like supply

chains, are not directly accessible to the senses; the consequences

are substantial.

As noted earlier, many supply-chain authors either ignore soft-

ware or treat it superﬁcially. Data are no exception. In particular,

data are usually conﬂated with the information they contain, as

if transforming the former into the latter were a given. Nothing

could be further from the truth, and this transformation alone

often consumes more than half the technical eﬀort in a supply

chain initiative. Moreover, far from being mere software techni-

calities, this transformation requires a deep grasp of the supply

chain challenge. As a result, it is profoundly misguided to think

this task can be delegated to IT, or to any specialists who are not,

ﬁrst and foremost, supply chain specialists.

Against this backdrop, a stock explanation is wheeled out

whenever a planning initiative stalls—the data are bad. Past

failures—rarely investigated—are paraded as proof. The slogan

is convenient: it absolves vendors and integrators, blames an

amorphous scapegoat, and discourages any examination of the

ledgers. In digitized supply chains, the transactional record is

usually good enough; what is missing is the hard work of turning

records into information—semantics clariﬁed, provenance and au-

thority established—and then into decisions. The sections that

follow separate those steps and make their failure modes explicit.

5.4.1 Origin of the data

Most business data lives in systems of records, yet those systems

collect it only incidentally. The point of systems of records is

5.4. DATA 167

to digitize the ﬁrm’s information processes, making them more

reliable and productive. Data are not the intended outcome; it is

a by-product of those operations. This insight is simple, yet its

consequences are frequently misunderstood.

Data are organized to mirror the system’s quirks. Such intri-

cacies arise from subtle performance constraints, leftovers from

aborted upgrades, outdated concepts from an earlier development

era, or the routine compromises of software development.

To make this concrete, consider two commonplace examples

showing how record structures reﬂect engineering trade-oﬀs rather

than analytical needs.

Decades ago, a well-meaning engineer—working on a still-

popular ERP—stored product returns alongside sales orders in the

sales

table with negative quantities. With that trick, summing

sales over time yields a ﬁgure “almost” net of returns. The de-

sign, however, has several downsides and proves far less clever in

practice. For service quality, relying on net sales biases analysis

toward overconﬁdence about stockouts. If 10 units are sold in one

week and 3 are returned the next, at least 10 had to be in stock

in the ﬁrst week—not 7—to serve demand. Conversely, if most

returned units can be reconditioned and resold

, then replenishing

inventory right before season’s end is likely to generate sizable

overstocks, as stock will naturally beneﬁt from the intake of re-

turns. More generally, returns are not sales; while they may share

ﬁelds (customer, product identiﬁers), conﬂating them in one table

forces columns that make sense for only one of the two—e.g., a

cause_of_return ﬁeld.

Consider a simple online store front-end. After each trans-

action, stock-on-hand must be decremented to keep availability

current for later patrons. Updating stock-on-hand is a straightfor-

ward

UPDATE

on the table that holds those values; call this table

stock

. However, as virtually every team behind a moderately

In the 2010s, fashion e-commerce in Germany routinely featured return

rates above 50% of the units ordered. Indeed, many leading e-commerce retailers

had generous return policies, and patrons were used to order multiple sizes for

their garments and return all the units that didn’t ﬁt.

168 CHAPTER 5. INFORMATION

popular store has found

, updating

stock

after every transaction

creates heavy contention in the database—precisely around that

table. Indeed,

stock

is sizable—typically tens or hundreds of

thousands of rows—and each update can trigger a cascade in the

app’s front end. The usual ﬁx is to add a

pending_updates

table

that queues stock changes. On each transaction, rows are added to

pending_updates

instead of updating

stock

. Then, every minute

or so,

pending_updates

is purged while

stock

is updated in batch.

As a result, stock-on-hand information is split across two tables

rather than one. The pattern is sound, but a supply-chain special-

ist must decide whether ignoring

pending_updates

—purely for

simplicity—remains acceptable.

Many other cases tell the same story: data in systems of records

was never meant for data

science work. For the engineers who

wrote the software, it was at best a secondary concern, often an

afterthought.

Corporate managers are frequently surprised by the time and ef-

fort required to extract the right information from business systems.

Many attribute the overheads to the IT department’s supposed

ineﬃciency and to overcomplicated tools. That impression is re-

inforced by IT specialists who hide behind technical opacity to

justify costs and delays. This perspective, however, is mistaken.

Data in systems of records is readily accessible and, assuming a

mainstream database, the query tools are nothing short of excellent.

Costs and delays are high because turning data into information is

surprisingly diﬃcult—especially when attempted by IT specialists

lacking the ﬁeld expertise the task requires.

5.4.2 Semantics of the data

A typical enterprise system of records stores tens of thousands of

ﬁelds; the largest exceed one hundred thousand. The semantics of

those ﬁelds—what they mean, the purpose they serve, the behaviors

If counting in-house developments, then the ‘e-commerce front end’ app has

been redeveloped independently more than a thousand times worldwide since

the early 2000s.

5.4. DATA 169

they trigger—are usually, at best, loosely documented. That

documentation may come from the original vendor or, for in-house

systems, from the company itself. Custom extensions—ubiquitous

in enterprise software—further blur meaning. In practice, there

is rarely more than a short paragraph per ﬁeld, and too often it

merely paraphrases the ﬁeld name.

To see why semantics matters, consider a single ﬁeld: “order

date”. That one timestamp might denote checkout time, last edit,

acknowledgment, or payment clearance. Each reading drives a

diﬀerent operational reality: it shifts measured lead times, alters

service and ﬁll-rate metrics, and changes how cash ﬂows and

liabilities are recognized. Ambiguity at this level quietly corrupts

every downstream calculation.

In an ideal world, two things would hold: the builders would

leave precise ﬁeld-level documentation, and management would

enforce day-to-day practices that keep meanings stable in opera-

tions. In practice, three forces break that ideal: age (systems and

notes outlive their authors), ﬁeld-level inconsistency (meanings

drift across modules and versions), and operator-driven semantics

(users repurpose ﬁelds to get work done). The next paragraphs

examine each in turn.

By software standards, most systems of records are old—two

or three decades are common. Early implementations rarely came

with ﬁeld-level documentation and, even when they did, the authors

long ago moved on. The code has been patched, migrated, and

extended so often that the original intent is unrecoverable from

commit messages. Crucially, ﬁeld semantics are inseparable from

the behaviors wired to them, and those behaviors shift across

versions and modules. The net eﬀect is predictable: large swathes

of meaning are simply lost, and reconstructing them amounts to

reverse-engineering—a slow and expensive task. Outdated—or

outright obsolete—notes are the bane of long-running software

initiatives.

Loss is only half the story. New meanings also accrete informally

as operations adapt. Teams invent workarounds, reinterpret labels,

and attach new workﬂows to convenient ﬁelds. Over time these

170 CHAPTER 5. INFORMATION

ad hoc practices become the system’s de facto contract, regardless

of vendor intent or what the manual still says. This emergent,

undocumented layer drives semantic drift: the “current meaning”

of a ﬁeld is whatever the data-entry crowd now uses it for—and

it can diﬀer by site, period, or role. Pinning this down requires

observing practice, not just reading schemas.

In practice, users are structurally unaware of this drift. Mean-

ings accrete over years—often longer than anyone’s tenure in a

given role—so today’s interpretation simply feels “the way the sys-

tem works.” If the application exposes two near-synonyms such as

Discontinuation Date

and

Phase-out Date

, and neither drives

a visible automation, teams adopt a convenient house meaning

and move on. Whether that meaning matches the vendor’s intent

is immaterial; what matters is that the local crowd converges

on one reading and uses it consistently. Nobody will mistake

Volume

for “heel height,” but many will, quite sensibly, agree

that

Discontinuation Date

marks the moment remaining stock

is routed to oﬀ-price liquidation. Given that most ﬁelds trigger no

behavior at all, divining the vendor’s semantics is wasted eﬀort;

ensuring everyone inside the ﬁrm uses the same semantics is the

real work.

Furthermore, ﬁeld semantics are often row-dependent; not

all values of a given ﬁeld mean the same thing. We saw such

an example above with “order quantity” referring to a purchase

when positive and to a return when negative. More generally, a

ﬁeld’s semantics often depend on the value of another ﬁeld (or

several others). Documentation cannot be a mere dictionary of

ﬁelds; meaning changes with context. To capture row-dependent

semantics, documentation must cover the relevant business logic—

again a costly, lengthy exercise, worsened by years of system

evolution.

Ultimately, meaning rests with the data

entry crowd—employees,

clients, even suppliers—whatever the engineers once intended. For

example, if Purchasing repurposes an obscure ﬁeld—once unused

and tied to arcane Incoterms—to encode a supplier’s transport

route, then the semantics of that ﬁeld become the transport route,

5.4. DATA 171

not the original documentation. While repurposing a ﬁeld is hardly

good practice, it happens routinely. Moreover, as ﬁeld semantics

are tricky at the best of times, teams make genuine mistakes,

attributing close-but-not-quite-right meanings to numerous ﬁelds.

Pinning down ﬁeld semantics is hard—even “market-leading”

systems prove surprisingly and invariably arcane. This work is not

pedantry: without stable meanings, raw records cannot be turned

into information a decision engine can use. Beyond the practical

obstacles above, one more hurdle remains: we must be able to

verify that the meanings we ascribe are in fact correct.

Even a modest supply-chain initiative typically requires docu-

menting at least a hundred ﬁelds. For each ﬁeld, half a page to a

full page is usually needed to pin down the semantics that matter

for decisions: deﬁnition; units and admissible values; provenance

and authority; row-dependent interpretations; cross-ﬁeld depen-

dencies; and concrete examples. These notes should be grounded

in the ﬁrm’s own data—simple distributions, ranges, and counts

often surface what matters (for example, a typical hypermarket

in our portfolio carries more than 100,000 SKUs, a fact that rules

out algorithms whose cost grows superlinearly with the number of

items). As a consequence, the semantic dossier for a “small” scope

easily runs to a hundred pages or more. Thus, given the volume,

it is unreasonable to expect all of it to prove factually correct.

Errors—ranging from crass mistakes to subtle misunderstandings—

will litter the documentation. Hence a method is needed to assess

the validity of this knowledge and weed out the errors.

Extra reviews—even peer reviews—rarely catch every error.

Indeed, once a misunderstanding is solidiﬁed in the documentation,

a new element is usually needed to reveal it. That element must

be real-world feedback from a decision-making process that draws,

among other things, on this knowledge. If ﬂawed knowledge yields

sound decisions, the ﬂaw is harmless and the issue moot. The ﬁne

print of this methodology—experimental optimization—will be

detailed in a later chapter. For now, suﬃce it to say that semantic

validity must ultimately be rooted in empirical feedback from the

practical application of this knowledge while generating decisions.

172 CHAPTER 5. INFORMATION

5.4.3 Shenanigans in data

Data, like everything else in supply chain, bends to the incentives

of its makers and collectors. As noted earlier, incentives often

diverge from the company’s interests. This yields numerous quirks

in the data—some minor, some consequential.

Consider a large manufacturer’s real-world mishap. The manu-

facturer was suﬀering from chronic overstocks. A glance at past

orders revealed routinely exuberant buys—the main cause of the

overstocking. To redress the situation, management chose a new

supply chain vendor to fully automate replenishment, removing

well-meaning but misguided judgment calls from the purchasing

team. Pre-production tests showed the excessive orders vanished.

This still left the possibility of overstock whenever a large, unex-

pected drop in demand occurred, but instant overstocks absent

major market swings were eliminated. The solution looked promis-

ing.

Yet two months into production, excessive replenishment orders

persisted. The result was puzzling: the orders bore no resemblance

to the recipe’s output.

Closer inspection suggested a mundane cause: purchase or-

ders were still keyed into the ERP by hand because bidirectional

integration had been deferred—the IT team was overloaded. His-

torical data extraction from the ERP—a venerable four-decade-old

system—was automated, but writing back the new orders was

not. The decision engine produced orders unattended; clerks then

retyped them into the ERP each day. This remaining manual hop

became our prime suspect.

However, after days of careful assessment, it became clear that

while manual data entry was involved, this step was performed

very carefully, and that, under no circumstances, were employees

intentionally modifying the quantities. Thus the new solution

again became the primary suspect. Yet log inspection quickly

showed the new solution had never generated those excessive orders.

Dates and products matched; quantities did not. The baﬄement

deepened: day after day, the orders entered into the ERP the

5.4. DATA 173

previous day exactly matched the solution’s output. Yet days—or

even weeks—later, some reorder quantities suddenly ballooned,

and the next inventory check conﬁrmed the surplus.

Replenishment orders seemed to surge spontaneously, without

rhythm or reason. So the vendor’s team studied those occurrences

more closely. A surprising pattern emerged: the surges appeared

chieﬂy in orders placed near quarter-end. Because lead times varied

by product, the overstocking events were fairly evenly distributed,

which had obscured the pattern. The calendar pattern was clearly

human, but the actors remained a mystery.

Weeks of investigation led the vendor’s teams to the receiving

docks, where merchandise was unloaded from supplier-chartered

trucks. The receiving teams followed multiple quality-assurance

steps. One step was to count the units and ensure they matched

the ordered quantity. When a discrepancy arose, the ERP oﬀered

little to handle the edge case. In fact, the only oﬃcial policy

the ERP supported was to declare the shipment noncompliant

and return it wholesale. Yet the receiving teams had learned this

invariably incurred the production teams’ wrath. If goods had

been ordered, they were needed; without them, production halted,

infuriating colleagues.

Decades ago, to cope, the receiving teams established a back-

door into the ERP. To avoid returning a shipment for a single-unit

discrepancy, the team used the backdoor feature to adjust the

order quantity to the quantity received. The supplier was then

paid the adjusted amount, reﬂecting what was actually received.

For years, the setup worked, until the backdoor was essentially

forgotten by all but the receiving team.

Over time, a few suppliers noticed that small over-shipments

were accepted without so much as a phone call. This was unusual:

clients typically returned the excess units or at least complained

loudly. A few suppliers began exploiting the process. At quarter-

end, if lagging their targets, they used the manufacturer as the

variable of adjustment. At ﬁrst, suppliers inﬂated orders only

slightly, but the manufacturer’s silence soon emboldened them to

submit far larger quantities.

174 CHAPTER 5. INFORMATION

Once the pattern was understood, identifying the misbehaving

suppliers was easy. A series of phone calls to the shortlist of

incriminated suppliers ended the explosive surges overnight.

Unexpectedly, though the pattern was baﬄing and obviously

detrimental to supply chain performance, the data were funda-

mentally correct. The ordered quantities were real and accurate,

as plainly demonstrated by the stock on hand. The error lay

in the real policy governing replenishment—letting suppliers de-

cide whatever they wanted—which the manufacturer had never

envisioned.

While data shenanigans are not always as convoluted as the

one above, unintentional adverse incentives routinely arise in any

sizable supply chain, leading to all sorts of data oddities. We

have already noted a common mistake in supply chain circles:

treating data problems as matters to be left to IT specialists.

The example above makes clear why this approach usually fails.

Grasping supply chain data is ﬁrst and foremost the province of

supply chain specialists.

5.4.4 Shadow IT

In organizations, shadow IT denotes departmental systems that

operate without central IT oversight—systems the main IT team

may not even know exist.

Shadow IT exists in virtually every

large company, though prevalence varies greatly. From a supply

chain perspective, shadow IT systems are predominantly loose

systems of records and systems of reports, often mixing the two.

Historically, shadow systems have almost never been systems of intelli-

gence. True decision engines demand durable data integrations, empirically

validated modeling, version-controlled code, resource isolation, and runtime

monitoring—requirements that exceed what spreadsheets or ad hoc desktop

databases can deliver. Consequently, shadow eﬀorts concentrated on records

and reports. However, since the early 2020s, large language models and agent

frameworks have made it feasible for non-IT teams to assemble narrow, quasi-

autonomous decision bots. This “shadow intelligence” will dominantly arise in

small, well-scoped niches (e.g., survey and qualify potential suppliers based on

public web data) where data plumbing requirements are minimal.

5.4. DATA 175

Shadow IT matters for supply chain because the “system of records”

portion often holds valuable data. For example, ERPs commonly

omit some ordering constraints that apply to speciﬁc suppliers. As

a result, to compose a valid replenishment order, the practitioner

relies on his own shadow system containing a complete survey of

the applicable constraints.

Spreadsheets are, by far, the most common technology for

shadow IT. Any workﬂow that relies on employees interacting

with a shared spreadsheet is almost certainly shadow IT. IT knows

spreadsheets are used, and the software itself is approved. However,

a spreadsheet-run system of records lacks what IT should expect

of any supervised system: versioning, backups, audit trails, ﬁne-

grained permissions, etc.

Beyond spreadsheets, simple databases

are also commonly

used as part of shadow IT. These databases allow more polished

systems of records, enforcing a modicum of type-level correctness

(a value is a number, a date, etc.). Yet, like spreadsheets, they lack

the supporting processes that ensure long-term maintainability

(versioning, backups, etc.).

The motivation for shadow IT is straightforward: central IT is

swamped for months—sometimes years—so business teams build

what they cannot wait for. The paradox is that shadow IT is

rarely born of excessive zeal for software; it is born of too little

software culture. Software now touches almost every workﬂow, so

most departmental problems are, in part, software problems. If

the organization expects a single central IT group to solve all of

them, overload is guaranteed.

The cultural deﬁcit ampliﬁes the backlog. Outside IT, teams

often lack the vocabulary to state requirements, to rank them

economically, or to estimate engineering eﬀort. Pet projects are

promoted as “quick wins,” essential needs are deferred because

they look dull, and tickets arrive as solutions (“add a ﬁeld”) rather

From the mid-1990s to the late 2010s, the database management system

Microsoft Access enjoyed considerable popularity in large companies, allowing a

white-collar employee to roll out his own system of records backed by a relational

structure.

176 CHAPTER 5. INFORMATION

than problems (“we need to stop double-billing returns”). Central

IT, besieged by poorly framed requests, appears unresponsive;

business units, forced to deliver, turn to spreadsheets and ad hoc

databases. Friction becomes the norm and productivity erodes on

both sides.

Central IT is also in a bind: employees are understandably

reluctant to expose the full extent of their shadow initiatives, which

often run with tacit managerial approval and plainly violate IT

guidelines. As a result, the very people who could help regularize

these tools discover them only when something breaks. Expecting

central IT to “bring the data” is therefore unrealistic; by design,

much of it is invisible.

The way out is straightforward, if not easy: replace shadow

systems of records with small, maintainable CRUD apps, one by

one. Prioritize ledgers ﬁrst, reports later. Records are at risk

of loss; reports can be recomputed once the underlying tables

are sound. Concretely: take the live spreadsheet (or desktop

DB), freeze it read-only, sketch a minimal schema that reﬂects the

actual workﬂow, stand up a narrow CRUD service with versioning,

backups, audit logs, and permissions, backﬁll the data, redirect

users, then retire the old ﬁle. Rinse and repeat. Once the data sits

in a production-grade store, rewriting the surrounding reports is

cheap and fast.

Supply-chain specialists must own this remediation. Software

is an auxiliary science of the practice, not an outsourcing target.

Central IT should provide the guardrails (runtime, security, back-

ups), but the scope, sequencing, and semantics must be driven by

those who understand the ﬂow of goods.

5.4.5 Derived data

Business systems store more than facts. Alongside event records

(sales, receipts, stock counts, price agreements, calendar con-

straints) sit large volumes of what I will call derived data: numbers

created from other records—aggregations, forecasts, EOQs, safety

stocks, classiﬁcation ﬂags, and the like. Derived data add no novel

5.4. DATA 177

information; they are a re-expression of what is already in the

ledger. A beginner expects every table and ﬁeld to carry “new”

data; in practice, the majority of tables in a mature application

are artifacts. For clarity, facts data refers to the original records of

the ﬂow, and derived data refers to anything computed from them.

Not all derived data are equal. One class is properly motivated:

working or temporary tables (materialized views, caches, denor-

malized summaries) introduced for performance or operational

convenience. They exist to make the system responsive and are

disposable because they can always be recomputed from facts data.

The other class is epistemic decoration: planning artifacts saved

into ledgers (forecasts, EOQs, “service levels”, ABC tags, etc.).

These tables also contain no new information; they merely freeze

the output of a recipe. When mixed with records, they blur ac-

countability, multiply inconsistencies, and later mislead systems of

reports and systems of intelligence.

Demand forecasts are a classic example of such artifacts. More

generally, almost all planning data held by companies are nothing

but derived data. Unfortunately, many practitioners mistake these

artifacts for stepping stones toward a system of intelligence; in

practice they are landmines.

Before turning to why planning artifacts are misleading, let

us dispatch a benign subclass of derived data. Engineers often

introduce “working” or temporary tables—materialized views, de-

normalized summaries, staging areas—solely to keep CRUD ledgers

responsive or to decompose heavy logic. These tables are conve-

niences, not knowledge; they carry no new information beyond

the underlying facts and can be recomputed at will. Practitioners

should largely ignore them for decision-making.

Misguided practitioners are naturally drawn to artifacts such

as sales forecasts, economic order quantities, safety stocks, and

service levels because these aggregates feel “digestible”. These

aggregates compress thousands of row-level events into a handful

of numbers—a weekly sales total looks far more actionable than a

page of transactions—so their informational density for the human

reader is high. Yet this convenience should not be confused with

178 CHAPTER 5. INFORMATION

novelty: these ﬁgures contain exactly what the underlying facts

already implied—and nothing more. Treating such summaries as

primary inputs to a decision engine mistakes ease of consumption

for added information and betrays a misunderstanding of the task

at hand.

First, derived data contain no novel information. They are

deterministic functions of facts data. As long as the underlying

facts can be exported, every forecast, EOQ, “service level,” or

ABC tag can be recomputed outside the original system. More-

over, CRUD ledgers are culturally and technically hostile to heavy

computation. Sophisticated analytics belong in environments built

for them; recomputing those ﬁgures in a non-CRUD setting is

both simpler and more reliable than bending the ledger to do the

math.

Put diﬀerently—and to connect the discussion back to informa-

tion—derived data contain zero new bits. They are deterministic

functions of the ﬁrm’s fact data; at best they are lossy compres-

sions, convenient for a human reader. Treating such artifacts as

primary inputs conﬂates representation with knowledge: any un-

certainty they appear to resolve was already resolved—no more,

no less—by the underlying records.

Why, then, do ledgers teem with these tables? CRUD systems

are engineered to keep workﬂows snappy and auditable, not to

perform heavy optimization. Vendors therefore materialize interme-

diate calculations—forecasts, EOQs, safety stocks, ABC tags—so

screens load fast and so, a ledger can posture as “planning”. This

precomputation spreads because it is cheap to store and easy to

demo, not because it adds information.

Second, handling these artifacts is materially harder than work-

ing with primary (fact) data—often by an order of magnitude or

more. Flow events are bounded by physics; artifacts are bounded

Perfect numerical identity would require mirroring the source system’s

ﬂoating-point quirks, rounding rules, and version-speciﬁc bugs. For decision-

making, economic equivalence—not byte-for-byte equality—is what matters;

vanishing numerical diﬀerences do not change the ranking of options nor the

resulting proﬁtability.

5.4. DATA 179

only by storage—eﬀectively abundant. In software products that

conﬂate ledgers, reporting, and “planning”, derived artifacts prolif-

erate until they dominate the schema. It is common to see derived

tables outnumber fact tables ten-to-one. The consequence is pre-

dictable: more volume to move, reconcile, and govern, with little

or no informational gain.

Third, the analytical artifacts embedded in CRUD apps (setting

aside purely operational caches and materialized views) almost

always reﬂect naive, legacy approaches that do not belong in

modern practice. This is not an accident but a consequence of the

CRUD engineering culture: ledgers are built to keep workﬂows

snappy and auditable on top of relational databases, so vendors

gravitate toward recipes that are (a) expressible in SQL, (b) cheap

to precompute, and (c) easy to demo. Forecast tables, economic

order quantities, “service levels”, and ABC tags tick all three boxes;

pricing optionality, cross-SKU capital arbitration, or risk-adjusted

portfolio decisions do not. The outcome is a steady accumulation of

brittle numbers that freeze simplistic assumptions into the ledger,

blur accountability (which ﬁgure is authoritative—the artifact or

the facts that generated it?), and mislead downstream eﬀorts by

masquerading convenience as knowledge. Had the authors aimed

at economically sound decision-making rather than screen speed

and sales collateral, these artifacts would never have been mixed

into the system of records in the ﬁrst place.

Not every statement about the future is derived data. Some

future-dated entries are themselves facts: decisions or commitments

that bind the ﬁrm or its partners. For example, if the marketing

department has formally approved a budget for an advertising

campaign starting early next year—complete with timing, regional

splits, and channel allocations—this entry is not computed from

other records; it is a new constraint that changes cash ﬂows and

capacities regardless of what demand later reveals. The same

applies to eﬀective-dated price changes already agreed with a

supplier, planned store openings or closures with ﬁrm dates, or

a regulatory change whose enforcement date is ﬁxed. Such items

should be recorded as facts, with clear provenance, eﬀective/expiry

180 CHAPTER 5. INFORMATION

windows, scope, and revocation rules.

More broadly, the habit of treating derived artifacts as inputs to

a system of intelligence reﬂects a planning-centric worldview that

puts planning on a pedestal. Yet planning—and the artifacts it

emits—is a second-class citizen in supply chain. What matters are

the enacted decisions that commit scarce resources. Intermediate

computations such as time-series forecasts, especially when they

support far-oﬀ choices like next season’s replenishments, do not

deserve equal billing: they are provisional and will be revised

repeatedly before any cash or atoms move.

Systems of intelligence exist to issue decisions—purchase or-

ders, allocations, prices, schedules—that commit scarce resources.

While exploring the option space, the engine produces a trail of

intermediate calculations (scores, simulations, scenario logs, pro-

visional forecasts). These are derived data: they contain no new

information beyond the inputs and the code. Useful for auditability

and diagnostics, they are transient technical by-products, not mas-

ter data. The only durable outputs are the decisions themselves,

which must be written back to the system of records through a

narrow interface. Those entries are not “artifacts”: they are new

facts—commitments that the ﬁrm will execute and that will later

materialize as receipts, movements, and sales.

Consequently, a system of intelligence should consume authori-

tative facts from systems of records (and possibly a few external

feeds with clear provenance), not the frozen artifacts embedded

in ledgers or reporting tools. If a summary is needed, it should

be recomputed inside the engine from the underlying facts; ingest-

ing precomputed artifacts merely imports someone else’s assump-

tions—and their information loss.

5.4.6 External data

By external data we mean signals that do not originate in, and can-

not be reconstructed from, the company’s own systems of records.

They must be obtained from outside—public websites, oﬃcial re-

leases, third-party sensor feeds, or commercial data brokers. If the

5.4. DATA 181

bytes can be derived solely from internal ledgers, they are not exter-

nal; they are internal or derived data. Exploiting external data can

create value, but acquisition, cleaning, and upkeep costs are high

and often erode returns. As a rule of thumb, exhaust the economic

value of internal data ﬁrst; the recurring exceptions are competitors’

posted prices and a few highly curated, industry-speciﬁc datasets.

The chief obstacle with external data is the cost of acquiring,

cleaning, and keeping it current. Unlike the ﬁrm’s own ledgers,

external feeds are irregular, partially structured, and seldom aligned

with internal schemas or refresh cycles. Internal records tend to

scale with the ﬁrm; external sources do not. When external data

prove worthwhile, it usually falls into three recurring families: (1)

competitive intelligence—rivals’ posted prices, assortments, and

terms—which directly modulate demand and constrain pricing; (2)

weather observations and short-range forecasts, which can inform

near-term allocation and scheduling; and (3) social-media exhaust,

attractive in theory but typically unrewarding in practice.

Each family brings distinct frictions: pairing and normalizing

items across catalogs for competitive data; locality, horizon limits,

and heavy volumes for weather; and noise, manipulation, and weak

causal links for social media. The next subsections take them in

that order, starting with competitive intelligence.

Competitive intelligence

Competitive intelligence, in the supply chain sense, means har-

vesting rivals’ public signals—posted prices, assortments (styles,

colors), and terms (warranties, delivery promises)—and folding

them back into our own decisions. It is ﬁrst and foremost an

external data problem: these signals do not live in our ledgers;

they must be acquired, matched to our catalog, cleaned, and kept

current.

Economically, the link is direct: price rations scarce goods

and coordinates substitution. Small price moves—ours or a ri-

val’s—shift both total volume and market share, sometimes abruptly

once retaliation begins. Thus, to allocate capital and inventory

182 CHAPTER 5. INFORMATION

rationally, the supply chain must track competitors’ prices, assort-

ments, and terms and let these signals inform its decisions.

Before the late 1990s, gathering competitors’ posted prices

was tedious clerical work. The spread of e

commerce in the 2000s

made prices readily accessible on public sites. Even professional

goods once hidden behind requests for quotes now surface, because

a single reseller can publish the ﬁgure. Consequently, a cottage

industry of web

scraping experts—and tools to parse messy pages

while impersonating human visitors

—emerged during the 2000s.

Today, companies can cheaply buy extensive datasets, refreshed

several times a day, that map competitors’ assortments. Exploiting

such data is still laborious; several hurdles remain.

Competitive

intelligence data are far noisier than the company’s

internal records. Web scrapers lean on many heuristics, and these

can occasionally misread a value

. Even with high overall quality,

wrong values must still be detected and neutralized.

In most markets our catalog does not map 1-to-1 to a rival’s.

Even when a competitor sells the “same” article, its SKU, pack

size, color set, or included accessories diﬀer. Pairing is therefore

a matching problem, not a lookup. A workable approach pro-

ceeds in three steps: (1) generate candidate pairs using cheap

signals (shared manufacturer references, GTIN/EAN/UPC, nor-

malized titles, salient attributes such as size/volume/power rating,

and—when available—image embeddings); (2) score each candidate

with multiple independent tests—text similarity, attribute com-

patibility, price-per-unit coherence, and bundle/variant rules—to

An arms race happened in the 2010s between publishers—possibly operators

of large marketplaces—and scrapers, the former trying to prevent the latter.

However, for all practical intents and purposes, through the use of headless

browsers, proxy networks, and more recently LLMs (large language models),

the scrapers emerged victorious. Considering the evolution of the software

technologies, there is little reason to ever expect any lasting reversal of this

situation.

Competitive intelligence tools also frequently reveal that competitors them-

selves occasionally face pricing glitches, with a low percentage of their own

prices being bogus, either due to some “fat ﬁnger” clerical error, or due to bugs

in the e-commerce front end itself.

5.4. DATA 183

produce a conﬁdence score; (3) retain the full matching graph

(one-to-many and many-to-one) with timestamps rather than a

single “best” link, because assortments drift and the correct pairing

can change over time. Modern language and vision models make

step (1) easier, but they do not remove the need for steps (2) and

(3). Without an auditable pairing layer—complete with versioned

rules and conﬁdence thresholds—downstream price comparisons

will be numerically precise and economically wrong.

Data volume is not the only concern; persistence and prove-

nance are. Competitive feeds are not “one row per SKU per rival

per day”. To remain auditable and economically useful, the ﬁrm

must retain: (i) every raw observation (timestamped URL, page

or API snapshot, and the parser version used), (ii) the full match-

ing graph with per-edge scores and timestamped rule versions,

(iii) normalization rules and unit conversions as they were at the

time, and (iv) every detected change (price, availability, variant

set, bundle composition). Prices can move several times a day;

marketplaces expose multiple seller-speciﬁc oﬀers; variants and

accessories appear and vanish. Collapsing this volatility to a single

“daily price” discards exactly the signal we need for allocation and

pricing.

Practically, the competitive corpus often exceeds the ﬁrm’s

own transaction log by 10–100

in rows, and by far more in bytes

if raw HTML/JSON snapshots are archived for traceability.

Modern hardware digests the volume; the real cost lies in the

plumbing: ingestion pipelines resilient to site changes, anomaly

detectors to neutralize scraper glitches, periodic re-matching jobs

as assortments drift, and quality assurance ﬂags that quarantine

dubious observations without halting the feed. Neglect any of these

and the dataset will be numerically dense yet epistemically thin.

Overall, competitive intelligence is consequential and aﬀordable

Archiving raw pages or API payloads is indispensable for audits and parser

regression tests. It also guards against silent upstream changes. Observe the

target site’s terms of use and applicable law; rate-limit scrapers and respect

robots.txt directives unless a speciﬁc legal basis and business necessity justify

otherwise.

184 CHAPTER 5. INFORMATION

enough to merit inclusion in most supply chains. However, if a

project can launch without competitive data, add that feed later,

once the core recipe is live.

Weather data

Many categories—apparel, beverages, home and garden, automo-

tive accessories—are weather-sensitive. Because weather moves

demand, it is tempting to let forecasts steer the supply chain. The

right question, however, is not whether to use weather data but

when it truly informs a decision. Two practical axes determine the

matter: the decision horizon (hours, days, weeks) and the spatial

granularity at which the ﬁrm acts (store, city, region). A forecast

carries useful information only when its horizon and locality match

the decision’s. In practice, two hard constraints limit the payoﬀ of

weather-powered decisions.

Horizon: beyond roughly ten days, modern forecasts regress

toward seasonal averages. From the empirical rules of the early

century to today’s high-resolution satellite imagery, steady

progress has improved forecasts, yet they remain a resolutely

short-term aﬀair. Consequently, choices with a look-ahead beyond

ten days are no better informed by weather feeds than by plain

seasonality measured on the ﬁrm’s own history.

Locality: Weather is intensely local. Conditions can diverge

sharply across places separated by only a few kilometers—or even a

few hours—while large ﬁrms sell into and operate across hundreds

of thousands—or even millions—of square kilometers. A weather

signal helps only if it is available—and mapped to our assets

and customers—at the same spatial granularity at which we can

act (store, route, city, or region) and within the relevant lead

time. Achieving this match requires high-resolution grids and

dense historical reanalyses to calibrate the statistical link between

weather and demand. Covering the next ten days plus several years

of history quickly yields datasets tens of gigabytes in size—often

an order of magnitude larger than the ﬁrm’s own transaction log.

Modern hardware digests the volume, but the plumbing is non-

5.4. DATA 185

trivial. When decisions are taken at a coarser level (e.g., regional

allocations), ﬁne-scale signals largely average out, shrinking the

payoﬀ.

Furthermore, weather is not a one-dimensional phenomenon.

Typical parameters include temperature, humidity, wind speed

and direction, precipitation, pressure, visibility, and cloud cover.

Depending on the goods, some parameters prove irrelevant, yet

identifying them still takes work. For electricity demand, temper-

ature often suﬃces, whereas other products hinge on a diﬀerent

mix of weather factors.

Finally, asymmetric risks in a given supply chain decision can

undermine the beneﬁts of weather-enriched forecasts. Let’s consider

women’s razors. In Western countries, those products follow a sharp

quasi-seasonality: demand surges in the week just before the ﬁrst

hot spring weekend. Otherwise, demand stays low for the rest of

the year. For a general-merchandise store, the wiser move is to

avoid last-minute, just-in-time replenishment the moment a hot

weekend looms. If the forecast is wrong, replenishment happens

too late, and the store has missed its once-a-year opportunity to

sell those products. A sound, risk

adjusted choice is to replenish

in early spring. There will almost certainly be a ﬁrst hot spring

weekend; the uncertainty is its precise date. Keeping a pack of

small, nonperishable products on the shelf a few extra weeks is far

cheaper than missing the surge because of a bad forecast.

Weather helps only where its signal survives the two hard

constraints already noted: horizon (roughly ten days) and locality

(the unit at which the ﬁrm can act). Outside that window, the

physics of forecasting and the plumbing costs dominate, while

seasonal patterns and risk-adjusted policies carry more weight.

Tooling may soften these frictions later, but for now, weather is

an adjunct—not a foundation—for planning.

Social media data

During the 2010s, social-media data fascinated executives more

than any other external source. As millions of consumers began

186 CHAPTER 5. INFORMATION

voicing desires and opinions on social media, a company able to

continuously reﬁne its oﬀering on that information could outcom-

pete its rivals. Instead of relying on sales data observed with

weeks of delay, the hope was to capture market sentiment in real

time. Moreover, social media would hold information beyond the

company’s current oﬀering, providing clues to guide new-product

introduction.

Yet social-media data never delivered the riches vendors promised;

the expected beneﬁts did not materialize. Indeed, social-media

data are very challenging to exploit because it is extremely—and

often maliciously—noisy. Sock-puppet accounts abound; genuine

users troll or manipulate; inﬂuencers wax and wane. Inﬂated

audience ﬁgures—views or subscribers—are commonplace. Social-

media platforms have little incentive to purge click-farm traﬃc

that inﬂates those ﬁgures.

Even if quality were higher, matching posts to precise parts of

the company’s oﬀer—at scale and automatically—remains oner-

ous. While it is obvious that public feedback helps design or

engineering teams by surfacing subtle ﬂaws—or, conversely, ar-

eas for improvement—supply-chain feedback is mostly unhelpful.

The company does not need social networks to learn that recent

shipments were delayed; its systems of records—and carrier inte-

grations—already provide that information.

Finally, processing costs remain consequential. Unlike similarly

large datasets such as weather data, which are entirely numerical,

social-media data are largely unstructured, with text and images

representing almost all of it. Recent progress in multimodal LLMs

(large language models) eases processing, yet running them at scale

remains costly and slow.

Among external sources, social-media data exhibit the worst

cost-to-beneﬁt ratio. Extraction is arduous, and the insights are

rarely actionable from a supply-chain perspective. In practice,

A click farm is a setup where a large group of low-paid workers or automated

systems are employed to artiﬁcially inﬂate online metrics, such as clicks, likes,

views, or app downloads, to manipulate the popularity or ranking of digital

content, apps, or social media accounts.

5.5. MUNDANE KNOWLEDGE 187

ﬁrms should skip social

media data for supply

chain work until

every other source is exhausted.

5.5 Mundane knowledge

A common objection to mechanizing decision-making is that people

know many things that are not in the company’s systems of records.

However, while this statement is a truism—the warehouse staﬀ

knows the color of the door handles, knowledge almost certainly

absent from the system—it does not follow that near-complete

automation is impossible. On the contrary, in the public and in

corporate circles, a widespread mysticism endures about keeping

people in the loop, as if the human mind possessed preternatural

qualities no computer could emulate.

Let us start by clarifying the statement people know things

with respect to information versus knowledge. While employees

are certainly knowledgeable about the processes involved in the

ﬂow of goods, they are not necessarily highly informed, especially

about the ﬁne print of the ﬂow’s ever-changing state. The human

mind cannot reliably remember thousands of daily inventory posi-

tions, thousands of customer orders, hundreds of pending supplier

deliveries. Even rudimentary computers vastly exceed the human

mind’s capacity—both in reliability and in speed—to store and

retrieve information.

Today, almost all remaining manual decisions in supply chains

involve a planner who relies solely on information shown by com-

pany systems. When systems of records fall short, employees

reach for spreadsheets—their shadow IT safety net—to assem-

ble the missing information. This widespread practice illustrates

that supply chains are de facto dependent on digital information.

Whether this information is neatly integrated into well-engineered

systems of records or haphazardly spread across a loose collection

of spreadsheets, the case remains: for all intents and purposes, the

information exists only in digital form.

Recall the distinction drawn earlier in the Knowledge sec-

188 CHAPTER 5. INFORMATION

tion between special and mundane knowledge. Special knowl-

edge—formulas, source code, contractual rules—already inhabits

the written realm and thus is a natural feedstock for mechanization.

Mundane knowledge, by contrast, lingers in tacit practices: know-

ing that carrier X never shows up after 4 p.m., that truck “Bessie”

must be coaxed into ﬁrst gear, or that a given supplier forgets the

customs paperwork every second Tuesday. What skeptics of full

automation really fear is that this fuzzy remainder will disappear

once people are “out of the loop.”

The cure is not to renounce automation but to practice progres-

sive codiﬁcation. Whenever an operator relies on an unwritten rule

that alters a material decision, the rule must be surfaced, written

down, and placed under version control exactly like any other piece

of productive code. Over time, the organization assembles a living,

peer-reviewed corpus—a supply-chain playbook—from which engi-

neers can harvest deterministic rules, statistical priors, or training

labels for the forthcoming system of intelligence.

Cultivating such a playbook demands a company

wide written

culture. Meeting minutes, post-mortems, simulation notebooks,

and corrective SQL snippets should be treated as ﬁrst-class arti-

facts, stored in a shared repository for search, review, and refac-

toring. Written words decay slowly; oral traditions evaporate

overnight. The sooner a ﬁrm embraces this discipline, the faster it

converges toward fully informed, machine-executed decisions.

Therefore, the information-based objection to automation does

not stand. However, this does not address knowledge itself—and

the intelligence needed to wield it. This objection can be restated:

given all the necessary information, we still do not know how to for-

malize the transformation of information into decisions. Employees

can do it, but they cannot say how—at least not precisely enough

to permit reliable replication by a computer. This objection has

merit and will be revisited later in the chapter Intelligence.

5.6. BAD DATA 189

5.6 Bad data

Over the last four decades, supply-chain planning initiatives have

been an almost unbroken string of failures, as demonstrated by the

absence of production-grade unattended decision-making processes

in most companies. While this statement will be revisited in greater

detail later, let us immediately note that bad data has become

the favorite excuse. Software vendors, software integrators, and

data scientists alike invariably attribute their lack of results to

inadequate data. Yet, this explanation does not stand up to closer

scrutiny.

Since the late 1990s, all sizable supply chains have been dig-

itized: every purchase, every shipment, every sale, and every

production run is logged in a system of records somewhere. This

does not imply that those systems capture all relevant aspects of

the physical ﬂow, nor that they are free of limitations and other

issues. Yet, for more than two decades, the core transactional

history of large companies has been digital.

Empirically, transactional data held by systems of records

almost invariably exhibit error rates as low as one faulty entry in

a thousand. This accuracy is unsurprising. Maintaining a precise

ledger is a matter of survival for companies: unpaid customer debts

must be recovered, and supplier invoices must not be paid twice.

Routinely failing at those basics either guarantees bankruptcy or,

at best, an acquisition by a better-managed competitor, since those

problems are largely ﬁxable. Occasional clerical errors still slip

through despite safeguards, but they are too rare to meaningfully

aﬀect supply-chain optimization.

Non-transactional data held by systems of records are typically

lower in quality than transactional data, precisely because erro-

neous entries do not systematically trigger an economic sanction.

However, in supply

chain settings, these data are seldom outright

garbage. Indeed, most large companies are disciplined enough that

their records reﬂect daily operations. The main quality issue with

non-transactional data is that entries may be missing if employees

can evade the data-entry process. When the user experience is

190 CHAPTER 5. INFORMATION

poor, skipping data entry may be the only practical way to stay

productive. Yet those ﬁelds go unﬁlled precisely because they are

not critical to keeping the ﬂow running.

In a sense, the very fact that the company keeps operating

proves the data are “good enough”. Decades ago, some divisions

kept paper backups, but such practices have vanished from large

supply chains. If data are not in the company’s systems, it does

not exist. As a result, missing non-transactional data does not

prevent supply-chain optimization; it merely limits its eﬀectiveness.

Furthermore, the same data limits constrain employees performing

the task manually. Few situations exist where the information

in people’s heads suﬃces for optimization. Instead, they rely on

context-free policies that keep the ﬂow running—possibly ineﬃ-

ciently—on scant data. Yet those policies are not exclusive to the

human mind; software can reproduce them.

Let’s consider, for example, a retail network with a warehouse

serving a few dozen franchised stores. Each day, the independently

run stores send orders to the warehouse, expecting next

day fulﬁll-

ment. Yet, the total quantity ordered by the stores on a given day

might exceed the stock on hand in the warehouse. In this situa-

tion, the default policy is simply “ﬁrst

come, ﬁrst

served”. This

naive rule invites gaming: stores overorder whenever they sense

a possible shortage, which only deepens the problem. A better

policy would distribute the remaining stock across the network

rather than granting it all to the earliest claimants. However, such

reﬁnement requires more data, i.e., more context.

These observations cast doubt on the popular claim that “bad

data” sinks most supply

chain projects. After all, most initiatives

amount to ﬂow planning that needs only transactional data—data

that is both available and reliable. An alternative, more con-

vincing explanation is that most technologists—software vendors,

software integrators, in-house data scientists—fail to exploit the

data already sitting in the company’s systems of records. Indeed,

supply-chain books and software alike tend to dismiss the problem:

data preparation is wholly ignored, implicitly treated as a task so

straightforward that it does not deserve any speciﬁc methodologies

5.6. BAD DATA 191

or technologies.

Yet, as detailed in the previous section, systems of records were

never designed for data science and are unlikely ever to be. Large

companies often operate many—sometimes dozens—of function-

ally overlapping systems of records. Such a chaotic application

landscape arises from mergers and acquisitions, from successive

roll

outs that never retire old systems, and from ever

new functional

demands. Thus, data preparation—especially pinning down ﬁeld

semantics—is invariably a huge challenge for any sizable company.

That challenge can only be tackled by supply-chain specialists.

In short, “bad data” often serves as a convenient scapegoat

for plain incompetence. While external sources often suﬀer from

overwhelming quality problems, this is not the case for the systems

of records operated by the company itself.

192 CHAPTER 5. INFORMATION

Chapter 6

Intelligence

Intelligence is the elusive faculty that lets people articulate their

wants and plan the actions needed to reach them. It is self-

evident that supply chain, understood as mastery of optionality,

demands substantial intelligence to execute proﬁtably. Yet a supply

chain run crudely saddles the company with constant, avoidable

overhead—costs sharper resource allocation would spare.

Deﬁnition (Intelligence).

The capacity to make choices that yield superior future

rewards.

In an unhampered market, ﬁrms managed intelligently are

rewarded, while those that are not are weeded out. The market

is remarkable for delivering capabilities its participants do not

explicitly understand. Earlier we saw the market deliver prices;

now we see it delivers intelligent management as well. Those

capabilities are obtained only indirectly—an aggregate property

of the market as a whole. An executive must still rely on his own

intelligence to improve his ﬁrm—speciﬁcally its supply chain.

Understanding intelligence is crucial to supply chain because

much of it can now be engineered and delegated to computing

hardware. In practice, for well over a decade, at-scale, unattended

193

194 CHAPTER 6. INTELLIGENCE

automation of the mundane operational decisions that keep goods

ﬂowing has been within reach. This is not a claim that general

intelligence is “solved”; rather, it is an observation that specialized,

engineered intelligence already delivers superhuman consistency

on narrow tasks and large productivity gains. Today’s ubiquitous

route-planning apps oﬀer quiet yet powerful evidence.

The architectural form of this engineered capability is the

system of intelligence. Before contrasting general and specialized

intelligence, let us examine these systems on their own: how they

arise in a modern supply chain, how they turn raw records into

proﬁtable unattended decisions, and why their design principles

diverge sharply from the CRUD applications dominating enterprise

software.

6.1 Systems of intelligence

Decision-making is mechanized through systems of intelligence.

Unlike systems of records or systems of reports, systems of in-

telligence are engineered as replacements for human operators,

not for man–machine interaction. The purpose of a system of

intelligence is to generate the most proﬁtable decisions, and to

have those decisions made in a fully unattended manner.

For

most companies, relentless automation has been the cornerstone of

proﬁtability. Systems of intelligence extend this principle to tasks

traditionally delegated to the white-collar workforce. Our ability

to engineer a system of intelligence depends squarely on our ability

to mechanize intelligence, whether general or specialized.

Although every corporate function—marketing, ﬁnance, HR,

and so on—will ultimately have systems of intelligence of its own,

supply chain is one of the most promising segments. Any sizable

supply chain demands tens of thousands—sometimes millions—of

decisions each day. Absent systems of intelligence, dozens or

Full automation is also a practical requirement for a dual-run—operating

the new decision engine in parallel with the legacy process—without duplicating

the planning workforce. We return to this point later.

6.1. SYSTEMS OF INTELLIGENCE 195

even hundreds of clerks are needed just to keep goods moving.

By the early 2020s, many retailers and manufacturers employed

more white-collar staﬀ updating spreadsheets than blue-collar

staﬀ handling the goods themselves. Blue-collar roles have been

aggressively mechanized for two centuries—a process still underway.

By contrast, nearly all productivity gains from enterprise software

in the past ﬁve decades have come from systems of records alone.

White-collar headcounts have hardly fallen; rising supply chain

complexity has absorbed the gains.

Yet the ﬁrst inventory control systems, deployed in the 1970s,

were expected to deliver extensive decision-making automation

Those systems almost invariably relied on the safety stock paradigm,

or on minor variants of it: for every SKU, the buﬀer is sized

from a demand forecast, a lead

time input, and a target service

level. The method assumes demand uncertainty follows a normal

distribution. The original expectation was that such a system

could automatically generate inventory decisions, under modest

supervision from employees to update parameters once in a while.

The expectation proved misplaced; the systems remained intensely

labor-intensive.

Yet in the 21

century, after a few decades of stalemate, the

practical automation of mundane supply chain decisions became

possible through systems expressly engineered as systems of intel-

ligence. For supply chain practitioners, the importance of those

systems cannot be overstated: the modern practice of supply

chain is, for the most part, a specialized software undertaking. It

amounts to engineering systems of intelligence dedicated to supply

chain. This fundamental insight is routinely missed by supply

chain authors, consultants, and academics alike. As a result, the

traditional paradigm fails to turn the supply chain practice into a

productive asset.

In any case, those systems were heavily promoted as such by their respective

software vendors.

196 CHAPTER 6. INTELLIGENCE

6.1.1 A productive asset

What if we train employees and they leave? Indeed, but

what if we don’t and they stay? (old business saying)

Before software, ﬁrms had to hire and keep clerks for every

routine decision, no matter how small, mundane, or repetitive. As

decisions multiplied, so did clerks and their managers. A company

could narrow its focus to cut this headcount, but only by shrinking

its oﬀering. In this view, the staﬀ is a pure operating expense

(OPEX): the staﬀ’s workdays are spent and leave no lasting asset

once a decision is made.

Even with training and reﬁned organization, large workforces

have long since reached their competence ceiling. For instance,

APICS

(American Production and Inventory Control Society) was

formed in 1957; its teachings have been stable for decades. Despite

what many authors of the consulting variety still advocate, it is now

unreasonable to expect that revamping the supply chain processes

or organization, while remaining anchored in the pre-software

paradigm, will yield anything but thin returns.

The fundamental proposition behind a system of intelligence

is to turn those operational expenditures into capital expenditures.

Even the man

days invested in building the system improve the

system itself. Operating and maintaining it still costs money,

yet—as software—those outlays are typically a small fraction of

the payroll required to achieve the same results manually. Thus,

through the system of intelligence itself, the practice of supply

chain is reiﬁed as a productive asset. This asset is productive

because it generates ongoing returns for the company, through the

decisions it automates, well beyond its operating costs.

Unlike a clerical workforce that can only be trained to a point,

there is no a priori upper bound on supply chain performance that

can be expected from a system of intelligence. First, it becomes

possible to deliver what amounts to deﬁnitive ﬁxes to certain in-

eﬃciencies or mistakes. Indeed, no matter how well-trained and

It became the ASCM (Association for Supply Chain Management) in 2018.

6.1. SYSTEMS OF INTELLIGENCE 197

how diligent the managers might be, any manual process is bound

to suﬀer occasional clerical errors. Moreover, introducing further

redundancies in manual processes—such as extra validations by

peers or by the hierarchy—is usually counterproductive, as it slows

the ﬂow and adds hurdles and costs. Second, every decision can

be further reﬁned through more data, more accurate predictors,

more eﬀective optimizers . . . with no built

in ceiling on their so-

phistication. Software can already package what would otherwise

require lifetimes of accumulated insights and skills, and this has

been the case for decades

As the maintenance of a system of intelligence is a problem

of general intelligence, no system is yet fully autonomous. Since

the early 2010s, however, a growing number of companies have

run systems of intelligence that keep the mundane execution of

their supply chains largely unattended

. Numerical recipes remain

untouched for weeks, if not months.

6.1.2 Muddy emergence

Since the 1970s, supply chain software vendors have repeatedly

claimed that their products would automate planning; in practice,

these promises did not materialize—the systems shipped did not

make unattended decisions, and for most vendors this remains true

in the mid-2020s.

Genuine systems of intelligence emerged only in

the late 20

century, ﬁrst at scale in quantitative ﬁnance as algo-

rithmic trading platforms—often referred to as “trading algorithms”

or “automated trading systems”. Starting in the 1980s, ﬁnancial

In 2023, the Linux kernel has over 30 million lines of code, while Microsoft

Windows has over 50 million lines of code. Even Android, a mobile operating

system, has more than 7 million lines of code when counting dependencies.

At a minimum, those companies include the corporate clients of Lokad.

Furthermore, while technological giants, like Amazon or JD.com, don’t fully

publicize the ﬁne print of their technologies, their research publications on the

same time period suggest they have also uncovered methods capable of delivering

very high levels of automation, quite comparable to those achieved by Lokad.

Mainstream ERP/APS oﬀerings still delegate decisions to human clerks

and function primarily as systems of records or reports.

198 CHAPTER 6. INTELLIGENCE

institutions employed algorithms to automate trading decisions,

eﬀectively replacing human traders in various market activities.

These systems were designed to analyze vast amounts of market

data, identify proﬁtable trading opportunities, and execute trans-

actions at speeds unattainable by humans. The success of these

systems highlighted the transformative potential of mechanizing

intelligence, setting a precedent for other industries.

However, in other industries—most notably in supply chain—the

confusion between systems of records and systems of intelligence

has persisted since the rise of enterprise software in the 1970s, with

software vendors—aided by market analysts—playing a critical

role in perpetuating it. In a nutshell, systems of records are easy

to design but oﬀer limited returns; systems of intelligence are

hard to design but have vast potential. As a result, most vendors

frame their systems of records as systems of intelligence to inﬂate

their prospects’ willingness to pay. Crude as it is, this marketing

technique remains eﬀective, with companies in the 2020s still pay-

ing high fees for technologies (CRUD apps) commoditized over a

decade ago. Many executives remain convinced that systems of

records contribute to the quality of their business decisions, despite

several decades of contrary empirical evidence.

The value a system of records can deliver is strictly capped,

no matter the quality of the solution. Indeed, once the system

achieves near-perfect reliability and complete functional coverage,

there are no further improvements to be made. For many pro-

cesses—invoicing, inventorying, time tracking . . . —this endgame

was reached more than a decade ago. As having the best accoun-

tant in the world makes no diﬀerence compared with having a great

accountant, the greatest system of records makes no diﬀerence

compared with a great one.

Enterprise software vendors have always been painfully aware of

the inherent limitations of systems of records. Since the beginning,

vendors have focused their discourse on the “better decisions”

companies would make thanks to their software products. For

example, in supply chain settings, vendors invariably claim that

their solutions lead to lower stock levels and fewer stockouts, even

6.1. SYSTEMS OF INTELLIGENCE 199

when the software in fact entirely delegates inventory decisions to

the client company’s staﬀ—as is invariably the case with ERPs.

Vendors selling systems of reports would later follow a similar

approach, touting decisions that were, at best, only indirectly

derived from their reports.

The crux of the technological challenge is that neither a system

of records nor a system of reports can practically be extended into

a system of intelligence. In defense of the early software vendors

of the 1970s, the reasons weren’t readily apparent at the time, and

thus the vendors themselves most likely believed that their early

systems of records would, in time, encompass decision-making

processes. Yet by the end of the 1990s, dozens of notable failed

attempts had demonstrated that such a transition would not take

place.

6.1.3 Culture and incentives

Automating supply-chain decision-making did not arise from the

enterprise-software pioneers that grew into giants in the 1990s.

This is doubly surprising: those vendors not only surveyed supply

chains extensively—through their systems of records—but also re-

lentlessly promoted their capacity to improve those very processes.

Understanding why those vendors failed to deliver systems of intel-

ligence for supply chain matters, because companies modernizing

their supply chains will face the same problems.

One might argue that late 20

century software was lacking

and vendors were overly optimistic; that proposition is questionable.

Indeed, the same vendors—primarily ERP and CRM vendors—still

had not pivoted a decade after it became clear that some compa-

nies had mechanized supply-chain decision-making; they lacked

designs that would make such automation possible. Thus, while

technological barriers existed, they were not the primary hurdle.

The ﬁrst problem is incentives. Vendors selling systems of

records generally align fees with user counts; even fees not tied

to headcount—e.g., support—track it in practice. As a result,

vendors are incentivized to add traits that increase headcount and

200 CHAPTER 6. INTELLIGENCE

disincentivized from steering products toward reducing it. Systems

of intelligence, whose explicit aim is to automate decision-making,

run directly counter to that agenda.

Thus it is no surprise that vendors of systems of records—often

highly proﬁtable—have little reason to fund technologies that

could erode today’s cash cows. If economics teaches one lesson,

it is that incentives matter. If the organization’s proﬁtability

depends on a breakthrough not occurring, visionaries within it are

inconsequential

The second problem is engineering culture. As noted, systems of

records are developed almost exclusively along the CRUD paradigm.

Compared to alternatives, CRUD demands far less engineering skill

to maintain and extend an app. This has become a cornerstone

of workforce management for vendors of systems of records. As a

result, they can run larger teams while reducing dependence on

engineering talent. By contrast, systems of intelligence invariably

involve hard engineering challenges, scarcely comparable to CRUD

trivialities. Unsurprisingly, a workforce that has dealt almost

exclusively with “easy” problems—often for a decade or more—is

reluctant to tackle genuinely diﬃcult ones. As anecdotal evidence,

the near-totality of computer-science breakthroughs in the last two

decades came from consumer software companies

, while notable

contributions from enterprise vendors of systems of records are

hard to name.

Therefore, when rolling out a system of intelligence, a company

should avoid vendors specializing in systems of records. Because

systems of records are deployed ﬁrst, managers are invariably

Numerous examples of such companies can be found in history. For example,

Kodak was a pioneer in photography and had developed many patents related

to digital cameras. Kodak employee Steven Sasson developed the ﬁrst handheld

digital camera in 1975. Larry Matteson, another employee, wrote a report in

1979 predicting a complete shift to digital photography would occur by 2010. Yet

they hesitated to embrace digital photography, fearing cannibalization of ﬁlm.

That reluctance hastened their decline and culminated in a 2012 bankruptcy as

competitors seized the digital market.

For example, cloud computing was popularized by Amazon, deep learning

by Google.

6.1. SYSTEMS OF INTELLIGENCE 201

tempted to turn to the incumbent vendor that has already proved

trustworthy. Unfortunately, this trust is misplaced: the traits

that make a vendor succeed at systems of records are wholly

counterproductive for systems of intelligence.

6.1.4 Conﬂicting design requirements

Beyond incentives and culture, attempts to extend systems of

records to encompass decision-making are largely doomed by con-

ﬂicting design requirements. Performance, reliability, and main-

tainability matter to both camps, but what those notions entail

varies drastically.

As we later explore technologies for supply-chain systems of

intelligence, it will be clear that such a system cannot meaningfully

grow out of a CRUD application and is best developed separately.

Even basic considerations reveal how wide the gap is between

systems of records and systems of intelligence.

Performance

A system of records must respond at low latency: whatever the

input or screen, actions should ﬁnish within milliseconds—ideally

under 100 ms—to remain imperceptible; beyond that, productivity

degrades across mundane operations. Although 100 ms sounds

fast, queuing ensures that a system averaging that ﬁgure will still

inﬂict multi-second delays—latencies users notice and resent.

Therefore, in a system of records, every operation must ﬁnish in

(eﬀectively) constant time within a ﬁxed resource budget. Because

all operations compete for the same transactional core, any “fat”

operation will eventually starve the system and degrade latencies

across many operations. With thousands of features, even one

exception is risky. Today, most companies with “slow” business

systems suﬀer the predictable consequences of fat operations that

should never have been implemented in the system of records.

It is easy to introduce such an expensive operation inadver-

tently. For example, summing an unbounded number of lines—e.g.,

202 CHAPTER 6. INTELLIGENCE

computing a one-year moving-average revenue for a product cate-

gory—already qualiﬁes as problematic. Yet it is a basic piece of

descriptive statistics. Readers versed in algorithms may object that

most descriptive statistics can be maintained and retrieved within

a constant resource envelope. While true, such sophistication is

impractical

. First, CRUD’s virtue is that it avoids requiring algo-

rithmic expertise. Second, at scale across thousands of features,

performance woes creep in. Avoiding these operations altogether

is the only scalable safeguard.

A system of intelligence faces a very diﬀerent performance

landscape. Consider deciding when and how much to order from an

overseas supplier. Although the decision is time-sensitive, spending

minutes—or even hours—on an optimization that yields a better

order is sensible. Imposing a 100 ms cap on a decision that will

take weeks to unfold makes no sense. The point of systems of

intelligence is to generate the best decisions the company can

aﬀord to engineer. So long as computational spend is dwarfed by

marginal gains, expending more is reasonable.

Reliability

Reliability means something very diﬀerent for systems of records

than for systems of intelligence. If a system of records goes down,

any dependent process immediately stalls. For example, picking

stock may not proceed if the inventory system is down. Thus, when

engineering a system of records, it is critical to keep it operational

at all times. Because CRUD applications isolate concerns, most

bugs are local—they aﬀect only one screen or process. Keeping

the system of records running despite an identiﬁed issue is usually

preferable to shutting it down until the bug is ﬁxed. In fact, it

should be possible to patch the system while operations are still

in progress, as the resolution of a local problem should not be

detrimental to the rest of the company. This capability—hot-

patching—is generally considered technically diﬃcult. However,

Modern relational engines oﬀer extensive mitigations; nevertheless, engineer-

ing complex operations under a constant resource budget remains challenging.

6.1. SYSTEMS OF INTELLIGENCE 203

the CRUD paradigm, via the relational database engine, largely

delivers hot-patching. Thus, in practice, software engineers devel-

oping a system of records rarely require expertise in distributed

computing to achieve near-perfect uptime.

By contrast, when a system of intelligence is suspected of being

defective, prudence dictates shutting it down and diagnosing before

resuming. Corrupted decisions can be hugely costly. For example,

a vastly oversized order to an overseas supplier all but guarantees

stock depreciation, if not an inventory write-oﬀ.

Consequently, in a system of intelligence, the economic quality

of decisions matters more than uptime. When conﬁdence drops,

the engine should halt—deliberately and fast—based on explicit

heuristics; this avoids expensive mistakes and is usually the most

proﬁtable choice. These halting heuristics are not the “alerts” and

“exceptions” emitted when a system of records is stretched into

a decision tool. CRUD designs cannot encode the ﬁne print of

nontrivial decisions, so edge cases proliferate: basic replenishment

logic fails on launches, cannibalization, competitor promotions,

and other routine shocks. Yet shutting the application down is

unacceptable—edge cases are frequent enough that the tool would

be down most of the time, and, being also the system of record,

stopping it would paralyze operations. The tool therefore logs

an exception and pushes the case to a human. A true system

of intelligence does the opposite: the decision engine stops, and

operations resume only after a programmatic ﬁx—a change to the

logic—has been deployed; hand-clearing cases is not an option.

Maintainability

The two ﬂavors also diﬀer sharply in their stance toward maintain-

ability. In a system of records, once new entities are added, clients

depend on them: data has been stored and processes now rely

on it. Trouble starts when entities overlap and become unmain-

tainable. Overlaps create unﬁxable confusion—entities cannot be

removed or even truncated. They also create conﬂicts when further

developing the software. Similar situations occur when entities are

204 CHAPTER 6. INTELLIGENCE

ill-deﬁned, ambiguous, or when they otherwise compromise the

semantic integrity of the collection of entities. Thus, for a system

of records, maintainability is chieﬂy a matter of carefully vetting

entities and the behaviors overlaid on them.

Also, in a system of records, sheer codebase size is not, by itself,

a maintainability problem. Many such systems hold thousands

of entities across tens of thousands of tables. Larger codebases

impose more overhead to extend the product, but do not jeop-

ardize continuity. Furthermore, a hypothetical complete rewrite

taking years is acceptable. While not desirable, it is not a prime

concern: no situation calls for an immediate complete rewrite. If

the business extends in a direction unforeseen by existing systems

of records, it is acceptable to set up a new system to supplement

them. Integration may prove challenging, but nowhere near as

challenging as a complete rewrite of the older systems.

In contrast, a system of intelligence is maintainable if, once

confronted with unprecedented conditions, it can be re-engineered

quickly to resume operations. Such conditions can stem from a

drastic strategic shift, a sudden market change, or any factor that

fundamentally alters the ﬂow. A system of intelligence should

handle mundane variations, but a specialized intelligence cannot

be expected to handle radical disruptions. That requires general

intelligence, which is not readily available in computerized form.

For example, during the global panic of 2020, faced with a sharp

decline in passenger traﬃc, some aviation MROs stopped repairing

certain rotables (repairable components) and stored unserviceable

items to preserve cash. Repairs may cost a third of the item’s price;

with excess serviceable stock due to reduced traﬃc, delaying those

expenditures makes sense. Before 2020, storing unserviceable items

was not considered viable, and there was typically no process to

opt out of repairs. All rotables eligible for repair were repaired by

default. Unprecedented market conditions invalidated this policy.

It would have been unreasonable to expect a system of intelligence

engineered prior to 2020 to respond adequately to the lockdowns

of that year. Systems in production had to be extensively modiﬁed

to accommodate the new reality.

6.1. SYSTEMS OF INTELLIGENCE 205

Because new conditions can demand a full rewrite of the decision

logic, that rewrite must remain feasible. Practically, the team

should draft a rough rewrite within hours and a polished one

within days. Between such extremes, when a halting heuristic

triggers, it should be possible to investigate and clear the case

within hours—possibly less than an hour. This implies an engineer

must always be capable of jumping into the core logic to assess

and, if necessary, ﬁx problems on the spot.

Therefore, the codebase of a system of intelligence should stay

small and clear—ideally manageable by one engineer. Assume the

existing logic will eventually encounter conditions that exceed its

capacity to produce meaningful decisions. When this happens,

there is no alternative but to resort to general intelligence from

a capable employee to re-engineer logic for the new reality. In

essence, maintainability for a system of intelligence is the capacity

to rewrite, more or less completely, the system in the event of

disruption.

6.1.5 No problem without solution

Systems of intelligence live or die by how they frame the business

game they are asked to play. Even a ﬂawless optimizer that

pursues the wrong objective destroys value. Hence, before we let

software reason on our behalf, we must ﬁrst clarify exactly what it

is supposed to reason about. This undertaking proves surprisingly

diﬃcult.

From primary school through university, students are presented

with “problems” to be solved. Many—if not most—grades depend

critically on students’ capacity to ﬁnd solutions to those problems.

The approach persists less because of pedagogical merit than

because it gives institutions an easy, ostensibly objective way

to grade students. However, this problem-solving mindset is deeply

ingrained in our industrialized culture and has at least two notable

consequences. These consequences matter greatly for supply chain

and, more generally, for truly diﬃcult undertakings.

First, there is a quasi-instinctive reluctance to think about

206 CHAPTER 6. INTELLIGENCE

“problems”. Academic institutions train and reward solution-

ﬁnding, not the invention of problems. After two decades inside

those institutions, this pattern becomes deeply ingrained in most

of us. As a result, we often struggle to articulate a problem unless

a solution is already in view. Thinking about solutions comes

naturally—we have spent thousands of hours doing it—whereas

thinking about the problems themselves is, for most, rarely prac-

ticed.

Consequently, major supply-chain problems are often ignored

by practitioners and authors simply because no ready-made solu-

tion is at hand. Examples include price breaks, cannibalization,

adversarial behavior, model uncertainty, and optimization under

uncertainty—all consequential issues that today’s supply-chain

discourse largely sidesteps. Companies address these problems

indirectly through corporate policies—operating recipes that keep

the business moving—but rarely state explicitly the precise prob-

lem each policy intends to solve. For a system of intelligence,

this oversight is fatal: the code will faithfully optimize yesterday’s

question while the market has already switched games.

Second, challenging the problem statement itself is instinctively

perceived as challenging authority. Indeed, after two decades in

academic institutions, every student knows that answering it doesn’t

matter to a posed question guarantees a bad grade. Yet too often

that answer is nonetheless the appropriate one. Careful selection

of which problems to address is fundamental. Resources—time,

computing power, people—are always limited, so we cannot tackle

every conceivable problem. One of the entrepreneur’s most critical

skills is wisely choosing which problems he decides to solve. This

perspective radically diﬀers from employees’, who are usually tasked

not only with resolving a given problem but also with following a

codiﬁed method embedded in corporate policies.

Both academia and business maintain incentives that discourage

questioning the problem statement itself. In universities, publica-

tion, grant money, and student recruitment all depend on staying

within oﬃcially sanctioned research ﬁelds. Scholars may pursue

any “relevant” topic—provided it ﬁts the canon of their discipline.

6.2. GENERAL INTELLIGENCE 207

Max Planck captured this dynamic in his Scientiﬁc Autobiography

(1950): A new scientiﬁc truth does not triumph by convincing its

opponents and making them see the light, but rather because its

opponents eventually die and a new generation grows up that is

familiar with it.

In companies, each problem’s solution is embodied in an org-

chart and a budget line. Questioning the problem therefore threat-

ens the status quo and creates managerial winners and losers.

Politics are unavoidable in large ﬁrms and present a major hurdle

to reframing the problem. Corporate history shows that organiza-

tions, like people, die—and refusing to confront new problems is a

prime cause of decline.

Hence, the ﬁrst deliverable in a system-of-intelligence project is

not code but a clear, testable statement of the economic problem.

Only after this statement survives adversarial scrutiny should opti-

mization, forecasting, and data wrangling begin. With engineering

and cultural groundwork in place, we can broaden the lens and

revisit intelligence per se—ﬁrst in its universal form, then in the

specialized varieties that supply-chain work handles daily.

6.2 General intelligence

Systems of intelligence contain narrow, software-bound reasoning,

whereas general intelligence is problem-agnostic and need not excel

at any single task. Its hallmark is the ability to reﬁne both the

framing and the solving of problems. Riddles and tales—tools for

developing intelligence in children and adults alike—have accom-

panied mankind since the dawn of time. A riddle is an exercise

intended to sharpen the mind for its own sake. Tales play a similar

role but leverage our emotional response to facilitate memorization.

Deﬁnition (General Intelligence).

The capacity to intentionally improve intelligence itself.

Writing, invented over 5,000 years ago, is best seen as an

208 CHAPTER 6. INTELLIGENCE

ampliﬁer of human general intelligence rather than its cause. It

externalizes memory: perspectives, insights, and facts can be

recorded, critiqued, and recombined long after their authors’ deaths.

The printing press—and, much later, the web—lowered copying

costs and expanded coordination, accelerating this feedback loop.

Yet more storage does not by itself produce understanding or

judgment. A gram-sized device can hold many lifetimes of literature

and still be entirely unintelligent; memorization is not intelligence.

Intelligent animals such as orcas and chimps transmit cul-

tural know-how—hunting or foraging tricks—to their young. They

demonstrate notable intelligence, yet general intelligence eludes

them. Contrary to past philosophical theories, no single trait

appears uniquely human. Complex language, named individuals,

humor, tools, formal logic, and theory of mind

all exist in lesser

form in the animal kingdom. The shift from intelligence to general

intelligence is therefore a loose threshold—many mental faculties

must cross a quantitative line before a qualitative leap occurs.

Until the early 2020s, deliberate self-improvement of general

intelligence was strictly human and decidedly artisanal. The mind

resists “engineering” in the sense of limitless, reproducible upgrades.

Millennia of pedagogy have produced many techniques, yet no one

expects breakthrough in this area, at least not in the foreseeable

future

. A new language, linear algebra, or thermodynamics

cannot be mastered overnight; disciplined study remains the only

path.

By the 2010s, specialized intelligences could self-improve in

narrow arenas—most famously Go

. The early 2020s brought a

watershed: large language models whose largest instances display

A theory of mind reﬂects the understanding that others’ beliefs, desires,

intentions, emotions, and thoughts may be diﬀerent from one’s own.

Mind-enhancing drugs are a favorite science-ﬁction trope—Dune’s Sapho

juice, for instance—but no such known compound rivals anabolic steroids’ impact

on muscle.

Silver et al., Mastering the Game of Go without Human Knowledge (2017).

AlphaGo Zero trained against itself and beat its predecessor 100-0.

6.2. GENERAL INTELLIGENCE 209

a discernible spark of general artiﬁcial intelligence

. Like the

printing press or the web, these models expand humanity’s ability

to reﬁne its own intelligence.

With these foundations in place, we can clarify “general intelli-

gence”. The next three subsections tackle the subject from diﬀerent

angles. First, we ask when a question becomes a general-intelligence

problem—one whose option space deﬁes formal bounds. Second,

we look at clerical roles in supply-chain teams to show why they

need specialized, not general, intelligence and are therefore ripe

for automation. Third, we assess large language models—today’s

strongest bid for engineered general intelligence—and sort real con-

tributions from hype. Together, these lenses turn a philosophical

idea into operational guidance for modern supply chains.

6.2.1 General intelligence problems

General-intelligence problems are inherently open-ended. Each

solution merely deepens our understanding and forces a new formu-

lation, which then demands a fresh solution. Breaking this cycle

is never ﬁnal: stopping points are partly arbitrary—a pragmatic

truce set by today’s economics, tooling, and attention—until reality

forces a new formulation. Such problems may feel subjectively rare

because, once named, most “problems” are promptly wrapped in

formal boundaries to make them tractable. However, nearly all

business problems, in their naked form, are general intelligence

problems.

Deﬁnition (General Intelligence Problem).

A general intelligence problem arises when options have

no formalizable boundaries.

Entrepreneurial proﬁt-seeking is a clear example of a general-

intelligence problem. A ﬁrm’s capital, time, and managerial at-

Bubeck et al., Sparks of Artiﬁcial General Intelligence: Early experiments

with GPT-4 (2023).

210 CHAPTER 6. INTELLIGENCE

tention are ﬁnite; the ways they can be deployed are eﬀectively

unbounded and continuously shifting. At every moment, the

entrepreneur must decide whether to commit to the best cur-

rently known option or to expend resources searching for a bet-

ter one—knowing the search itself consumes those same scarce

resources. Because the option space shifts as technologies, com-

petitors, and regulations change, no ﬁxed formalization closes the

problem. Entrepreneurship therefore belongs squarely to general

intelligence. History shows entrepreneurs repeatedly redeﬁning—or

inventing—industries their predecessors never imagined.

By contrast, the vehicle-routing problem—choosing optimal

routes for a ﬂeet that must serve given customers—is the archetype

of a bounded problem. Solving it may be hard

, but it does not

require general intelligence. Any solution must obey the formal

graph-theoretic statement of the task. The option set is ﬁnite—even

if astronomically large—and every candidate shares the same shape:

a permutation of stops (and timings) evaluated under a ﬁxed cost

model; no genuinely surprising move ever appears.

Every problem begins as a general-intelligence mess; turning

it into a tractable “bounded” problem is itself an act of general

intelligence

. The key question is whether the chosen formaliza-

tion is adequate. Academia may catalog puzzles indeﬁnitely, but

businesses must pick one framing, because each resource can be

allocated only once. We return to this methodological point later.

Conversely, any bounded problem can be lifted back into a

general-intelligence problem by changing the frame that made it

“bounded”—a move sometimes dismissed as “cheating” because it

violates the original rules. In vehicle routing, the textbook problem

The vehicle routing problem is known to be an NP-hard problem. In

computer science, NP-hard problems are a class of problems that are jointly

conjectured to be strictly more diﬃcult to solve than any given polynomial-time

algorithm. Exhibiting a polynomial-time solution for any NP-hard problem

would refute the conjecture, proving that all NP-hard problems can, in fact, be

solved in polynomial time.

One can also do this unintelligently by inventing arbitrary variables and

constraints, then applying standard algorithms—a pattern common in academic

supply-chain papers.

6.2. GENERAL INTELLIGENCE 211

assumes a ﬁxed set of customers, service promises, ﬂeet, and prices.

The moment we renegotiate any of these—drop or defer remote

deliveries, price them diﬀerently, add pickup lockers or a parcel

carrier, switch vehicle types, widen delivery windows, or even make

delivery unnecessary altogether (as streaming did to cassettes)—the

original problem evaporates. We have not found a smarter route;

we have changed the game. That reframing—altering constraints,

commitments, or payoﬀs—is precisely the work of general intelli-

gence.

Earlier chapters showed that several supply-chain challenges

are genuine general-intelligence problems. Three stand out. (1)

Selecting the economic model, which deﬁnes how actions translate

into costs, revenues, risks, and constraints—the score we optimize.

(2) Deﬁning the option space: the set of admissible actions, tim-

ings, and quantities the ﬁrm is prepared to consider (a decision

is a selection from this set, not the set itself). (3) Engineering

the numerical recipes that, given data, choose decisions within

that option space to maximize risk-adjusted returns while hon-

oring constraints. Because adversarial behavior—by competitors,

employees, suppliers, customers, or vendors—constantly pushes

against boundaries, only general intelligence can tackle these issues.

The story of suppliers exploiting a weak ERP to ship excess stock

is one such subversion.

Much published supply-chain work remains rigidly attached to

bounded formulations and leaves the general-intelligence design

choices—objective, constraints, and option space—implicit. This

does not make such problems intractable. Practically, surface

those hidden choices explicitly—state the economic objective in

monetary terms, enumerate admissible options, and deﬁne halting

conditions—and then decompose the work into bounded subprob-

lems a machine can solve. The next sections show concrete tactics

for doing so and for iterating when reality pushes back. The

program is necessarily incomplete—general-intelligence problems

cannot be “closed”—yet it is suﬃcient to yield unattended, prof-

itable decisions.

212 CHAPTER 6. INTELLIGENCE

6.2.2 Clerical jobs in supply chain

Supply-chain challenges demand substantial general intelligence. A

persistent misconception holds that routine decisions must be held

to the same standard. This is not the case; they only require spe-

cialized intelligence—structured processes that can be engineered

and handed to software.

The misconception stems from outdated practices. In many

ﬁrms, clerks still decide, say, whether to raise a replenishment order.

They do apply policy with intelligence, but general intelligence is

scarcely involved—on closer inspection, almost none.

These policies are usually slight twists on 1970s-era practices

Clerks seldom improve the fundamentals; they merely execute and

maintain the rules. Even parameter upkeep—tweaking service

levels, for instance—is a specialized, rule-bound task. General

intelligence again plays no part.

While better reporting tools raised productivity, they did not

improve the decisions themselves. Sparing employees the hunt

for stock levels and sales history is useful, yet simply surfacing

numbers does not, by itself, reveal how to turn them into proﬁt.

Bluntly put, the 21st-century supply-chain clerk

is the white-

collar analogue of the 20

century factory worker. Each day begins

by opening an app—often a spreadsheet—and slogging through

hundreds of rows. The cycle is endless: top performers become

trainers or supervisors, perpetuating it. A desk does not, by itself,

confer “intelligence”. Given today’s technology, these clerical roles

will soon belong in history books—obsolete, to general relief.

Yet the accumulated know-how of those very clerks is anything

but disposable. Their day-to-day heuristics—honed through years

of wrestling with software vendor quirks, catalog edge-cases, and

improvised workarounds—form the tacit domain knowledge that

tomorrow’s systems of intelligence must absorb, formalize, and sur-

ASCM, founded in 1957 as APICS, has issued over 100,000 CPIM certiﬁca-

tions since 1973.

Titles vary—demand planner, inventory analyst, replenishment manager,

scheduler, and so on.

6.2. GENERAL INTELLIGENCE 213

pass. Therefore, automation is not a repudiation of their expertise;

it is the vehicle that will preserve it, scale it, and ﬁnally liberate it

from the drudgery that keeps it underleveraged today.

Before considering how routine tasks vanish, recall why “soft-

skill” leadership loomed large in supply-chain teams. When plan-

ning was manual or semi-manual, leaders spent most of their energy

coordinating armies of clerks—arbitrating priorities, smoothing

friction between silos, and keeping throughput from stalling. That

requirement falls away as soon as decisions are automated. Once an

engineered—eventually artiﬁcial—intelligence carries the planning

load, the coordination problem collapses; a handful of specialists

can steer a very large ﬂow of work.

The mandate changes. The leader’s job shifts from herding

people to owning the machine that makes the decisions. That own-

ership has four parts: (1) state the economic objective in money

terms and deﬁne the admissible option space; (2) guard decision

quality—via audits, halting heuristics, and dual runs when needed;

(3) steward semantics and data provenance, so inputs remain trust-

worthy; (4) recruit and protect a small, engineering-minded team

able to modify the logic on short notice. Charisma and empathy

still help, particularly when aligning ﬁnance, merchandising, and

operations around the same objective, but they cannot substitute

for technical stewardship. Firms that cling to clerical workﬂows

will keep rewarding soft-skill theater even as margins erode—a

theme we revisit when discussing manual forecasting.

6.2.3 Large language models

Large language models (LLMs) that emerged in the early 2020s

mark a milestone in seven decades of rapid software progress.

The most capable LLMs arguably show the ﬁrst spark of general

intelligence and thus merit close attention. They matter to supply-

chain work—though many vendor-promoted “use cases” are mere

buzzword-riding nonsense.

Conceptually, an LLM does one thing: it reads a sequence of

symbols and predicts the next, appending it to the input. The

214 CHAPTER 6. INTELLIGENCE

input sequence is the prompt; the act of generating is sequence

completion. Symbols could be single characters, but performance

improves when the model operates on multi-character groups called

tokens (vocabularies typically number

∼

100,000). Text is tokenized

before inference and detokenized after, restoring plain language

Internally, an LLM assigns probabilities to all possible next

tokens. One may sample from this distribution instead of always

taking the top choice, injecting randomness controlled by a tem-

perature parameter (with temperature = 0 yielding deterministic

output).

LLMs have a ﬁxed context window—the maximum token count

for prompt plus output. When that limit is exceeded, the oldest

tokens are truncated and their information lost. Models now

commonly hold over 100,000 tokens (roughly 100 pages of text).

Techniques such as retrieval-augmented generation (RAG) extend

practical reach, but lie outside this discussion.

Running a model (inference) is simple; creating one (training)

is far harder. LLMs are deep learning networks with billions of

parameters arranged as tensors

and combined through mostly

linear algebra operations.

Those corpora typically feature a trillion tokens or more. Train-

ing processes the corpus one token at a time and, each time, nudges

the parameters

toward a more accurate probabilistic prediction

of the next token. After the public corpus is ingested, training

repeats with a much smaller corpus intended to mold the model’s

generative patterns toward those of a helpful assistant. This latter

process is ﬁne-tuning.

This summary also clariﬁes why the models are called pre-

trained: unlike most machine-learning models, users need not bring

Multimodal models ﬁrst convert images into a latent vector that occupies

token slots, so no explicit image-to-token step is needed.

In computer science, a tensor is the dimensional generalization of the matrix.

A scalar, a vector, and a matrix are respectively tensors of dimensions zero, one,

and two.

A feat performed through diﬀerentiable programming, which combines

automatic diﬀerentiation with stochastic gradient descent.

6.2. GENERAL INTELLIGENCE 215

their own dataset; the trainer supplies it.

LLMs embody an uncanny, non-human kind of intelligence.

Software vendors oversell their capabilities, while many supply-

chain experts—and entire organizations—downplay them, treating

LLMs as a curiosity that leaves both theory and practice unchanged.

Both stances are wrong. LLMs are neither magic nor negligible.

They require basic prompting skills

and proper instrumentation;

without these, any model looks “dumb”. Their performance is

highly task-dependent—subpar for arithmetic and strict numerics,

yet superhuman for code de-miniﬁcation, refactoring boilerplate,

or rephrasing brittle business rules. A competent practitioner

approaches them with prior beliefs about where they will shine

and where they will fail. In that sense, prompting has joined

spreadsheets and word processors as table stakes for white-collar

work.

Contemporary models are also multimodal. Most frontier LLMs

natively ingest images, and many now emit images as well, with

the same integration spreading to audio—both speech recognition

and synthesis—by shoehorning pixels or waveforms into pseudo-

tokens. Yet outside these native modalities, the impressive “extra”

powers that people see—web search, code execution, retrieval,

optimizers, simulators—still come from an orchestrator layered

on top of the model. The model emits a structured tool call; the

orchestrator invokes the external tool; the result is fed back into

context, and completion resumes.

An uninstrumented LLM is

a sharp autocomplete; the same weights, instrumented, behave

like a competent analyst with a browser, calculator, and terminal.

The intelligence in the weights is unchanged; the capabilities—and

therefore the usefulness—are not.

Modern language models show a spark of general intelligence

for two reasons: they can write code and they can follow chain-

of-thought prompts. Their code falls short of an experienced

“Prompt engineering” sounds grand; most bright people learn those skills in

a few days.

Tool names and wire formats keep changing, but the pattern is stable: the

model proposes, the runtime executes, state returns to the model.

216 CHAPTER 6. INTELLIGENCE

engineer’s output, yet it already outperforms low-skill IT work-

ers. The ability to write software is foundational, because self-

improvement—the hallmark of general intelligence—requires it.

Chain-of-thought prompting coaxes a model to reason aloud.

Merely appending “Let’s think step by step.”

to a given prompt

often boosts answer accuracy, but the extra reasoning tokens raise

compute cost. The ﬁgure below illustrates the eﬀect.

Q: A juggler can juggle 16

balls. Half of the balls are golf

balls, and half of the golf balls

are blue. How many blue golf

balls are there?

A: The answer (Arabic numer-

als) is

(Output) 8 ×

(d) Zero-shot-CoT (Ours)

Q: A juggler can juggle 16

balls. Half of the balls are golf

balls, and half of the golf balls

are blue. How many blue golf

balls are there?

A: Let’s think step by step.

(Output) There are 16 balls in

total. Half of the balls are golf

balls. That means that there

are 8 golf balls. Half of the golf

balls are blue. That means that

there are 4 blue golf balls. ✓

Chain-of-thought (CoT) helps by forcing the model to unpack

a task into smaller steps within its context window. The model

retains no memory beyond that window; no hidden state carries

from one answer to the next. CoT simply spends more tokens

at inference time to surface intermediate reasoning. This usually

improves reliability up to a point, with diminishing returns and

hard limits imposed by the window. Recent systems increasingly

perform this expansion under the hood and suppress the raw trace

See Large Language Models are Zero-Shot Reasoners (2023) by Kojima et

al.

6.2. GENERAL INTELLIGENCE 217

(“thinking” modes) to spare users long monologues. CoT is an

inference-time budget to spend on harder questions—not a new

capability in the model’s weights.

Despite their appeal, large language models typically consume

a million-fold more computation than classic algorithms solving the

same job. Hardware progress will lower per-token costs, yet these

models are unlikely ever to rival purpose-built data-processing

code. Reserve them for tasks plain algorithms cannot reach.

Language models matter to supply chain mainly in two ways:

ﬁrst, speeding up the writing and upkeep of numerical recipes and

their documentation; second, extracting features from raw text.

We revisit both later. Other applications are secondary.

Misuse of large language models

Since the early 2020s, many enterprise-software vendors have made

bold—often nonsensical—claims about artiﬁcial intelligence (AI).

They hide the details not to guard secrets but to mask how little

technology lies beneath. Most of the hype centers on large language

models (LLMs). Let us dismantle the main claims.

Our AI forecasts more accurately: Language models are ill-

suited to time-series forecasting—or numerical work of any kind.

Their performance, in both accuracy and computational cost, is

abysmal compared with even basic statistical models, let alone

top-ranked ones. Pretrained forecasting models—an LLM-style

idea—are under active study. They may help elsewhere, but, as

Chapter The Future shows, they are a dead end for supply-chain

work.

Our AI crunches all your data: Per byte, language models are

both expensive and slow, and will remain so for the foreseeable

future. As a rule, processing megabytes of data through a language

model is a nontrivial operation that takes minutes to complete.

Large companies have terabytes of records in their business systems.

Simply passing an entire corporate dataset through an LLM—even

once—will remain prohibitively slow and costly for years. Cost

aside, an LLM does only one thing—sequence completion. That

218 CHAPTER 6. INTELLIGENCE

tool is versatile, but forcing vast non-text datasets through it yields

no obvious beneﬁt.

Our AI talks to end-users: Because software vendors bill per

seat, ineﬃciency sells: each extra clerk forced to ﬁght the software

means extra revenue. Bolting an LLM onto a screen to create a

“chat” interface only worsens matters: conversation is slow and

tiresome. Chatbots belong at help desks, not in high-throughput

enterprise workﬂows; training people on a crisp interface beats

sluggish dialogue every time.

In enterprise software, LLMs are today’s fashion—one in a

long line soon to be replaced by the next. For forty years, CRUD

systems have ruled, and that culture, dominant among vendors

of systems of records, resists meaningful integration of new ideas,

even when the ideas are sound.

6.2.4 Misconceptions about intelligence

Popular culture has carried the idea of artiﬁcial intelligence (AI)

for decades, and much of today’s boardroom intuition still echoes

century-century theories about what “intelligence” is supposed

to look like. This matters because those intuitions leak directly

into supply-chain decisions: they shape which projects are funded,

which risks are feared, and which levers are left untouched. Before

we mechanize decisions, we must clear away the myths that keep

executives oscillating between paralysis and credulity.

In supply chain, the relevant question is not whether machines

will “think like us,” but whether software can systematically make

better bets about what moves, where, when, and how much, and do

so unattended, with auditable economics. Misconceptions about

AI push ﬁrms into two symmetric errors: alarmism, which delays

proﬁtable automation on the grounds that “AI is dangerous,” and

anthropomorphism, which mistakes human-like conversation for

decision-making ability and green-lights brittle toys.

The next pages examine three persistent misunderstandings—the

“singularity,” the Turing-test view that human-sounding chat im-

plies general intelligence, and the Moravec paradox, misread as

6.2. GENERAL INTELLIGENCE 219

evidence of machine limits—and recast them in operational terms.

The standard for intelligence in this book is strictly economic:

higher risk-adjusted returns from the same scarce resources. By

that yardstick, most cinematic worries are irrelevant, and most

demo-friendly chatbots are distractions.

The singularity

The singularity imagines an AI that self-improves so fast that it

outstrips humanity, then either transcends or enslaves us. Countless

action ﬁlms riﬀ on this theme, yet Hollywood’s grasp of AI is no

better than its grasp of real-world ballistics. For supply-chain

practice, this mythology is a non sequitur. The systems that

decide reorders, prices, routes, or allocations are narrow, auditable

programs with bounded option spaces, explicit objectives, and kill-

switches; they do not “wake up”, they optimize under constraints.

Refusing to mechanize replenishment or routing because “AI might

go rogue” is like refusing forklifts for fear of robot uprisings: it

trades certain, present waste for a speculative, distant hazard.

The real risks are mundane and tractable—bad objectives, sloppy

semantics, untested code—and so are the safeguards: dual-run,

halting heuristics, code reviews, and economic audits. Existential-

risk debates have no bearing on whether a ﬁrm should apply the

best that computer science oﬀers to the day-to-day allocation of

scarce resources.

While the general public may get the impression that the tech-

nology leaped forward, essentially overnight, back in 2022

, this

is absolutely not the case. In reality, progress has been slow, incre-

mental, and hard-won over the past seven decades. Furthermore,

it is not one thing being improved, but dozens of largely unre-

lated fronts: better paradigms, better algorithms, better hardware,

better datasets, better methodologies, and better codebases. All

of these improvements have been generated by a diverse crowd

of contributors. Thus, while it is possible to stumble upon some-

ChatGPT by OpenAI was released in November 2022.

220 CHAPTER 6. INTELLIGENCE

thing that exceeds human intelligence, the idea that such an entity

would—as an event—elevate itself into a transcendent being is

far-fetched.

Once we remove the “event” aspect of the emergence of artiﬁcial

general intelligence, it becomes diﬃcult to explain why this would

ever present an extinction-level threat. Any malicious entity, or any

entity operated by malicious people, will be countered by similarly

capable entities that armies, intelligence agencies, companies, or

even universities and hobbyists will develop. Artiﬁcial general

intelligence will be a late addition to the already long list of

technologies that can do immense damage when used for nefarious

purposes.

Such an entity will be the product of an advanced industrial civ-

ilization and will rely on its complex—and fragile—supply chains.

Semiconductor manufacturing already ranks among the most intri-

cate industries. Conceivable “alternatives”—neuromorphic, pho-

tonic, quantum, or biosynthetic substrates—are, at best, lab-grade

and themselves ride on the same semiconductor tooling, materials,

and precision-manufacturing stack. In short, there is no industrial,

non-silicon path to computation today that avoids those chains.

The continued existence of such entities will depend on the active

support of humanity for decades, if not centuries. People will have

ample time and opportunity to address the inevitable problems

that have accompanied the rise of every technology so far.

Doomsaying is ancient, and most experts and politicians join

in for self-aggrandizement.

The Turing test

In 1950, British computer scientist Alan Turing devised his imita-

tion game, later called the Turing test. The gist is that if a human,

chatting through a text-only channel, cannot reliably tell human

from machine, then the machine qualiﬁes as generally intelligent.

The usual objections to the test dwell on consciousness and

self-awareness; these are beside the point for supply chain. Our

concern is not whether a machine “sounds human”, but whether it

6.2. GENERAL INTELLIGENCE 221

consistently makes choices that raise risk-adjusted returns under

uncertainty.

Crucially, supply-chain intelligence is not self-evident to the

naked eye. Humans have no sixth sense for whether “200 units

next week” is slightly too high or too low. Even ex post, judging a

decision requires counterfactuals—what would have happened had

we acted diﬀerently—and horizons that match lead times. This

evaluation is hard: outcomes are noisy, delayed, and entangled

with thousands of parallel choices.

An engine that speaks poorly—or not at all—may therefore

look “unintelligent” while quietly compounding value. Conversely,

a ﬂuent chatbot can pass Turing-style conversations yet steer the

ﬁrm toward losses. In the 2010s, simple pattern-matching bots

frequently fooled human judges; they were rhetorically convincing

and operationally useless. The lesson endures: imitation of human

style is orthogonal to decision quality.

A practical illustration: a good replenishment engine may

place many tiny, oddly timed orders, reprice slow movers by a

few cents daily, or divert one container to an unfashionable cross-

dock. To a planner, these moves can feel fussy or even wrong;

on the ledger they lower holding costs, reduce write-oﬀs, and

speed cash conversion. By contrast, a tool that “explains” itself

beautifully while preserving legacy MOQs and batch calendars will

earn applause in meetings and quietly bleed working capital.

The only meaningful test is economic. Call it an economic

Turing test: under a dual run or via reasoned counterfactuals,

with the same constraints and audit trail, does the system of

intelligence deliver higher risk-adjusted operating proﬁt than the

incumbent process after all costs (inventory, logistics, markdowns,

labor, capital) are charged? If yes, treat it as intelligent—no matter

how taciturn its interface. If not, no amount of conversational

polish redeems it.

Thus, fascinating though it remains, the imitation game is an

inadequate criterion for our craft. Supply-chain intelligence systems

should be judged by auditable economics over the appropriate time

horizons, not by their ability to chat.

222 CHAPTER 6. INTELLIGENCE

Moravec’s Paradox

The American computer scientist Hans Moravec observed in the

1980s that tasks that seem simple—grabbing a teapot and pouring

tea—are among the hardest for artiﬁcial intelligence to replicate.

In contrast, tasks once deemed marks of genius—such as matrix

diagonalization—have proved straightforward for machines.

However, the paradox dissolves once its hidden premise is stated.

The paradox assumes that animal mobility—and the planning

behind it—is learned (nurture) rather than innate (nature). In

other words, acquiring mobility is held to result from learning—one

that has proved incredibly diﬃcult to replicate artiﬁcially for the

past four decades.

On closer examination, those capabilities involve little or no

learning. Foals—and their equine cousins—stand and walk within

hours. This is not “learning” but a quick calibration of a nearly

ﬁnished motor system. The cognitive structures are nearly com-

plete; the system needs only a slight nudge for the pathways to

lock into place.

In humans, the innateness is less visible: compared with other

mammals, our oversized heads force earlier birth, leaving motor

circuits to ﬁnish developing outside the womb. What many perceive

as the infant’s “learning” is cognitive development that would

continue unimpeded if the infant remained in the womb for a few

more months.

These largely innate circuits are evolutionary products, reﬁned

over roughly 800 million years and untold generations. No wonder

machines struggle to match animal mobility. The challenge is

akin to engineering a device that outperforms trees at convert-

ing sunlight into structural material, or ribosomes at assembling

macromolecules. Outperforming evolution at games played for

eons is brutally diﬃcult.

Mobility rests on hundreds of tightly knit heuristics; even a

snail can negotiate the three-dimensional maze of vegetation. Well-

chosen heuristics can seem “intelligent” provided the action space

is tightly bounded—as in the snail’s case.

6.2. GENERAL INTELLIGENCE 223

In nature, mobility was a necessary evolutionary path to general

intelligence: life could not leap from unicellular origins to general

intelligence without ﬁrst solving locomotion. Machines face no

such constraint—so long as humans supply energy and materials

while we solve the remaining hurdles (e.g., industrial substitutes

for muscle ﬁbers). Evolution solved these problems in ways that

do not lend themselves to industrialization; we must solve them

anew.

The implication for supply chain is easy to miss. The tasks that

feel “simple” to humans—walking across a crowded aisle, grasping

a box, recognizing a co-worker—are precisely those evolution has

tuned for hundreds of millions of years. By contrast, large-scale,

fast-moving resource allocation—ranking millions of micro-options

with delayed, stochastic payoﬀs—is the opposite of what our brains

evolved to do. Working memory is tiny, attention is fragile, and

our intuitions about probability are notoriously poor. Machines

invert this proﬁle: they struggle with animal motion yet excel at

enumerating vast option sets, scoring counterfactuals, and recom-

puting plans every minute. Thus the quip “AI can’t even fold

laundry” is irrelevant to whether software can outclass planners

at repricing 50,000 SKUs, rebalancing multi-echelon stocks, or

rerouting containers mid-voyage under volatile lead times. In the

Moravec sense, these are machine-native problems.

Two operational corollaries follow. First, do not condition

decision automation on progress in robotics or conversational pol-

ish; the relevant yardstick remains the economic one introduced

earlier: under the same constraints, does the engine deliver higher

risk-adjusted proﬁt than the incumbent process? Second, expect

machines to overtake humans ﬁrst where planners sweat most:

combinatorial allocation, adversarial pricing, multi-echelon balanc-

ing, and dynamic rescheduling. None of this requires humanlike

generality; specialized intelligence within well-deﬁned boundaries

suﬃces—exactly the habitat of supply chain engines.

In short, copying animal mobility is not a shortcut to general

intelligence: it is a cluster of tough yet essentially specialized prob-

lems. Solving them may inspire, or occasionally inform, work on

224 CHAPTER 6. INTELLIGENCE

artiﬁcial general intelligence, but that remains a separate pursuit.

6.3 Specialized intelligence

A specialized problem is one whose options are bounded by ex-

plicit formal rules; solving it calls for specialized intelligence. The

academic supply chain puzzles are typical examples, and each

solution—whether a simple heuristic or a large hyperparametric

model—counts as “intelligent” only by the quality of its results.

Declaring a method “smarter” merely because it looks arcane is a

mistake.

Deﬁnition (Specialized Intelligence).

The capacity to make choices yielding superior future re-

wards within formal boundaries.

Supply chain becomes tractable precisely when its sprawling,

general intelligence questions are carved into many bounded al-

location problems. Each bounded problem ﬁxes three elements:

(1) the option set—what moves where, when, and how much may

be chosen; (2) the constraints—capacity limits, calendars, MOQs,

service promises, and how often the decision is revisited; and (3)

the valuation—a money-denominated score that prices upside and

downside under uncertainty. Once these are explicit, the task

ceases to be “plan the supply chain”; it becomes “choose among

admissible options to maximize risk-adjusted return”.

This decomposition is not a rhetorical device; it is the engineer-

ing move that makes unattended software possible. Replenishing

a SKU at one location over a given horizon, selecting today’s price

for a slow mover, assigning cartons to a truck stop, accepting or

deferring a repair on a rotable—each is a specialized, auditable

resource-allocation decision. Loosely coupled problems can be

handled greedily at high frequency; tightly coupled ones can be

grouped and optimized with heavier machinery. Boundaries remain

revisable—when economics shift, we widen the option set or adjust

6.3. SPECIALIZED INTELLIGENCE 225

the valuation—but while they hold, the decision can be automated,

inspected, and improved by a system of intelligence.

The history of artiﬁcial intelligence is a game of moving goal-

posts. Researchers pick a hard problem, label its solution “real AI”,

then—once solved—rename it and move on to the next challenge.

Each triumph is soon judged mundane, and the cycle begins again.

This pattern explains why mature solutions often look “un-

intelligent”: they become routine and mechanical. Intelligence

should be judged by the quality of choices, not by how arcane

the machinery appears. Sophisticated models can masquerade

as “smarter”, yet they still address a bounded task; true general

intelligence questions must be confronted separately.

Counterintuitively, the ﬁrst workable solution is rarely the

simplest; achieving simplicity is a separate, often harder, task.

Progress does not run from simple to sophisticated but from messy

prototypes to streamlined cores. Maxwell’s electromagnetic theory

began with more than twenty equations; Heaviside later condensed

them into the four we use today. In the same way, today’s convo-

luted answers are tomorrow’s elegant principles.

The same cycle fuels AI booms and busts: people confuse

solving a tough niche problem with cracking general intelligence,

and investors ride the resulting hype. General intelligence is hard,

but countless specialized problems must be harder. Indeed, if we

ever build a general intelligence, every remaining unsolved problem

will, by deﬁnition, be tougher; otherwise they would have fallen

ﬁrst.

The highs and lows of the AI label should not eclipse computer

science achievements since World War II—many central to supply

chain. The ﬁeld is now so broad that any overview is necessarily

partial, but the following pages single out the topics most relevant

to supply chain work.

6.3.1 Algorithms

Algorithms are precise procedural recipes that terminate and return

a correct answer for a well-speciﬁed input. Formalized in the 1930s

226 CHAPTER 6. INTELLIGENCE

(Church, Turing, Kolmogorov), algorithms became the backbone

of 20

century computer science. Many early “AI” successes

of the 1950s—topological sorting, graph routing, linear-equation

solvers—are now simply known as algorithms.

Deﬁnition (Algorithm).

A ﬁnite sequence of rigorously speciﬁed instructions that

solves a well-posed problem.

Formally, any program that halts on all admissible inputs could

be called an “algorithm”. In practice, computer scientists use the

term more narrowly. An algorithm is an atomic, reusable building

block whose problem statement is independent of any dataset,

interface, or server, accompanied by a proof that, for every valid

input, it returns the speciﬁed output and by an analysis of its time

and space requirements as input size grows.

An “atomic” task cannot be decomposed into smaller, inde-

pendent, purposeful subtasks. By contrast, rendering a web page

involves hundreds of steps—from tokenizing HTML to adjusting

character kerning—and thus is not atomic.

An algorithm comes with a proof of correctness; absent such a

proof, it is merely a heuristic. The combined Fermat–Fibonacci

tests, for instance, detect primes reliably yet still lack a proof of

correctness, so they remain heuristics.

An algorithm’s value ultimately lies in its performance. While

brute-force methods exist for most tasks, a good algorithm reaches

the same result with far less computation and memory. On occasion

one can even prove optimality, though the subtleties of complexity

theory lie beyond this chapter.

Algorithms (and the data structures that support them) are the

bedrock of software; every method discussed later relies on them.

Accordingly, they are an auxiliary science for supply chain—though

well-designed tools can shield practitioners from their details, as

we shall see.

Readers seeking depth should consult Cormen et al.’s classic

6.3. SPECIALIZED INTELLIGENCE 227

Introduction to Algorithms (3rd ed., 2009). The annex provides a

worked example—the topological sort algorithm.

The CRUD pattern—introduced in Chapter Information—owes

its success to the fact that it lets teams ship business software

without deep algorithmic chops. In enterprise IT, this became

a career track: many capable developers spend decades building

forms and reports while never learning to design and analyze

algorithms. That gap is harmless inside systems of records, where

constant-time CRUD dominates and the database shoulders the

heavy lifting; it becomes crippling the moment we leave ledgers and

attempt optimization. It also explains CRUD’s cultural dominance

and, symmetrically, why vendors brilliant with records so often

stumble at systems of intelligence: the latter demand algorithmic

modeling, complexity control, and performance engineering—skills

CRUD habits neither teach nor select for.

6.3.2 Operations Research

Born in World War II, operations research tackled wartime resource-

allocation

questions—where to place radar, how to route convoys,

how to assign scarce aircraft—and, after 1945, business ones. It

spawned many algorithms—most famously Dantzig’s 1947 simplex

method—but, unlike computer science, it cared less for atomic

problems and more for end-to-end applications. In 1947, Scottish

radio engineer Robert Watson-Watt deﬁned operations research:

To examine quantitatively whether the user organization

is getting from the operation of its equipment the best

attainable contribution to its overall objective.

Watson-Watt’s wording echoes this book’s opening deﬁnition

of supply chain; operations research is its forerunner. Between

the 1950s and 1970s, the ﬁeld produced simulations, econometrics,

stochastic models, and mathematical optimization. Many of these

strands later grew into disciplines of their own, drifting away from

the operations research umbrella.

Operations research was originally understood as research on (military)

operations.

228 CHAPTER 6. INTELLIGENCE

The most notable contributions of operations research—at least

among those not reclassiﬁed into their own ﬁelds—are methods for

transportation optimization, linear and quadratic programming,

integer programming, and similar tasks. All these tasks share

a common structure: decision variables that must be chosen to

maximize an objective function that represents a real-world goal.

This framing still underpins most business decision-making, and

this book’s approach.

By the 1970s, operations research was falling short of its ambi-

tions. As mentioned earlier, American operations research pioneer

Russell Ackoﬀ captured this sentiment in his 1979 paper The Future

of Operational Research is Past. Most resource-allocation decisions

were still manual in 1979—and most remain so today. Ackoﬀ listed

several root causes:

•

Poor systems thinking: experts relied on simplistic, ﬁrst-

order causality.

•

“Pseudo-optimal” results driven by contrived objective func-

tions.

•

Fragile answers that assume a perfectly known, unchanging

context.

•

Sanitizing real-world “messes” into toy problems, leaving

practice untouched.

Those critiques still apply. Much of the supply chain literature

repeats the same mistakes—one reason so little of it survives

contact with reality

During the 1980s, operations research morphed into “OR” (the

acronym), specializing in software “solvers” for optimization models

written in domain-speciﬁc languages. OR thus settled as a subﬁeld

of computer science.

As of September 2024, a Google Scholar query for “inventory optimization”

returns 1.9 million publications. It is hardly an exaggeration to say that none

of this literature has ever survived in real-world conditions.

6.3. SPECIALIZED INTELLIGENCE 229

Commercial solvers form a healthy software niche and handle

many design tasks well. Nocedal and Wright’s Numerical Opti-

mization (2006) is still the standard survey, even for learning-based

variants. Beyond vehicle-routing variants, solvers rarely appear in

supply chain because three lingering limitations curb their value.

First, they focus exclusively on deterministic rather than stochas-

tic optimization, making it impossible to handle uncertainty—a

constant feature of supply chains.

Second, they oﬀer either scalability or expressiveness—but not

both. Solvers scale for supply chain purposes only in linear or

quadratic regimes, which are too simple to reﬂect real-world supply

chain situations.

Third, solvers prize mathematical purity over practical expres-

siveness

. Linear and quadratic programming can indeed certify

optimality, yet they describe the real world so poorly that the

“optimal” answer is invariably useless.

Setting purity aside, solvers still scale poorly. Faced with a

concrete instance—say, vehicle routing—companies often build ad

hoc code instead of using oﬀ-the-shelf tools. The issue is acute in

supply chains, where tens of millions of decision variables (several

per SKU) are common.

Thus, the legacy of operations research lies largely in the ﬁelds

that originally emerged from it. Present-day OR has little to

do with the goals operations research set out to achieve. Fur-

thermore, while OR’s favored techniques have merits and notable

achievements, they are unlikely to circle back into supply chain.

6.3.3 Statistical learning

The notion of algorithmically inferring a process from recorded ob-

servations appeared in the 1950s. It emerged alongside the earliest

writings on artiﬁcial intelligence. Because individual observations

can encode partial solutions, learning has long overlapped with

Techniques such as cutting-plane methods, branch-and-bound, and branch-

and-cut exemplify the mainstream view that a solver must not only provide good

solutions but also prove that the solutions are either optimal or quasi-optimal.

230 CHAPTER 6. INTELLIGENCE

optimization

. Early work on artiﬁcial neural networks was once

published under the operations-research banner; today it is ﬁrmly

classed as "learning".

Until the early 1990s, interest in learning techniques was limited

for two main reasons. First, computers lacked the power to achieve

anything substantial by learning. Consequently, non-learning ap-

proaches—such as expert systems

—were favored. For example,

Deep Blue—the ﬁrst chess-playing program to defeat the reigning

world champion, Garry Kasparov, in 1997—was engineered as

an expert system. There was also a scarcity of public training

datasets, since collecting and distributing large datasets remained

prohibitively expensive until the late 1990s. The MNIST dataset

contains 60,000 training images and 10,000 test images, each a

28×28 grayscale rendering of a handwritten digit. It provided

a shared baseline that fostered a generation of researchers and

standardized validation and benchmarking.

Second, high-dimensional learning remained baﬄing until the

late 1990s. Classical statistics assumes the number of variables is

small relative to the number of observations. With 100 observations,

a two-variable regression can be trusted; with ten variables, spuri-

ous patterns proliferate; with 10,000 variables, standard methods

collapse. Paradoxically, more information produced less insight.

The curse of dimensionality found its ﬁrst convincing an-

swer in Vapnik–Chervonenkis (VC) theory, developed from the

While nowadays learning problems and optimization problems have accu-

mulated sharply distinct collections of techniques, it is still not entirely clear

whether this distinction is entirely appropriate. Indeed, most learning tech-

niques do have an optimization algorithm at their core, and conversely, some

authors—including the present one—believe that the future of optimization lies

in introducing a learning component at its core. In the future, these two ﬁelds

might merge if a suitable unifying paradigm is discovered.

Expert systems are collections of logical and arithmetic rules written by

domain experts. They were largely abandoned in the 2000s, as they generally

proved more costly and less eﬃcient than learning-based alternatives. However,

rule-based subsystems are still prevalent in enterprise software, and many have

reached an operational complexity well beyond what was considered “advanced”

in the early 1990s.

6.3. SPECIALIZED INTELLIGENCE 231

1960s through the 1990s

. The theory led to support-vector

machines—ﬁrst implemented in 1995

—and provided a mathe-

matical framework to curb overﬁtting. Support vector machines

soon demonstrated their value on real-world datasets.

Under-ﬁtting Proper ﬁtting Over-ﬁtting

Figure 6.1: Underﬁtting, proper ﬁtting, and overﬁtting.

VC theory reframed the aim of statistical modeling as maxi-

mizing generalization—performance on unseen data. Though we

cannot measure accuracy on data we do not have, VC theory

proves an upper bound on the true error: empirical error plus a

model-complexity term determined by the model’s design. This for-

malization reﬁnes the classical bias–variance tradeoﬀ: low-capacity

models exhibit high bias, high-capacity models high variance. Fig-

ure 6.1 illustrates underﬁtting, a correct ﬁt, and overﬁtting. This

theory paved the way for statistical models that can deliver reliable

results even when the number of input dimensions vastly exceeds

the number of observations.

Operationally, support vector machines were rapidly super-

seded by alternative techniques—(gradient) boosting (1999)

and

L.G. Valiant’s 1984 paper A Theory of the Learnable introduced the concept

of probably approximately correct (PAC) learning, laying the ﬁrst rigorous

foundation—later called computational learning theory—for studying learning

from a statistical standpoint. PAC theory, however, ultimately proved less

inﬂuential than the subsequent Vapnik–Chervonenkis framework.

See Support-Vector Networks (1995) by Corina Cortes and Vladimir Vapnik.

See Greedy Function Approximation: A Gradient Boosting Machine (1999)

by Jerome Friedman.

232 CHAPTER 6. INTELLIGENCE

bagging (2001)

—whose mathematical analyses likewise showed

they were well-behaved with respect to overﬁtting. Together, these

ideas gave rise to gradient-boosting machines, which remained

state-of-the-art across many tasks for roughly a decade. Today,

gradient boosting machines remain popular, particularly when

observations are scarce.

These developments had an outsized impact: the built-in capac-

ity control of boosting, bagging, and SVMs let theoreticians prove

tight bounds on overﬁtting—assurances rival approaches lacked.

Throughout the late 1990s and much of the 2000s, this statistical

seal of approval led researchers to sideline nearly every competing

approach. Gradually, however, it became clear that no amount of

theoretical polish or engineering ﬁnesse could push these methods

beyond certain hard limits. The technology had eﬀectively stalled.

French-American computer scientist Yann LeCun captured the

mood by dismissing them as little more than “gloriﬁed template

matching”.

Despite these limits, statistical-learning ideas remain a corner-

stone of modern statistics and a vital auxiliary science for supply

chain. For an accessible, ﬁgure-rich survey, see the textbook The

Elements of Statistical Learning (2009) by Hastie et al. Boosting-

and bagging-based models still excel when data are scarce but

high-dimensional, and they require far fewer resources than most

alternatives—although they do not provide a route to general

intelligence.

6.3.4 Deep learning

In the 2010s, deep learning became the dominant machine-learning

paradigm. Deep learning descends from artiﬁcial neural net-

works, but it crystallized only after the ﬁeld abandoned biological

metaphors and embraced silicon hardware. Remarkably, its core

ideas are simpler than those of its bio-inspired predecessors.

Conceptually, a deep-learning model is a ﬁxed circuit of ﬂoating-

See Random Forests (2001) by Leo Breiman.

6.3. SPECIALIZED INTELLIGENCE 233

point operations parameterized by ﬂoating-point numbers. Most

layers perform matrix multiplications punctuated by rectiﬁers. Rec-

tiﬁers break linearity: stacking linear maps remains linear, so a non-

linear activation is required. Additional components—convolutions,

pooling, or softmax layers—allow the model to handle diverse input

modalities and output formats.

Consider again handwritten-digit recognition on the MNIST

dataset. If you ﬂatten each 28×28 image into 784 numbers and

pass them through three weight matrices with rectiﬁers, you obtain

a 234,752-parameter multilayer perceptron—tiny by today’s stan-

dards (see Figure 6.2). Running it (performing inference) requires

only three matrix multiplications and two rectiﬁer activations (see

the annex for details).

783

784

Layer 1

256

Layer 2

128

Layer 3

Digit 0

Digit 9

Layer 4

Figure 6.2: A compact 4

layer multilayer perceptron (784

256

128

10)

for handwritten-digit recognition.

So far we have speciﬁed the architecture and noted that in-

ference reduces to three matrix multiplications and two rectiﬁers;

what remains is to obtain the numbers inside those matrices. In

practice we want a well-trained network—one that, for each input

image, assigns its top score to the digit actually present. Train-

234 CHAPTER 6. INTELLIGENCE

ing is therefore a search over the parameter space for values that

minimize a chosen classiﬁcation loss. Deep learning tackles this

search through three coordinated steps: Initialization—choose

sensible random starting weights so activations stay numerically

well-behaved; Gradient—compute how an inﬁnitesimal change

in each weight would change the loss; and Descent—use those

gradients to nudge the weights step by step toward lower loss.

Together these steps iteratively reﬁne the network’s weights until

performance stabilizes.

Initialization – Weights begin as Gaussian noise and are imme-

diately rescaled so that, layer after layer, activations retain roughly

zero mean and unit variance. This simple precaution prevents the

product of many linear operations from blowing up (overﬂow) or

shrinking to numerical dust (underﬂow). When no closed-form rule

applies, a quick trial pass through the network rescales empirically

to preserve that invariant.

Gradient – The gradient tells each parameter which way—and

how far—to move to lower the chosen loss. Modern frameworks

obtain it by automatic diﬀerentiation, which threads derivatives

through the computation graph at roughly the cost of one extra for-

ward pass, but with higher memory since intermediate activations

must be cached. Earlier deep learning toolkits used hand-coded

gradient back-propagation; that the community took decades to

adopt automatic diﬀerentiation — dating from the 1960s — is a

reminder that progress is rarely linear.

Descent – Gradients become updates through stochastic gra-

dient descent (SGD), a 1950s-era workhorse that still dominates

deep learning. SGD samples one observation—or, for better hard-

ware utilization, a small set of observations referred to as a mini-

batch—nudges the weights in the gradient direction, then repeats.

Mini-batching trades extra arithmetic for sharply lower wall-clock

time by exploiting parallel cores. Variants with momentum, adap-

tive learning rates, or decoupled weight decay reﬁne the basic

update.

Deep learning overturned several statistical-learning dogmas

and still shapes what can—and cannot—be inferred from company

6.3. SPECIALIZED INTELLIGENCE 235

data.

The double descent

Deep-learning models are hyper-parametric: they usually contain

more parameters than training examples. The 234,752-parameter

perceptron above dwarfs MNIST’s 60,000 images. Statistical theory

predicts severe overﬁtting, yet practice says otherwise.

Discovered in the late 2010s, the double descent curve helped

clarify why many statisticians initially doubted deep-learning

claims. Trained to expect the classic U-shaped bias-variance trade-

oﬀ, they were puzzled when larger networks sometimes outper-

formed smaller ones instead of overﬁtting. Double descent shows

why: as model capacity grows, test error ﬁrst drops (good gener-

alization), then climbs (over-ﬁt), but beyond a second threshold

drops again as very large, overparameterized models settle into

new low-error regions (see Figure 6.3).

Figure 6.3: Double descent: test error falls, rises, then falls as the ratio

of parameters to data grows. Excerpt from Double Descent Demystiﬁed:

Identifying, Interpreting & Ablating the Sources of a Deep Learning

Puzzle (2023) by Schaeﬀer et al.

On the left of the double descent curve—the classic regime—

the network has too few parameters, so it underﬁts. As we add

236 CHAPTER 6. INTELLIGENCE

weights, the test loss falls until it reaches a ﬁrst minimum at the

“sweet spot”. Push past that point and the model swings into

classic overﬁtting: extra capacity now memorizes noise faster than

it learns signal. Curiously, in this zone the training error continues

to slide smoothly even as the test error rises—a behavior that long

puzzled statisticians who expected the two curves to diverge much

earlier.

Move to the right—the deep regime—and the pattern ﬂips.

Once the parameter count crosses a second, much higher threshold,

test loss starts dropping again: larger really is better, as deep-

learning practitioners had claimed since the early 2010s. Yet each

extra slice of accuracy costs an outsized chunk of compute and

energy, so returns diminish rapidly. State-of-the-art networks now

operate well beyond that threshold, trading massive hardware

budgets for the last fractions of a percentage point in performance.

For supply chains, the message is mixed: giant models win

accuracy but hurt maintainability, while smaller ones stay agile yet

fall back into the classic under/overﬁt dilemma. Hybrid designs

that mix the two are frequently the superior option.

Mechanical sympathy

Artiﬁcial neural networks (ANNs) have a clear weakness: they

are grossly ineﬃcient in their use of computing resources. Silicon

chips resemble biological neurons so little that even a crude digital

neuron squanders cycles. Deep learning broke free of this handicap

by adopting what race engineers call mechanical sympathy. In

early Formula 1, the fastest laps were set not by drivers who

ran at the limit, but by those who sensed how much stress the

engine, gearbox, and brakes could tolerate and modulated their

pace so the car ﬁnished intact at the checkered ﬂag. Translated

to code, mechanical sympathy means arranging algorithms and

data to move with the grain of caches, vector units, and memory

buses instead of colliding with them. Once this mindset took hold,

researchers shed nostalgic neuron metaphors and kept only what

maximized raw computational throughput.

6.3. SPECIALIZED INTELLIGENCE 237

For example, early ANNs used the sigmoid activation function,

chosen to echo biology, but its exponential is slow in hardware.

The simpler rectiﬁer (ReLU) proved faster and quickly took over.

In the early 2010s, treating cache lines, vector widths, and mem-

ory buses as ﬁrst-class design constraints felt almost heretical. The

algorithmic, operations research, and statistical learning camps

had risen on hardware-agnostic theory: prove a better asymptotic

complexity class and let Moore’s law absorb pesky constant factors.

Tuning for those constants—say, shaving cycles with a rearranged

matrix or a SIMD instruction—was seen as busywork, soon nulli-

ﬁed by the next processor generation. Deep-learning researchers

upended that view, showing that painstaking attention to hard-

ware minutiae could yield speedups unattainable by elegance in

algorithmic complexity alone.

The early quest for ever-larger networks was fueled by the

biological metaphor: with roughly 100 billion neurons, the human

brain became an informal size benchmark for ANNs. Experiments

soon made the metaphor unnecessary—performance rose simply

because the models grew. Meanwhile, the data landscape ﬂipped.

In the 1990s, public datasets were scarce; by the 2010s, they were

expanding faster than networks could absorb them. Training on

every available sample became impractical, and vast troves of

information remained untapped.

Hence the arms race: ever-larger models fed by ever-larger

datasets. Toronto’s 2012 ImageNet network had 60 million parame-

ters and ran on GPUs; a decade later, trillion-parameter transform-

ers sit on custom silicon. The scale boom also spawned heavyweight

tooling. Google’s TensorFlow, released in 2015, ballooned past

four million lines of code—an industrial eﬀort unthinkable in the

statistical learning era of 10,000-line libraries.

The race for ever-larger models also pushed designers toward

ever-smaller ﬂoating-point formats. Because transistor count, and

therefore energy cost, grows faster than bit width, shaving pre-

cision delivers outsized speedups: on current GPUs, arithmetic

throughput can quadruple when you drop from 32-bit to 16-bit.

Early 2010s deep-learning systems used 32-bit ﬂoats exclusively—a

238 CHAPTER 6. INTELLIGENCE

safe default that still suﬃces for most supply-chain calculations,

provided pathological edge cases are avoided (some tasks still ben-

eﬁt from 64-bit). By the early 2020s, however, training in 16-bit

and serving in 8-bit had become routine, courtesy of a friendly

co-evolution of GPU and CPU hardware. Research is now probing

1-bit—and even sub-1-bit—weights, showing that the lower bound

on usable precision has yet to be reached.

Mechanical sympathy matters in supply-chain practice. Swiss

computer scientist Niklaus Wirth warned in the 1990s that software

often slows faster than hardware accelerates—a pattern now called

Wirth’s law. Because of a general lack of interest in hardware

eﬃciency, many business systems run no faster today than in 2000,

despite far better chips. That slowdown is Wirth’s law at work.

Deep-learning tricks cannot be reused wholesale, yet they reveal

how mindful coding unlocks huge gains. Supply-chain professionals

therefore need at least a working grasp of mechanical sympathy.

Nonconvex optimization

In the early 2000s, mathematical optimization still followed the

original operations-research playbook. Three tenets deﬁned it.

Statistical-learning practitioners—support-vector-machine devo-

tees included—accepted it wholesale.

First, early optimization theory treated local minima as a prac-

tical brick wall: in nonconvex landscapes one might, in principle,

escape them, yet in real projects one almost never did. That as-

sumption spawned an entire branch—convex optimization—whose

express purpose is to sidestep that obstacle. Because a convex

function has no local dips—only a single global minimum—any

well-behaved descent algorithm converges to it. For a thorough

treatment of the proofs, algorithms, and applications that rest on

this property, see Boyd and Vandenberghe’s Convex Optimization

(2004).

Second, for nonconvex problems, any respectable method was

expected to provide a bound on the distance of its solution from

the global optimum. One does so by furnishing an upper bound on

6.3. SPECIALIZED INTELLIGENCE 239

Local Maximum

Global Minimum

Global Maximum

Local Minimum

Figure 6.4: A real

valued function showing both local and global extrema.

the gap between the best-known solution and the global minimum.

Techniques such as branch

and

bound and branch

and

cut oﬀer

such guarantees. However, unlike convex optimization—which

beneﬁts from highly scalable techniques—these general methods

scale poorly. As mentioned earlier, the poor scalability of such

solvers has long been a chief reason for their limited adoption in

supply-chain circles.

The deep-learning community jettisoned that classical view. It

embraced a blunt tool—stochastic gradient descent—and applied

it to nonconvex landscapes, sidestepping fears of local minima.

This approach has proved wildly successful, enabling models with

trillions of parameters—a scale inconceivable in the 2000s.

A new outlook emerged: the obstacle was no longer local

minima but broad, ﬂat plateaus. The prevailing view holds that

local minima become exceedingly rare as dimensionality increases.

Thus, intuition from low-dimensional examples fails in high dimen-

sions

. Instead, vast low-gradient regions (“plateaus”) pose the

main challenge for descent methods.

In everyday experience with two or three dimensions, shapes such as circles

and spheres ﬁll a signiﬁcant fraction of the enclosing squares and cubes. In

higher dimensions, however, the volume of a hypersphere becomes a vanishingly

small fraction of the hypercube’s volume; almost all the space lies in the corners,

well beyond the reach of the centrally placed hypersphere.

240 CHAPTER 6. INTELLIGENCE

Third, for (hyper)parametric models, optimality proofs become

meaningless in the double-descent regime. The training loss can

plunge and hover near zero, yet those tiny residuals say little about

how the model will generalize. Worse, many loss functions favored

in deep learning—such as cross-entropy—do not even have true

minima

While the deep-learning community had reasons of its own to

reject the classic optimization stance inherited from operations

research, this rejection matters for supply chain. It shows that

the classical paradigm suﬀers from counterintuitive limitations

that escaped notice for roughly six decades (1950s–2000s). In

what follows, we will see that supply chain has further grounds to

reject this classic stance and that alternative, far more valuable

paradigms exist.

The bag of tricks

Every programming paradigm oﬀers its own mental model of

software work, with core concepts, practices, and tools; most

are opinionated about data representation and control of program

behavior.

Deﬁnition (Programming Paradigm).

A maximally composable way to design and structure

programs.

Deep learning was the ﬁrst

broadly adopted programmatic

Technically, cross-entropy computed with ﬁxed-precision ﬂoating-point arith-

metic does have a deﬁnite minimum, since the range of representable values

is ﬁnite. However, such a minimum is largely meaningless because numerical

overﬂows and underﬂows cause critical failures long before this threshold is

reached.

Fuzzy logic—a form of many-valued logic in which variables can take any

real number between 0 and 1—was originally developed in the 1960s. It might be

considered the ﬁrst programmatic paradigm for what was then called “artiﬁcial

intelligence”, although it never became more than an extremely narrow niche

with limited applications.

6.3. SPECIALIZED INTELLIGENCE 241

learning paradigm; in the early 2010s, it puzzled researchers by

defying prevailing expectations in machine learning.

Until then, papers and software focused on the ﬁnished product:

the model ready to be trained on a fresh dataset. What set deep

learning apart was its focus on architectures, numerical tricks, and

guiding principles for crafting models. While toolkits for statistical

learning (such as scikit-learn

) featured dozens of models, those

for deep learning shipped with none. The ﬁeld spent much of the

2010s absorbing that shift. The old approach—identifying a novel

model, running a benchmark, and publishing results—became

obsolete. Novelty no longer lay in the model itself or its statistical

characterization. Even ﬁrst-year computer students could cobble

together a “unique” deep learning network by cherry-picking layers

from the available tensor operations and benchmark it on one of

many public datasets. For a brief period, deep learning conferences

and journals were ﬂooded with papers that were little more than

such compositions; despite their “uniqueness”, such contributions

were often inconsequential.

Deep-learning architectures serve as the modern analogue of

the “models” once championed by operations research, yet they

leave signiﬁcant latitude for the data scientist in ﬁnalizing the

network to be trained and deployed.

The most valuable contributions came to be known as the “bag

of tricks” of deep learning: small, modular tweaks that improve

parts of a network rather than redeﬁning the whole. Weight ini-

tialization strategies, moment estimators, learning rate scheduling,

dropout, early stopping, residual connections, dense connections,

attention mechanisms, transformer architectures, batch normaliza-

tion, gradient accumulation, hyperparameter optimization, data

augmentation, positional encoding, pruning, quantization, knowl-

edge distillation, low-rank factorization, among others, form only

a small subset of this growing “bag of tricks”. However, these con-

tributions—at least the signiﬁcant ones—proved highly accretive.

The scikit-learn open-source project, initiated by French data scientist David

Cournapeau in 2007, exempliﬁes this approach.

242 CHAPTER 6. INTELLIGENCE

One technique seldom precludes another unless they belong to the

same class.

Since the 1960s, many paradigms—functional, object-oriented,

distributed, reactive, and more—have tackled general software

concerns. Specialized paradigms have also emerged, such as rela-

tional algebra (e.g., SQL for relational databases). While debates

continue over which paradigm is “best”, no serious software project

is undertaken without embracing one or more paradigms. Merely

choosing a programming language already constitutes a paradig-

matic choice.

The key takeaway for supply chains: choosing the right paradigm

matters even outside pure software work. Deep learning, as a pro-

gramming paradigm, has redeﬁned the state of the art in machine

learning. This raises the question of whether other domains, such

as supply chain, could beneﬁt from similarly dedicated paradigms;

as we shall see, dedicated programming paradigms are of primary

importance for supply chain.

6.3.5 Generative Era

In 2022, a handful of breakthrough releases—soon grouped under

the banner “Generative AI”—vaulted from niche research to global

headlines. ChatGPT, the conversational assistant unveiled by

OpenAI in November 2022, and Stable Diﬀusion, the text-to-

image model released in August 2022 by researchers at LMU

Munich and Heidelberg University, became instant landmarks

in text completion and image generation, respectively. Their

apparent overnight success masked years of steady, open-ended

progress by the wider deep-learning community; what changed

in 2022 was not the underlying science, but the packaging. For

the ﬁrst time, non-specialists could summon large, pre-trained

models through a simple web interface and obtain striking results

in seconds—a watershed that turned an academic curiosity into a

mainstream product category. Although these systems are squarely

built on deep-learning foundations, they also challenge some of the

paradigm’s early assumptions—for example, about data quality and

6.3. SPECIALIZED INTELLIGENCE 243

loss-function design. In that sense, the “generative era” represents

a genuine second generation of deep-learning practice rather than

a mere extension of the ﬁrst.

First, vendors began shipping pre-trained models by default—a

radical, albeit unexpected, advance. With pre-trained models, end-

users need no longer concern themselves with generating models

(the learning phase) and can focus on using them (the inference

phase). This greatly eased adoption. It also paved the way for

hybrid learning processes, in which a deep learning network could

be assembled from pre-trained components and supplemented with

a trainable section. Although staged learning processes predate

deep learning, the generative paradigm added a new dimension

and prompted the machine learning community to adopt further

open-source practices, sharing models in addition to code.

Second, the hallmark of the generative era is an almost insa-

tiable appetite for web-scale data. For decades the adage was

“garbage in, garbage out” (GIGO): low-grade inputs doom a

model’s outputs. Yet today’s text- and image-generators train

on billions of lightly ﬁltered pages and pictures scraped straight

from the open web, and still return prose and visuals that eclipse

the average quality of that raw material. Their results are hardly

ﬂawless, but they are unmistakably better than the median of

their sources—an outcome that overturns the older GIGO credo.

Harnessing such oceans of data, however, is only possible when

the software is written with deep mechanical sympathy, squeezing

every cycle and memory bank the hardware can oﬀer.

Third, ever-larger models display emergent skills for which their

loss functions were never designed. For example, once large lan-

guage models (LLMs) reach billions of parameters and are trained

on billions of tokens, they start exhibiting translation capabilities.

Yet the loss function reﬂects only next-token prediction; it does not

account for translation. Similarly, text-to-image models exhibit

emergent capabilities such as style transfer that go beyond their

training data.

Those abilities upend an assumption borrowed from operations

research. Until then, it was generally assumed that the best way

244 CHAPTER 6. INTELLIGENCE

to solve an optimization problem was to steer the process by

the very metric used to evaluate the ﬁnal solution. Proxy loss

functions—approximations of the true loss—were acceptable only

when they oﬀered signiﬁcant computational beneﬁts—a trade-oﬀ

between solution quality and resources. However, the emergent

capabilities of generative models have repeatedly surpassed prior

state-of-the-art methods driven by more direct loss functions.

In the generative era, the relationship between the score for

a given task and the loss function used to drive stochastic gra-

dient descent has become tenuous. Self-supervised learning has

supplanted supervised learning, which had dominated until then.

The intuition behind self-supervised learning is that the quantity

of unlabeled data

typically dwarfs the amount of labeled data.

Thus, if learning is decoupled from the narrow task of predicting

“labels” and instead focuses on intrinsic properties of the data, it

can better capture underlying structure.

While generative models have redeﬁned the state of the art in

machine learning, leveraging them for supply chain purposes is not

as self-evident as many software vendors claim. Relational records

constitute the bulk of data relevant to supply chain, yet neither

text completion, text-to-image, nor image-to-text models are well

suited for processing such data. Two propositions commonly put

forward by vendors in the early 2020s warrant refutation.

First, large language models ﬁt text, not numerical time-series

forecasting. Although there are proposals

to enhance the arith-

metic capabilities of such models, these developments are unlikely

to render language models competitive with models speciﬁcally

engineered for numerical processing. At best, large language mod-

els may become an extremely convoluted way to achieve what

simple parametric models can accomplish with a tiny fraction of

In machine learning, “labels” refer to known instances or known solutions

to a given learning task. For example, in automated translation from French to

English, the label for a French text is its corresponding, accurately translated

English text.

See xVal: A Continuous Number Encoding for Large Language Models (2023)

by Golkar et al.

6.3. SPECIALIZED INTELLIGENCE 245

the computing resources.

Second, chat-style interfaces usually hurt productivity. Convers-

ing with a chatbot is signiﬁcantly slower than traditional navigation.

Any task intended to be repeated routinely deserves a “traditional”

user experience rather than a cumbersome conversational interface.

In an enterprise environment, employees are expected to be trained

and proﬁcient with the system—not to “muddle through” it. The

enterprise perspective diﬀers fundamentally from the end-consumer

perspective, where users are assumed to be less knowledgeable re-

gardless of the task.

In supply chain, large language models shine at drafting nu-

merical recipes and smoothing the informal work that surrounds

decisions; we will revisit these contributions later.

246 CHAPTER 6. INTELLIGENCE

Chapter 7

The Future

Every supply chain decision carries an implicit claim: “we expect

the company’s future conditions to make today’s allocation of

resources proﬁtable.” The prediction may remain unsaid, but it

cannot be evaded. A ﬁrm may ignore the forecasting challenge; it

cannot ignore the consequences.

Thus, the future—and, more generally, time—matters greatly

to supply chain. Yet time is deceptively diﬃcult—not because it is

too abstract, but because it appears self-evident and resists delib-

erate thought. The future is akin to the air we breathe: ubiquitous

and yet invisible.

With such topics, one is tempted to take pre-

conceptions for granted and skip the intellectual work altogether.

Chemistry taught us that millennia-old preconceptions about air

were wrong

. Just so, in supply chain, unexamined beliefs about

tomorrow—often a tacit extrapolation of yesterday—quietly shape

prices, capacities, and inventories. When they are wrong, they

While chemistry has advanced to where it is common knowledge that “air”

is 78% nitrogen and 21% oxygen, with the remainder mostly argon and water

vapor plus traces of other gases, it still takes a deliberate eﬀort to bring those

facts to mind.

Ancient Greek philosophers like Aristotle classiﬁed air as one of the four

fundamental elements—alongside earth, water, and ﬁre—without recognizing

its composite nature. Only in the late 18

century, through scientists such as

Antoine Laurent de Lavoisier, was air’s true composition established.

247

248 CHAPTER 7. THE FUTURE

harden into plans that misallocate scarce resources and propagate

failures across the ﬂow.

To anchor the discussion, this chapter connects the simple

observation—every decision is a bet on tomorrow—with the in-

struments that make such bets proﬁtable. It proceeds in three

moves.

First, we surface the unspoken visions that steer how ﬁrms think

about the future. Much twentieth-century practice descends from a

teleological vision: settle tomorrow by forecasts and an engineered

plan. By contrast stands a rugged vision: treat tomorrow as a

ﬁeld of opportunities under uncertainty and preserve options so

the ﬁrm can move when the odds turn favorable.

Second, we examine why forecast-driven planning collapses

in business even when it looks mathematically tidy: time-series

formalism ﬂattens what matters; the future is shaped by decisions

not yet taken; small numbers, batching, adversarial behavior, and

fat tails shatter the neat symmetry between past and future. The

result is brittle orchestration and bureaucratic ritual.

Third, we assemble predictive instruments that ﬁt the rugged

vision and the economics of optionality introduced earlier in the

book. We replace point time series with probabilistic forecasts (de-

mand, lead times, prices, returns), add high-dimensional forecasts

(market baskets, substitution, contagion), and introduce functional

forecasts that speak in the native objects of the ﬂow rather than

in equispaced time buckets. These tools do not guess “the” future;

they price alternatives and make opportunity costs explicit.

7.1 Visions

[A vision] is what we sense or feel before we have constructed

any systematic reasoning that could be called a theory [..]

A vision is our sense of how the world works [..] Visions

are all, to some extent, simplistic – though that is a term

usually reserved for other people’s visions, not our own. A

Conﬂict of Visions (1987), Thomas Sowell.

7.1. VISIONS 249

Of all the unspoken ideas that drift around us, the most power-

ful shape our intuition of causality for the objects at hand—here,

supply chains. Indeed, this intuition deﬁnes how we frame situ-

ations, how we see problems—whether we see them at all. Here,

vision refers to that intuition of causality.

Vision precedes theory, which precedes models. Visions perme-

ate companies—their cultures, practices, and processes. Visions

matter because they serve as a rough compass for thought. Amid

the inﬁnite subjects that could claim our attention, visions decide

what is worth thinking about.

For example, “Day 1”, as advocated by Jeﬀ Bezos, Amazon’s

founder, is one such vision. It emphasizes a perpetual startup

mindset—continuous innovation and proactive action—to avoid the

stagnation of “Day 2”. It means treating every day as if it is the ﬁrst,

encouraging rapid decisions, embracing experimentation—even at

the risk of failure—and favoring long-term opportunities over short-

term gains. By adhering to the “Day 1” mindset, Amazon aims to

sustain its entrepreneurial spirit and avoid the complacency that

plagues larger, more mature organizations.

Yet the opposite vision also has its proponents. As Captain

Chesley “Sully” Sullenberger—famed for the Hudson River emer-

gency landing that saved 155 lives—remarked, “For 42 years, I’ve

been making small, regular deposits in this bank of experience,

education, and training. And on January 15, the balance was

suﬃcient for a very large withdrawal.” This view dominates avia-

tion, combating complacency not through disruptive innovation

but through relentless reﬁnement of existing practice. Past failures

were paid in blood, and future ones will be as well.

Visions govern how people instinctively approach complex sys-

tems—systems beyond what a human mind can readily grasp in

full. Consider a retail store struggling to maintain adequate stock,

with half its shelves empty. Instinctive diagnoses will vary greatly,

depending on one’s vision of how supply chain works.

Take a professor of supply chain analytics. He may instinctively

blame inaccuracies in the demand forecasts driving replenishment.

Here, the vision places the burden of service quality on a techno-

250 CHAPTER 7. THE FUTURE

logical solution—on software. This vision extends to his academic

community, whose research inﬂuences the design and accuracy of

such software, thereby reinforcing a technology-centered view.

By contrast, a regional manager in the same chain may blame

poor store stewardship. In this vision, people—the store manager

and staﬀ—are responsible for running the store eﬃciently. Respon-

sibility lies ﬁrst with those closest to the problem. This vision also

implicates upper management: they allow an ineﬀective store man-

ager to persist in his position, further underlining a people-focused

view.

These two visions arise from the same empty shelves but assign

responsibility—and, consequently, resolution—to entirely diﬀerent

quarters. One turns to a technological solution, the other to a

management solution. A third, equally plausible diagnosis lies

outside the ﬁrm: rampant shoplifting makes displayed inventory

vanish. Naturally, whether the store’s problem stems from faulty

software, poor management, or an adverse public-order context is

another matter entirely. Visions prove nothing; they merely govern

our immediate assessment of complex situations.

Visions are consequential. History shows that many businesses

came to dominate their markets thanks to the peculiar visions of

their founders—“visionaries,” aptly named

. Yet the same visions

can, over time, set companies up for failure. Thus, for any company,

it is reasonable to identify the dominant visions held by upper

management and assess their ﬁtness for the business.

Unlike corporate strategies, visions are rarely discussed, let

alone weighed for merits and demerits. Within the same company,

people may hold radically diﬀerent visions. Because visions are

rarely spelled out, employees feel that whenever they push, others

pull—and vice versa. Yet divergence in vision is seldom named as

the cause.

Standard Oil, General Electric, Ford, Kodak, IKEA, McDonald’s, Apple

are but a few that became, at some point, vision-led giants.

7.1. VISIONS 251

7.1.1 Values

In politics as well as in business, leaders often highlight divergent

values to underscore diﬀerences with their rivals. The phrase “We

do not have the same values” can be heard on all sides. Yet this

perspective, while not without merit, tends to obscure the profound

inﬂuence of visions.

In A Conﬂict of Visions (1987), Thomas Sowell astutely notes

that when people meet diﬀering interpretations of the same facts,

they tend to attribute the diﬀerences to values. Often the variation

in values is far less pronounced than the catchphrase “We do not

have the same values” suggests. In the political realm, for instance,

one would be hard-pressed to ﬁnd anyone advocating for poverty,

crime, or war. Yet despite shared values against these ills, people’s

visions guide them toward starkly diﬀerent solutions.

The same holds in supply chains. Regardless of ﬁeld or sector,

companies seek proﬁtability, growth, service quality, and waste

reduction. Companies that openly oppose such values are rare, if

they exist at all. Diﬀerent visions yield markedly diﬀerent strategies

and practices, all aimed at the same values.

Consider Amazon’s founder, Jeﬀ Bezos, who has often empha-

sized a relentless focus on the customer. He once said, “The most

important single thing is to focus obsessively on the customer. Our

goal is to be Earth’s most customer-centric company.” That is a

statement of values—and a fairly mundane one. Indeed, execu-

tives rarely devalue customers in public. When an executive is

caught doing so, he rarely keeps his position afterward. What

distinguishes Amazon is not its espoused values—which align with

most businesses—but its distinctive vision and culture.

Thus, as we assess how understanding the future shapes supply

chain practice, we must remember that men may propose radically

diﬀerent solutions while sharing the same values. More often than

not, the divergences are rooted in conﬂicting visions.

252 CHAPTER 7. THE FUTURE

7.1.2 Time

The passage of time shapes our experience and our understanding of

the world. It is so concrete and omnipresent that abstracting away

the dimension of time is diﬃcult. Yet many—if not most—supply-

chain challenges deserve this treatment, if only to surface the

underlying visions at work with regard to time. Thus we pose

a series of questions about time. At this stage, answering those

questions is unimportant and will not be attempted. Instead, we

aim to characterize the visions—the intuitions of causality.

Do we see the future as knowable or unknowable?

Forecasting is only as valuable as the portion of tomorrow

that can be anticipated in practice. When the future is at least

partly knowable, forecasts are powerful instruments; when it is

dominated by noise, they are mere ceremony. Planning inherits

the same premise: it pays only if some features of the future can

be foreseen reliably enough to guide commitments. If outcomes

routinely diverge from expectations, planning eﬀort turns to waste.

Do we see the future as fated? Or open to change?

If the future is ﬁxed, a forecast is an early measurement of a

state that will occur; accuracy is judged by its match with the

realized outcome. If the future is malleable, a forecast acts as

a directive shaping behavior; accuracy is judged by how closely

execution conforms to the plan.

Do we see uncertainty as a ﬂaw or as risks and opportunities?

Few dispute that the future is uncertain. One view treats that

uncertainty as a defect to be minimized—via sharper forecasts,

tighter controls, and prompt corrective actions. The other treats it

as raw material—to be priced, hedged, and occasionally embraced

when odds favor it.

Do we see acting sooner as preferable to deferring until a later,

better-informed decision?

Momentous decisions made on the spot strike some as rash and

others as decisive. Likewise, inaction reads either as weak-willed

hesitation or as strength to wait for better information.

Do we see knowledge as a characteristic of what doesn’t change,

7.1. VISIONS 253

or as the speciﬁc circumstances of time and place?

Scientiﬁc knowledge aims at permanence: theories are cast as

timeless claims, and reproducibility assumes a phenomenon that

does not change with date or venue. Business knowledge is the

opposite. Once a fact is widely known, its private value collapses.

What creates an edge is the speciﬁc circumstances of time and

place—who wants what, at what price, given today’s capacities

and constraints. Valuable knowledge is therefore local, timely, and

perishable.

Do we see the past as a foundational asset to build upon, or a

ﬂawed legacy steering people toward complacency?

Founders may start from a blank page; every successor inherits

a status quo. One stance treats that legacy as a compounding

asset—skills, supplier ties, data, and routines that, when reﬁned,

reduce risk and cost. The other treats it as a liability—sunk costs,

ossiﬁed habits, and internal constituencies that bias choices toward

yesterday’s market.

Do we see investment in people as indeﬁnitely accretive, or is

accretion reserved for technologies and techniques?

Some see human capability as compounding. With practice,

feedback, and better mental models, planners keep getting faster

and more accurate; thus spending on training is a capital-like

outlay. Others expect an early plateau: once competence is reached,

durable gains come chieﬂy from improved techniques and software;

investment should therefore target tools rather than people. The

stance taken strongly governs what counts as an “investment” in

the ﬁrst place.

These questions are, of course, false dichotomies: the truth may

lie between the poles, vary with context, or lie elsewhere entirely.

Far from mere philosophical musings, these questions show that

one’s intuition about time—about the future—is consequential.

Diverging visions do not merely produce divergent solutions: people

may not even agree on the problems worth solving.

Within supply chain circles, views of time and the future have

largely converged into what we call the teleological vision: the

belief that tomorrow can be ﬁxed in advance by forecasts and

254 CHAPTER 7. THE FUTURE

then engineered into existence through compliance with a plan.

Over the 20

century, this view became dominant and remains the

default in the 21

century. It is seldom named, rarely questioned,

and silently assumed by most authors and enterprise software

alike. The next section makes this vision explicit and examines its

consequences.

7.2 The teleological vision

The future remains inaccessible only to those who have

not embraced the mathematical instruments that modern

science oﬀers. Whatever weaknesses still plague our present-

day forecasting models will ultimately be remedied by the

unstoppable force of progress. Furthermore, those instru-

ments not only tell us what will come to pass but also why.

Thus, the modern leader, unlike his primitive predecessor,

leaves nothing to chance. His plans are no longer rooted in

the fallible intuition of men; rather, they are engineered to

the precision that characterizes this machine age.

These lines capture the essence of the teleological vision

. Over

the course of the 20

century, this vision became the dominant

view in intellectual circles. By intellectuals we mean people who

make a living selling their ideas (writers, historians, academics),

as opposed to selling tangible services or products. This deﬁnition

passes no judgment on intelligence; it simply labels occupations.

For example, a journalist is an intellectual, while a neurosurgeon

is not. However, the neurosurgeon can be expected to be more

educated and more intelligent than the journalist; such is the

expectation of every patient placing his life in the surgeon’s hands.

Once a view grips intellectual circles, it often spreads and shapes

public opinion

. The teleological vision became dominant—if

not exclusive—within supply chain circles, among authors and

The Oxford dictionary deﬁnes “teleological” as “relating to or involving the

explanation of phenomena in terms of the purpose they serve rather than of the

cause by which they arise.”

See Intellectuals and Society (2009) by Thomas Sowell.

7.2. THE TELEOLOGICAL VISION 255

practitioners alike. Enterprise software vendors entrenched this

vision through workﬂows and technologies that left little or no

room for alternative views.

Unfortunately, this vision is as popular as it is ﬂawed. Despite a

mountain of contrary evidence, it remains one of the most seductive

economic ideas of all time. The prospect of weeding this vision out

of supply chain is daunting but necessary. We must ﬁrst examine

where this view came from and how it spawned its supply-chain

oﬀshoots.

7.2.1 Historical emergence

Modern quantitative forecasting

—as opposed to the traditional

soothsayer, augur, oracle, or prophet—emerged at the beginning

of the 20

century in the United States with the ﬁrst generation

of economic forecasters. Demand for forecasts was fueled by the

emergence of an investing middle class unique to the United States

at the time. At the start of the 20

century, only half a million

Americans owned stocks; by the end of the 1920s, that ﬁgure rose

to 10 million. Those early forecasters were conﬁdent that their

techniques would ultimately make economic uncertainty a relic of

the past:

Week after week, his [Roger Babson] newsletter explained

to its readers that economic events were neither random nor

uncertain but governed by forces that could be understood

through careful study. He began issuing regular forecasts

after the Panic of 1907. [. . . ] He oﬀered prognostications

on everything from prices to employment to sales. —Walter

A. Friedman, Fortune Tellers (2013)

The modern ﬂavor of weather forecasting predates economic forecasting by

more than half a century, dating back to the invention of the electric telegraph

in 1835. By the late 1840s, weather forecasts could be made from knowledge of

weather conditions farther upwind, because information traveled faster than the

winds themselves. However, weather forecasting belongs to the natural sciences,

whereas economic forecasting does not.

256 CHAPTER 7. THE FUTURE

Babson’s swagger set the template for economic pundits through-

out the century. Babson’s core idea was to adapt Newton’s third

law

—also known as the law of action and reaction—to economics.

He held that it would ultimately render the course of the econ-

omy as predictable as that of celestial bodies. While now widely

regarded as naive and misguided, mid-20

century forecasters re-

mained as conﬁdent as Babson, shifting their trust from Newtonian

mechanics to statistics.

The Soviet Union’s Gosplan, founded in 1921, began issuing

annual plans in 1925. The committee drew directly on a teleologi-

cal vision that the future could be conquered. Yet the committee

produced nothing but unrealistic plans that could not be executed.

The results proved disastrous. The Soviet economists Nikolai

Shmelev and Vladimir Popov write: “The administrative system

was distinguished by economic romanticism, profound economic

illiteracy, and incredible exaggeration of the real eﬀect that the

‘administrative factor’ had on economic processes and on the moti-

vations of the public.”

Popular culture echoed these beliefs: Isaac Asimov’s science-

ﬁction series Foundation (1951) captures the prevailing views of

the era’s intellectuals. The central storyline turns on a ﬁctitious

theory, “psychohistory”, blending particle physics and sociology,

said to achieve near-perfect predictive power when vast numbers

of humans are involved. However, Asimov concedes that some

individuals can counter psychohistory’s predictive power. It takes,

however, what amounts to a demigod—a human blessed with

immense psychic powers—to do so.

Most twentieth-century intellectuals remained supremely con-

ﬁdent in the ultimate predictability of human aﬀairs, even as

economic forecasts kept failing. While Gosplan’s failure can be

attributed, in part, to the general ﬂaws of socialism, market-

driven American pioneers such as Moody’s and Standard & Poor’s

also suﬀered setbacks. Both began by selling investment publica-

Newton’s third law states that when two bodies interact, they apply forces

to one another that are equal in magnitude and opposite in direction.

7.2. THE TELEOLOGICAL VISION 257

tions—manuals, company digests, and market letters that included

explicit forecasts—rather than merely tabulating past accounts.

By mid-century, both ﬁrms had pivoted to credit rating—a “safer”

business that spared them the embarrassment of inevitably faulty

forecasts.

Forecasting setbacks did not slow operations research: as the

ﬁeld rose in the 1950s and 1960s, it embraced the teleological

view. The future was taken as a known input to the model,

either under stationary conditions or via a time-series forecast.

By the end of the 1970s, time series had become the cornerstone

of quantitative supply chain theories. The prominence of time-

series forecasts increased further with the rise of S&OP (Sales and

Operations Planning) at the end of the 20

century. Their status

and importance remained essentially unchallenged until the early

2010s.

7.2.2 Time-series paradigm

A time series is a sequence of values of a quantity observed at succes-

sive times, typically at equal intervals

between them. Extending

the series beyond today yields a forecast—speciﬁcally, a time-series

forecast. While time series can represent anything—from sunspot

counts to a country’s population—most supply chain authors and

software use them primarily to represent demand, measured in

units (eaches) or in monetary terms.

A time series is an unbounded sequence of numbers laid out

on a ruler, with “0” marking today. Everything to the left has

already happened; everything to the right has yet to be inferred. In

practice, we ﬁll those slots with forecasts, treating time in separate,

evenly spaced steps (days, weeks, and so on).

This formalism clariﬁes a few assumptions that underpin the

A time series featuring equal time intervals is said to be equispaced. In

practice, non-equispaced time series are so rare in the literature that any

reference to a time series implicitly assumes equal intervals. However, note that

monthly and yearly intervals are not strictly “equal”: both months and years

have varying numbers of days.

258 CHAPTER 7. THE FUTURE

Time

Value

Present

Figure 7.1: Historical time series (solid) up to the present, and its

forecast (dashed).

time-series paradigm. First, time series have no beginning and no

end, like a geometric line. Of course, any visual representation is

truncated to a ﬁnite segment for practical reasons. However, this

fragment should not be confused with the whole.

Second, time series are structurally symmetrical with regard to

their time dimension. By “structural” we do not mean past and

future values coincide, only that they are of the same nature. They

are undiﬀerentiated save for the cutoﬀ at time zero, a matter of

convention.

Third, time series adopt a discrete time axis. This choice

drastically simpliﬁes both the display of time series and the models

built atop them, whether analytical or predictive.

These three assumptions match the prevailing view in the

natural sciences. A meteorologist studying temperatures at a given

location adopts the same paradigm. Though records begin at some

date, the meteorologist does not doubt temperatures could have

been recorded earlier. Conversely, measurements can continue

indeﬁnitely. Although temperature evolves continuously, records

are deliberately discretized—for example, one daily measurement

at a ﬁxed hour—to ease analysis. Finally, the physical laws are

7.2. THE TELEOLOGICAL VISION 259

nearly perfectly time-symmetric

with respect to time.

It is hard to overstate the time-series paradigm’s dominance in

supply-chain circles at the beginning of the 21

century. Nearly

all forecasting literature revolves around demand time-series fore-

casting. Few authors acknowledge alternative forecasting tar-

gets

—beyond demand—and fewer still acknowledge alternative

forecasting structures—beyond time series. Forecasting has eﬀec-

tively become synonymous with “demand time-series forecasting”.

Similarly, most “applied optimization”—the heir to operations

research—takes time series as input whenever possible. Again, few

acknowledge alternative perspectives.

On the software side, most reporting systems rely on OLAP

(online analytical processing) cubes that, by design, embrace the

time-series paradigm. Among the cube’s dimensions, one is invari-

ably time. Crucially, such cubes are incompatible with alterna-

tive—more expressive—structures beyond time series.

On the management front, the widespread practice of S&OP

(Sales and Operations Planning) puts time series squarely at the

center. Cross-functional teams convene to produce a single deliver-

able: the strategic plan. In practice, that plan is a bundle of time

series—usually the ﬁrm’s projected sales—arrayed across future

time buckets.

Nearly all fundamental laws of physics, such as Maxwell’s equations, are time-

reversal symmetric. Reversing time (

t 7→ −t

) leaves their equations unchanged.

However, the Standard Model exhibits time-reversal violation via the weak force

(CP violation). TCP violation was discovered and experimentally veriﬁed in

the 1960s. Nevertheless, it remains an advanced consideration, largely ignored

outside physics—especially at the “vision” level.

A Google Scholar survey over roughly the past 50 years (query run in 2024)

indicates a ratio on the order of 1,000 papers mentioning “demand forecasting”

for every paper mentioning “lead time forecasting.” The imbalance is striking

given that nearly every ﬁrm operating a supply chain must anticipate both

future demand and future lead times.

260 CHAPTER 7. THE FUTURE

7.2.3 Planning through time-series

In business, a time-series forecast is not a neutral projection as in

the natural sciences; it doubles as a commitment. Once a forecast

is sanctioned, it becomes the baseline that sets budgets, reserves

capacity, and schedules replenishments. The organization then

works to make those numbers hold; the forecast becomes less a

guess about tomorrow than an instruction to shape it.

Once forecasts are set—future demand treated as known—all

that remains is to orchestrate resources to meet it promptly at

minimal cost. In the teleological view, planning means ﬁxing future

demand and assigning the matching resources.

Indeed, if the future is treated as settled—one known daily

demand for every SKU at every location—then the planning prob-

lem collapses into arithmetic: the material decisions (what to buy,

make, or move; where; when; and how much) are mechanically

inferred from the forecast itself

. Execution then reduces to

gauging compliance with “the plan”. For example, if tomorrow’s

e-commerce forecast for a given SKU at the fulﬁllment center is

exactly 2 units, stocking fewer is a planned stockout; stocking more

is, by deﬁnition, avoidable overstock that a more precisely timed

replenishment “should” have prevented.

Once the forecast is sanctiﬁed as “the plan,” the ﬁrm’s agency

migrates to the ritual of ﬁxing that number. The forecast sits on

the throne; everything else is compliance. Hence the turf wars

around the baseline—not a dispute about statistical merit, but a

budget negotiation in disguise. To forecasters, the wrangling looks

perverse; to managers, “accuracy” is incidental. The real prize

is a larger share of capital, capacity, and attention for one’s own

ﬁefdom.

Orchestration then runs into a structural mismatch: forecasts

This perspective is exempliﬁed by the book Flowcasting the Retail Supply

Chain (2006) by American supply chain consultants Mike Doherty and Andre

Martin. The ﬂowcasting approach consists of establishing daily forecasts at the

store level, SKU by SKU, and of deriving all the allocations across the multiple

echelons of the network from those “foundational” forecasts.

7.2. THE TELEOLOGICAL VISION 261

are cast in one geometry, resources in another. The baseline

may be weekly by SKU, while trucks depart daily and never on

Sundays; production is batched around changeovers; purchase

orders obey MOQs and price breaks. A small army of planners

is therefore tasked with back-scheduling every detail—shifting

quantities across days, squeezing orders before cutoﬀs, padding

here, trimming there—so the ledger appears aligned with the

baseline. To an outsider this bustle looks like “making decisions”.

In truth, discretion is minimal: choices are bounded by compliance

with the plan, and the remaining degrees of freedom are clerical

rather than economic.

This view also explains why pricing is almost always ignored by

supply chain authors, practitioners, and software vendors. Indeed,

because prices aﬀect demand, they belong within supply chain—as

we maintain throughout this book—yet the classic stance, following

the teleological view, ignores them altogether. Prices are simply

expected to be settled prior and independently of the planning

phase. To the traditional supply-and-demand planner, prices are a

given—neither to be assessed nor challenged.

This view of planning—i.e., a forecast steering the allocation

of resources—is so dominant in present-day supply chain circles

that few even acknowledge alternatives. Disagreements are nar-

rowed to the choice of forecasting methods, inventory models, and

orchestration workﬂows. Yet, despite its popularity, teleological

planning has demerits and deserves scrutiny.

7.2.4 Limits of planning

So let us call here the teleological fallacy the illusion that

you know exactly where you are going, and that you knew

exactly where you were going in the past, and that others

have succeeded in the past by knowing where they were

going. Antifragile: Things that Gain from Disorder (2012)

by Nassim Nicholas Taleb

Planning, as it emerges through the teleological vision, is deeply

ﬂawed. Still, despite a century of contrary evidence—most noto-

262 CHAPTER 7. THE FUTURE

riously the ﬁascos of the Soviet Gosplan—it remains the central

doctrine of large organizations, public and private. Few ideas have

seduced the intellectual circles of the 20

century so thoroughly

while delivering so little. Before examining why this fascination en-

dures, we ﬁrst clarify planning’s technical shortcomings, beginning

with the inadequacies of time series.

As a mathematical structure, time series are ill-suited to reﬂect

business situations. It is tempting—but profoundly misguided—to

recycle time series as used in the natural sciences. The crux of the

issue is that, in business settings, time series categorically ignore

entire facets of the phenomenon under scrutiny.

Consider the meteorologist taking one temperature measure-

ment every day at the same place—the time series of interest.

The meteorologist knows that the measurement itself could be

made more precise with slightly better equipment. He knows the

measurement could be taken twice, a minute apart, or 100 meters

apart. None of these improvements, however, is expected to change

the picture radically. It is always possible to make measurements

more precise or more granular—but with diminishing returns. The

time series is an approximation, yet the meteorologist controls its

nature and extent. The meteorologist is never “seconds away” or

“meters away” from a radical breakthrough that would upend his

understanding of the weather.

Business doesn’t play by those rules. Seemingly minor measure-

ment changes can radically alter the assessment of the situation.

Consider a company that has sold the same product for years. Sales

are steady—about 1,000 units per week—with little ﬂuctuation.

What are the odds that the demand for this product will drop

to zero next week and remain at zero forever? To answer this

question, let’s consider two situations, both exhibiting identical

historical time series of weekly sales.

Consider two demand structures producing the same weekly

total of about 1,000 units: Case A—1,000 independent customers,

each buying roughly one unit per week; Case B—a single cus-

tomer buying roughly 1,000 units per week. The historical time

series—weekly totals over years—looks identical in both cases, yet

7.2. THE TELEOLOGICAL VISION 263

the tail risks diverge. In Case A, taking next week’s sales to zero

would require losing essentially all customers, a process that—short

of catastrophe—unfolds over many weeks or months. In Case B,

one decision by one buyer suﬃces; the hazard of complete collapse

is the churn probability of that single account.

Consider the inventory implications. In Case A, declines typ-

ically appear gradually, allowing stocks to be trimmed as churn

reveals itself; write-oﬀs tend to remain contained. In Case B, a

large write-oﬀ is a matter of when, not if : once the sole buyer

defects, the stock accumulated for them becomes overhang. Thus,

two situations with identical weekly sales time series entail opposite

risk proﬁles—and call for diﬀerent decisions.

Consider a second setting: a store sells an average of 10 units per

week of a given SKU. The store is open seven days and replenished

daily. What on-hand level delivers reliable service? The point

forecast for daily demand is

≈

4 units, yet two demand structures

sharing the same time series imply opposite stocking needs.

Variant A — many small baskets. Ten distinct customers each

buy one unit during the week. With daily replenishment, keeping

roughly 5–6 units on hand suﬃces for most days: one day of cycle

stock plus a modest buﬀer covers ordinary noise.

Variant B — few large baskets. One customer per week buys 10

units at once. Holding fewer than 10 units guarantees a stockout

for that visit; to achieve decent service, you need at least 10,

and because two such customers may arrive on the same day,

20—possibly 30—is the relevant range.

The time series is identical in both variants, yet the econom-

ically sensible stock position diﬀers by a factor of four or more.

What matters is the distribution of basket sizes, not the average

parceled into daily buckets.

As the two examples above demonstrate, even for a single

SKU, time series ﬂatten critical aspects of the ﬂow. Time series

are a lossy representation of transactional history: the projection

discards information, and unlike meteorology, what is lost often

proves essential. Consequently, the resulting plans are invariably

dysfunctional because they ignore critical aspects of the situation.

264 CHAPTER 7. THE FUTURE

As the weakness lies in the time-series paradigm itself, it is delu-

sional to think the matter can be cured by greater sophistication.

Adding more series, extending their horizon backward or forward,

increasing granularity, or improving point accuracy won’t ﬁx the

problem.

Many more similar examples could be adduced. Even for a

single SKU, time series fail: with perishables (each unit has its own

shelf life); with repairables (each unit has its own life cycle); with

reverse logistics (returns create confusion); with product launches

(no series yet), etc. Indeed, it is hard to ﬁnd a vertical where a

sizable share of the business is incompatible with the time-series

paradigm—and thus with “planning”.

These single-SKU caveats do not vanish at scale; they explode.

In a sizable ﬁrm with thousands of SKUs, the time-series lens—and

the planning rituals built on it—produces the same errors, only

magniﬁed. Consider a sportswear company with its own stores

that, among many items, sells a black backpack. Under the plan-

ning view, the ﬁrm prepares a 12-month sales projection for that

backpack and sets production accordingly, then repeats the exer-

cise across the assortment. The practice is so common that few

question it, yet its premise is unsound.

The projection is nonsensical. Prominent in-store placement

would lift demand; conﬁning the backpack to e-commerce would

suppress it. If the company introduces many backpack vari-

ants—colors and sizes—the variants will cannibalize demand for

the original backpack. If the company adopts an aggressive price,

making the backpack the tip of a marketing campaign to draw new

customers into the store network, demand will be far higher than

if it is merely one unremarkable product within a vast assortment.

The plan doesn’t make sense because each projection is con-

ditional on decisions that have yet to be made, yet the exercise

proceeds as if those decisions had already been made. Once the

plan is issued, clerks must reverse-engineer their choices to make

subsequent commitments appear to conform to it. In practice, the

plan is not impossible; it is made “feasible” only through costly

approximations—expedite fees, excess buﬀers, artiﬁcial smooth-

7.2. THE TELEOLOGICAL VISION 265

ing, and deferred obligations—that keep the ledger aligned while

eroding economics. Feasibility here is an accounting artifact rather

than an operational truth.

There was nothing special or surprising about Gosplan’s pro-

ducing infeasible plans. This is a defect inherent in teleologi-

cal planning. Corporate executives, feudal lords, and political

commissars—adopting the same view—would likewise end up with

infeasible plans. To understand why, remember that supply chain,

at its core, is an economic process: allocating resources that have

alternative uses.

When conjuring a plan, managers implicitly command thou-

sands of scarce resources and expect them to be ready at the right

time, place, quantity, and cost. The odds of perfect alignment

between the plan’s innumerable expectations and the resources

actually available to the company—at every time and location

throughout the planning horizon—are essentially nil.

Time-series planning—and, more broadly, the teleological vi-

sion—does not merely fall short; it misdirects eﬀort. Yet well into

the 21

century, it remains the default in most companies and in

nearly every supply chain forum. How did a repeatedly refuted

notion become orthodoxy? The explanation lies less in logic than

in incentives. The next section examines those incentives—the

bureaucratic appeal of planning—and shows why the doctrine

endures despite its poor results.

7.2.5 Bureaucratic appeal

Corporations are in love with the idea of the strategic plan.

They need to pay to ﬁgure out where they are going. Yet

there is no evidence that strategic planning works—we even

seem to have evidence against it. A management scholar,

William Starbuck, has published a few papers debunking the

eﬀectiveness of planning—it makes the corporation option-

blind, as it gets locked into a non-opportunistic course of

action. Antifragile: Things that Gain from Disorder (2012),

Nassim Nicholas Taleb.

266 CHAPTER 7. THE FUTURE

The Gosplan might be gone, but its spirit lives on in large

corporations and government agencies. The teleological vision is

undeniably appealing, but not solely because of ignorance or stu-

pidity. Its staying power owes less to ignorance than to incentives

that reward plan theater; unless those incentives are named and

confronted, the doctrine will endure.

At its core, planning is a bureaucratic function executed by

back-oﬃce specialists. The function is unavoidable: the ﬁrm needs

a mechanism to allocate a tangled mix of resources across time

and locations. They seldom touch the physical ﬂow directly; they

orchestrate it indirectly. The allocation problem mirrors the ﬂow it

governs and is too intricate for any one person to manage end-to-

end. Because the work is analytical and data-heavy, specialists are

markedly more productive than generalists at it. Thus, spreading

the workload across the entire management layer is rarely viable;

the sound pattern is to centralize the task with a small cadre of

planners.

As in any bureaucracy, failure is punished—often harshly—while

success barely registers. For example, a successful salesman clos-

ing twice his expected yearly sales quota is in a strong position

to negotiate a steep raise. Yet a planner who halves the inven-

tory write-oﬀs of his peers rarely receives comparable recognition.

While a salesperson’s success is viewed as personal, a planner’s

achievements are seen as dependent on many other actors inside

and outside the ﬁrm, such as suppliers. Conversely, a salesperson

is seldom dismissed for missing a lucrative deal, whereas a planner

who triggers a major write-oﬀ may face severe career consequences

if the loss is traced to the plan he proposed.

Whether justiﬁed or not, such preconceptions pervade large

companies, particularly among middle managers. Consequently,

members of the planning bureaucracy soon learn that dodging ac-

countability is safer than claiming credit for the occasional success.

Predictably, a process-oriented culture displaces a results-oriented

one. This outcome is sustained by redeﬁning “good planning” as

adherence to codiﬁed processes. This twist makes the bureaucracy

nearly unassailable: success is redeﬁned as continual compliance

7.2. THE TELEOLOGICAL VISION 267

with rules that it wrote for itself.

Unfortunately, mere process compliance yields little tangible

return in planning. Corporate planning is unlike aviation safety,

where each now-rare crash is examined to identify the adjustments

that will prevent a repeat. Instead, planning mistakes are so

frequent and so signiﬁcant that such adjustments often introduce

at least as many ﬂaws as they ﬁx. As a result, mediocrity remains

a long-term characteristic of planning.

In particular, time-series forecasting creates an accountability

trap for the planning team. A point forecast is guaranteed to be

wrong at the point of use; when shortfalls or overages occur, the

miss is visible and, inside the ﬁrm, it accrues to the forecasters.

Bureaucracies avoid such single-owner exposure. The routine

response is to broaden “ownership” of the forecast—pulling sales,

marketing, ﬁnance, and operations into the ritual—not primarily

to sharpen the numbers, but to ensure that any error becomes a

collective one.

This maneuver typically appears as a large “collaborative” ini-

tiative that claims to improve forecast accuracy. S&OP (Sales and

Operations Planning) is one of the most popular ﬂavors of such

initiatives. S&OP’s endless meetings ensure that many other par-

ties co-sign the forecasts. There is no empirical evidence that such

meetings improve planning quality. In fact, evidence shows that

large committees almost always deliver inferior results, whatever

the task at hand.

In short, teleological forecasting appeals to clerks because it

lets them sidestep both decisions and consequences. Forecasts

serve as an occupational buﬀer that deﬂects responsibility for the

resulting resource allocations. Planners then tweak the forecast-

ing process—often by branding it “collaborative”—to dilute their

exposure even further.

Although a straightforward analysis lays bare the ﬂaws of the

teleological view, applying that critique to a speciﬁc company

is often hard. These ﬂaws are often masked by methodological

bolt-ons that give the planning process a “scientiﬁc” varnish.

Critique is not a method. Firms still need a compass—one that

268 CHAPTER 7. THE FUTURE

does not pretend tomorrow is a ﬁxed extension of yesterday. The

alternative is to treat uncertainty as an input rather than a defect,

to regard optionality as an asset to be cultivated and spent, and

to judge choices by their risk-adjusted contribution to proﬁt rather

than by plan ﬁdelity. This stance, common among entrepreneurs

and rare in bureaucracies, recenters agency on allocation rather

than on the forecast used to excuse it. We call this the rugged

vision, examined next.

7.3 The Rugged Vision

The future is irreducibly messy. Customers are ﬁckle, em-

ployees capricious, and suppliers unreliable. Yet rivals face

the same turmoil. You need not be perfect—only better.

Every bump is also a passing lane. Decisive action beats

elaborate planning, and luck rewards the prepared. Prepa-

ration, then, is the art of cultivating opportunities rather

than guessing tomorrow’s exact market.

These remarks distill the rugged vision. The teleological camp

treats the future as a journey that demands an ever-more-detailed

map—the forecast. If the map is precise enough, no other instru-

ment is needed: it alone should guide the traveler to a known

destination. Traveling without such a map feels reckless to its

believers.

By contrast, the rugged vision relies on continuous opportunity

assessment—a homing beacon, not a map. If that beacon is sound,

progress is a matter of moving steadily in its direction. Knowing

the destination in advance adds no speed; shifting terrain soon

renders static maps useless.

Even though entrepreneurs practice it daily, many intellectuals

dismiss the rugged vision. Their scorn springs less from doubts

about its eﬃcacy than from moral overtones, as though embracing

it reveals a ﬂaw in character.

The ﬁrst stigma arises from the implicit association of the

rugged vision with gambling and other forms of idle speculation.

7.3. THE RUGGED VISION 269

However, while the entrepreneur does take his chances, this isn’t

“idle” speculation, and the process is expected to be proﬁtable

for both the entrepreneur and his clients. For example, the en-

trepreneur who launches a product that becomes a massive hit may

be judged an undeserved success, as his economic contribution—to

the inattentive observer—seems minor. However, to achieve that

one success, he may have launched dozens of products, losing money

on each that didn’t hit. Furthermore, whether a product is a minor

variation or a radical improvement lies solely with consumers. A

major commercial success suggests that consumers found some-

thing distinctly appealing. Unfortunately, the costs of exploring

many angles to better serve the market are largely invisible to

outsiders, so success is often dismissed as pure luck—like a lottery

win.

The second stigma arises from characteristic behaviors that

many observers deem exploitative. Again, observers focus on the

beneﬁts—real or imagined—gained by the entrepreneur, skewing

their perception. First, in a free market a transaction occurs only

when both parties consent. Accordingly, no matter the price asked

by the seller, the buyer cannot be “exploited” unless he agrees to

the price

. For example, a ﬁrm that rushes portable generators

into a storm-damaged region may charge far more than the “normal”

price. Yet nothing about this is exploitative. Buyers are not forced

to buy the generators, and those who do judge themselves better

oﬀ with one than without

. More generally, in the rugged vision,

Most countries nowadays have all sorts of compelled transactions in place,

forcing people to pay for goods or services they didn’t choose: schooling,

healthcare, roads, vaccines, foreign aid, etc. Assessing whether those compelled

transactions are a net positive for society or not, and in which circumstances

they can be considered exploitative, is beyond the present scope. Let’s however

point out that those “transactions” have nothing to do with a free market.

The “price gouging” laws are the archetype of this ﬂawed understanding of

economics in general and of supply chains in particular. Those laws restrict both

the capacity of buyers to get what they want, when they want it the most—thus,

incurring a net loss for those underserved buyers—but also restrict the capacity

for the sellers to expand their oﬀering through extra investments that could

only be recouped if their prices are higher than usual.

270 CHAPTER 7. THE FUTURE

prices shape the ﬂow: buying low and selling high is its driving

force. Yet it is often miscast as exploitative because inattentive

observers don’t realize that “buying low, selling high” isn’t luck

but requires constant eﬀort and entails unavoidable ﬁnancial risks

as well.

Since the 19

century, intellectual circles have labored to

stigmatize the rugged vision

and have spread this perception

to the public. Many quirks of what passes for the “mainstream”

supply chain theory nowadays—such as dodging the obvious impact

of price on demand—owe much to this stigmatization.

Yet truth is not decided by popularity; the rugged vision is

the superior alternative to the teleological one, both conceptually

and empirically. Conceptually, the rugged vision embraces the

proﬁt-seeking perspective and its attendant traits. For example,

adversarial behaviors have no place in the teleological view yet ﬁt

naturally in the rugged view. Empirically, the rugged vision proves

more resilient to ambient chaos because it is far less dependent

on plan correctness, yielding superior risk-adjusted decisions. For

example, the very notion of “opportunities” is self-evident in the

rugged vision yet missing from the teleological one.

7.3.1 Irreducible uncertainty

The rugged vision assumes that the future is resolutely uncertain,

without implying that it is entirely unknowable. The rugged view

neither posits that possible futures are unthinkable nor that all

are equally likely. On the contrary, this view holds that some

futures are much more likely than others, that such futures can

be identiﬁed, and thus that resources can be allocated to support

those probable futures. In turn, customers can be served more

diligently and more proﬁtably.

See The Anti-Capitalistic Mentality (1956) by Ludwig von Mises for an

extensive discussion on why intellectual elites, and academia in particular, are

almost invariably ﬁerce opponents of entrepreneurial behaviors, which includes

the rugged vision presented in this chapter.

7.3. THE RUGGED VISION 271

The rugged vision holds that part of tomorrow can be antic-

ipated, often only in coarse, structural terms. Much of what we

know is comparative—“a gram of gold will almost surely remain

pricier than a gram of silver”—or relational—“SKU A will outsell

SKU B at equal price”. Such knowledge is operationally useful

yet awkward for equispaced point-time series, which oﬀer only one

number per date. Time-series slots cannot express inequalities,

orderings, or conditional statements; they ﬂatten relative facts into

absolute guesses.

Even when the knowledge is not comparative, its structure

resists the time-series mold. Consider aviation: global passenger

counts rise in most years, punctuated by rare, severe collapses. A

point series forces us to pick one number per future date; it cannot

encode “almost always up, except for catastrophic down years with

small probability”. Capturing asymmetry and tail risk requires

probability distributions or regime variables, not a single baseline

path. In short, the time-series formalism is too narrow for the

kinds of claims that give ﬁrms a real edge.

Seen from this angle, the rugged view is no less “predictive” than

the teleological one; it simply uses a diﬀerent grammar. Rather

than ﬁlling time buckets with a single baseline path, it assigns

probabilities to families of outcomes and records comparative

facts—e.g. that SKU A will likely outsell SKU B at equal prices,

that a heat wave lifts water sales, or that lead times carry a fat right

tail. What matters is not a solitary number per date but the priced

set of alternatives these statements unlock. A forecast is deemed

“good” insofar as it creates an economic edge—enabling the ﬁrm

to commit, defer, hedge, or reprice better than rivals—rather than

because it minimizes an academic error score.

Unlike the teleological view, which ﬂattens tomorrow into a

single quantity-over-time, the rugged view treats the future as a

portfolio of uncertainties spread across the whole ﬂow. Quantity

demanded is only one variable. Prices and commercial terms

can shift; a rival may extend the warranty from three to ﬁve

years, forcing a response; product mix and substitution evolve with

merchandising. Lead times wobble—often with fat-tailed delays;

272 CHAPTER 7. THE FUTURE

purchased and operating costs drift; suppliers and production lines

sometimes fail. Each of these deserves its own probability law

because each reshapes opportunity costs as much as demand does.

The practical task is to price these uncertainties jointly rather than

pretend one scalar forecast can stand in for them all.

In the rugged view, there is not one future but a boundless

multitude. Reducing that multitude to a handful of scenarios—let

alone a single forecast—is wishful thinking. Because the future is

only partly knowable and brimming with uncertainties, listing every

plausible outcome is impossible. Any eﬀort to craft a sweeping

“grand-master plan” therefore borders on self-deception.

Instead, the rugged view treats planning as an attempt to

reconcile diverse and contradictory insights about the future in

order to allocate resources most proﬁtably. Going big on one

unlikely outcome may be acceptable if the returns are high and

oﬀset the risks. Conversely, a very likely outcome may merit no

allocation if no proﬁt can be had, however certain.

In summary, the rugged vision treats tomorrow as a portfolio

of heterogeneous clues. It refuses to compress them into a single

time-series baseline and keeps each in its native form—probabilities,

regimes, inequalities, and comparative statements. The practical

problem is synthesis: turning disparate clues into consistent choices.

That task belongs to the tooling, not the vision. The remainder of

this chapter introduces the theory and instruments that perform

this synthesis.

Before the machinery, one principle: in the rugged view, the

decision—the allocation itself—comes ﬁrst.

7.3.2 Opportunistic allocations

The rugged vision treats the future not as a ﬁxed “baseline” of

units to purvey, but as a rolling stream of opportunities—some

dismally unproﬁtable, others very lucrative. Every decision is,

above all, an economic valuation of the opportunity to be seized.

In the rugged vision, the decision sits atop—a deliberate allo-

cation of scarce resources to an opportunity. Forecasts are inputs

7.3. THE RUGGED VISION 273

to that choice; in the teleological vision, by contrast, the forecast

wears the crown. This sharp, distinctive focus complicates any

attempt at dialogue between proponents of the two visions. To

the rugged exponent, forecasts are sophisticated but ultimately

secondary, wholly trumped by the need to act decisively. To the

teleological exponent, the forecasts and their associated plan are in-

separable from a rational course of action. Decisions not grounded

in a plan are seen at best as shortsighted and at worst as whimsical.

Yet while the rugged vision is fundamentally opportunistic, it

is just as forward-looking as the teleological vision. Indeed, every

allocation of resources comes with an unavoidable opportunity cost.

As a result, a seemingly proﬁtable opportunity might be rejected

because it could open the door to a better one or, conversely, spawn

problems that erode the expected gains.

For example, let’s consider an airline leasing an expensive,

repairable aircraft part to a peer so that one of its jets is not

grounded for lack of that component

. As the part is urgently

needed, the airline can command a very high price for the lease

from its peers. Yet the airline will frequently decline to lease the

part, even if, by basic accounting rules, the operation appears

excessively proﬁtable on paper. Indeed, the airline must consider

its own ﬂeet. When leasing the part, the airline increases the risk

of grounding one of its own aircraft precisely because this very

part ends up missing. The cost of this risk can entirely oﬀset the

proﬁt from the lease, despite a price point that, on paper, appears

highly proﬁtable.

Let’s note that the teleological vision is entirely sidestepping

the matter of opportunity costs. Indeed, from this perspective,

either the plan is deemed feasible—in which case no customer will

be left unserved—or it is deemed infeasible, in which case demand

forecasts are revised and lowered until the plan becomes feasible

In the aviation industry, an “AOG” (aircraft on ground) refers to an incident

where an aircraft is grounded due to a missing part or any other technical issue

that must be addressed prior to letting the aircraft ﬂy again. Such incidents

are costly: a single delay can cascade into missed connections and, if extended,

force the carrier to provide hotel rooms for stranded passengers.

274 CHAPTER 7. THE FUTURE

again. In the teleological vision, the very notion of opportunity is

buried—along with nearly every other supply-chain concern—inside

the time-series projection of future demand.

Exact calculation of these opportunity costs is diﬃcult in prac-

tice and acknowledged as such in the rugged vision. Thus, as an

alternative to explicit mental arithmetic on opportunity costs, the

rugged vision favors comparative assessments. For example, should

the company allocate its last unit to a new but unknown customer,

or keep it implicitly reserved for a loyal customer the company

isn’t willing to disappoint? While comparative assessments can be

made through sophisticated methods, the rugged vision emphasizes

a general preference for looking at every allocation through the

lens of its alternatives.

Through such comparative assessments, one can also decide

whether to allocate at all: if a preferable deferred opportunity exists

for the same allocation, the current one should be disregarded. For

example, the company can stop buying additional units, reserving

cash for later, once current stock is further depleted and needs are

clearer.

As a result, the rugged vision doesn’t need a “plan” in the sense

of the teleological vision. Resources will be allocated incrementally

until opportunity costs are deemed too high to continue. The

existence of preferable deferred opportunities is all the company

needs to avoid exhausting its resources through premature allo-

cations. Thus, despite its opportunistic stance, the rugged vision

is no more short-sighted than the teleological one. Both rely on

estimates of the future and are therefore forward-looking, even if

their predictions diﬀer in form.

Once allocations are comparable, they can be prioritized. A

single, continuously resorted priority list can steer the entire ﬂow as

new information revises each valuation. At enterprise scale, this list

easily runs to millions of entries—each admissible allocation—well

beyond human curation. The rugged vision says what to rank and

why. How to maintain and act on that ranking is an engineering

matter we will address when introducing the instruments that

mechanize the vision at scale. First, we need one more ingredient:

7.3. THE RUGGED VISION 275

the recognition that the future is not a mirror of the past but the

consequence of decisions yet to be made—the asymmetry of time.

7.3.3 Asymmetry of time

Unlike the perspective common in the natural sciences—where

many laws are eﬀectively time-reversal symmetric and tomorrow is

treated as another stretch of the same process—the rugged vision

treats the future as shaped by choices not yet taken. In business,

tomorrow is not a mirror of yesterday but the consequence of

present commitments. Forecasting while deferring the decision

reverses causality: decisions cause outcomes, not the other way

around.

We have already discussed the case of a sportswear company

selling backpacks. Future sales cannot be thought of as a mere

projection of past sales, because the company can shape demand

along many dimensions: lowering the price, introducing a larger

backpack model, or oﬀering it in a new color, paying for advertising,

featuring the item prominently in stores, etc. Thus, from the

rugged vision, it does not make sense to treat the coming year as

an extension of the previous year. The coming year is categorically

diﬀerent from the previous year.

Even more unsettling to teleological planners, the rugged vision

treats dependence on decisions not yet taken as perfectly normal.

Present commitments may be made expressly contingent on later

choices: we secure and price options today precisely so that we can

decide with fresher information tomorrow, when opportunity costs

are clearer. To the rugged practitioner, this is not circularity but

prudence—acting now to enlarge the future choice set, while defer-

ring selection among those choices until the market has revealed

more.

For example, a fashion brand might negotiate and pay for a

large upfront capacity commitment from a trusted supplier. This

commitment puts the supplier in a position to aggressively source

the raw materials, such as textiles and leather. This mitigates the

risk of the supplier running into delays caused by later diﬃculties

276 CHAPTER 7. THE FUTURE

sourcing the raw materials, while also paving the way for lower

acquisition costs. However, when the commitment is struck, the

brand has not yet decided what it will order. Under the rugged vi-

sion, those details can wait. Provided the capacity is broadly right,

postponing the purchase order is preferable because it preserves

the option to align later orders with the market’s latest desires.

The value of preserving options is another cornerstone of the

rugged vision. Indeed, whenever the company commits to a course

of action, it loses some of its agency. For example, once a man-

ufacturer triggers a production batch, the raw materials are con-

sumed and can no longer be used for an alternative production

run. Similarly, if the same manufacturer commits to triggering

the production batch next month, the matching raw materials are

eﬀectively reserved for that upcoming production.

Beyond direct commitments, options also erode through prece-

dent—the ratchet eﬀect. Once a behavior is observed, counterpar-

ties update their expectations and make reversal costly. A fashion

brand that ends a season with deep markdowns trains customers

to delay purchases and expect the same discounts next season; a

“temporary” service upgrade becomes the new baseline; a one-oﬀ

MOQ waiver becomes the new rule. Like a mechanical ratchet,

motion in the tightening direction is easy, while release is resisted.

In the rugged view, every action is priced not only for its immediate

margin but also for how it expands or shrinks tomorrow’s option

set; moves that lock future choices must clear a higher bar than

those that preserve ﬂexibility.

More generally, whenever part of the future is locked in, it

becomes functionally identical to the past, since it can no longer

be acted upon, even if events have not technically unfolded yet.

Not only does the rugged vision treat the future as radically dis-

tinct from the past, but it also cherishes preserving what makes

the future distinct—namely, the company’s agency. To rugged

practitioners, committing oneself to a ﬁnal course of action is a last

resort, undertaken only when the beneﬁts of commitment outweigh

the opportunity costs.

For those who hold the teleological vision, this stance is of-

7.3. THE RUGGED VISION 277

ten perceived as ﬁckle or mercurial. There is no commitment to

one’s former self, only to formal agreements involving third par-

ties. For example, when probed about a future course of action,

an exponent of the rugged view will simply oﬀer his best guess,

without any particular attachment to the statement. It’s just one

option—tentatively the most likely—but there is no certainty. This

option coexists with a vast number of alternatives that, in aggre-

gate, are even more likely. Later—as events unfold—the course

will be revised in light of new information.

From the teleological viewpoint, the existence of an underly-

ing plan is taken for granted: it is the central characterization of

the future. The possibility that there is no plan at all is nearly

unthinkable and is treated with contempt as amateurism when en-

countered. Thus, changing the course of action implies changing the

plan. Such an outcome is seen as a reﬂection of an underlying prob-

lem. This outcome reﬂects either inadequate execution—failure to

stay compliant with the plan—or an inadequate plan in the ﬁrst

place

Quantifying a future deemed dependent on decisions yet to be

made yields numerous technical diﬃculties. In particular, time-

series forecasts and most planning instruments of the teleological

vision end up being inadequate. As the supply-chain literature

oﬀers next to nothing to support the rugged vision, most of its

exponents rely on back-of-the-envelope calculations. Those calcu-

lations are deemed crude by teleological planners. Yet, as will be

shown in what follows, instruments supporting the rugged vision

can match—indeed, often exceed—the sophistication of classic

planning methods.

Large bureaucracies tend to evolve mechanisms that diﬀuse accountabil-

ity—layers of committees, ambiguous mandates, and gameable metrics. A

textbook case is the Space Shuttle Challenger accident (1986): the Rogers

Commission concluded that the launch decision was structurally ﬂawed, with

responsibility fragmented across NASA and contractor management and dissent

ﬁltered out by process rather than owned by any single decision-maker. For the

broader pattern, compare Parkinson’s Law (1955) and Pournelle’s “Iron Law of

Bureaucracy” (2010).

278 CHAPTER 7. THE FUTURE

7.3.4 Superiority of the rugged vision

While the rugged vision enjoys virtually no support from intellec-

tual circles

, it is vastly superior to the teleological vision. Em-

pirically, when successful entrepreneurs disclose their vision, they

invariably lean toward the rugged vision. Ingvar Kamprad (Ikea),

Jeﬀ Bezos (Amazon), and Warren Buﬀett (Berkshire Hathaway)

are a few who took such public stances. Successful entrepreneurs

devote nearly all their eﬀort to running their businesses and thus

do not produce as many papers or books as intellectuals who spe-

cialize in such work. Hence, writings favoring the rugged vision

are rare.

Furthermore, there is ample empirical evidence of the dismal

results of the teleological vision. For seven decades the Soviet

Union’s Gosplan imposed severe economic ineﬃciencies on more

than 100 million people. More broadly, in both private and pub-

lic sectors, large multi-year plans frequently derailed, generating

immense costs. Conversely, grand-planning successes that made

a large company’s fortune are unheard of. At best, plans execute

with limited overhead—in cost and delay—and that is the full

measure of “success” here.

Those observations—both successes under the rugged vision

and failures in its absence—stem largely from a single factor: how

uncertainty is managed. The teleological vision tries to eliminate

uncertainty; the rugged vision embraces it. Thus teleological

methods are fragile to shocks, whereas rugged-vision methods can

mitigate—or even proﬁt from—the same shocks.

In a world with little or no uncertainty, the teleological vision

might perform admirably. We do not live in such a world. Free

markets alone suﬃce to create substantial uncertainty for all partic-

ipants. Indeed, free markets enormously magnify minor variations.

For example, Walmart drove competitors into bankruptcy—Kmart

in 2002 and Sears in 2018. Walmart took Kmart’s revenue from

These intellectual circles also include management consultants who, like

academics, depend on a steady stream of publications to gain the notoriety their

careers demand.

7.4. PREDICTABILITY OF HUMAN AFFAIRS 279

over $36 billion in 1994 to zero despite being only 2.2% cheaper

In a free market, being 1% worse than a competitor typically yields

a 100% loss of market share within a decade or two. Thus, even if

non-market sources of uncertainty—wars, hailstorms, earthquakes,

epidemics—were vanquished, irreducible uncertainty would still

deﬁne free markets.

Supply-chain instruments—whether algorithms or methodolo-

gies—that support the rugged vision are scarce—an unsurprising

consequence of its lack of supporters among intellectual circles.

Most practitioners rely on experience and the intuition it yields.

Yet nothing prevents us from introducing such tools—an endeavor

these chapters undertake step by step.

7.4 Predictability of human aﬀairs

A supply chain’s performance hinges on the capacity of the com-

pany, both its managers and its software systems, to adequately

anticipate future events. These events include, above all, human de-

cisions. Perfectly predicting natural events is conceivable—though

rarely attainable beyond simple cycles like tides—but perfect fore-

sight in human aﬀairs is impossible. All it takes is a contrarian

customer

changing his mind to undermine a supposedly “perfect”

forecast. Yet habit makes actions somewhat predictable in the

short run.

For instance, when a supermarket manager places a pack of

yogurt on the shelf with a price tag, he expects a customer to

buy it. Moreover, he expects the sale to occur well before the

yogurt reaches its expiration date. Accurate anticipation keeps

See Wal-Mart Stores, Inc. (1994), European Case Clearing House, by

Stephen P. Bradley, Pankaj Ghemawat, and Sharon Foley.

When visiting a local grocery store, all it takes to undermine the forecast

of the retailer consists of emptying the shelf of a given product, immediately

causing a stockout. While a few products are stored in large quantities making

this exercise impractical, such as bottled water, many more products are stored

in very limited quantities, low single digit quantities, and thus, making it quite

aﬀordable to adopt such an adversarial behavior.

280 CHAPTER 7. THE FUTURE

write-oﬀs and stockouts in check—a discipline most supermarkets

master. Proﬁtable supermarkets prove that such anticipations can

be produced at scale as routine business.

In general, supply chains remain proﬁtable because managers

anticipate future events well. For the most part, this means antici-

pating decisions to be made by others. Until the late 19

century,

the process relied on intuition and experience. In the early 20

cen-

tury, formal analytical methods appeared, and model accuracy

began to be measured rigorously. These methods ﬂourished after

1950, buoyed by steadily increasing computing power. This ush-

ered in the time-series paradigm, which, as discussed previously,

became the dominant predictive approach in supply chain.

Yet the time-series lens is brittle in business settings. It ab-

stracts away the very levers that make tomorrow unlike yesterday

and compresses sparse, discrete events into fractional averages.

Where it helps, it does so for two reasons only: short-run momen-

tum and calendar cyclicities. We examine those narrow strengths,

then replace the lens with instruments that ﬁt the economics of the

ﬂow: (i) probabilistic forecasts that attach distributions to demand,

lead times, prices, and returns; (ii) high-dimensional forecasts

that capture baskets, substitution, and cross-eﬀects across items,

channels, and locations; and (iii) functional (policy-conditioned)

forecasts that speak in the native objects of the ﬂow—orders, al-

locations, and prices—rather than in evenly spaced time buckets.

These tools do not guess “the” future; they price alternatives and

make opportunity costs explicit.

7.4.1 Momentum and cyclicities

Time-series forecasting treats human aﬀairs as periodic measure-

ments projected forward. Once past numbers are recorded, the

task is to propose sensible values for future dates—and to do it as

faithfully as our tools allow.

With millions of papers

on time-series forecasting, it is diﬃcult

On Google Scholar (November 2024), the query “time series forecasting”

7.4. PREDICTABILITY OF HUMAN AFFAIRS 281

to overstate the perceived importance of this paradigm in academia.

Yet, for a corpus of this magnitude, it has surprisingly little to oﬀer.

It rests on two principles that give time-series models predictive

power: momentum and cyclicities. They matter, but their limits

are obvious.

Momentum

, in supply chain settings, is the short-run reluc-

tance of people to change their habits. This reluctance is not mere

stubbornness; change often carries sizable costs. Assessing whether

change improves on the status quo may require considerable eﬀort,

and the rollout may undermine the value of existing productive

assets.

For example, a manufacturer may decide to source its machine

tools from a lower-priced supplier. As machine tools are not simple

commodities, assessing suitability is time-consuming. Teams must

be retrained and processes adjusted to make the most of the new

machines. Finally, existing spare parts may lose most of their value

if they are incompatible with the new supplier’s machines.

Thus momentum explains why the moving-average model fam-

ily—including exponential smoothing—often provides a decent

short-term baseline: the near future resembles the recent past.

Moreover, momentum governs more than demand: lead times,

return rates, defect rates, purchase and selling prices, etc.

As habits shift, momentum’s predictive power wanes; the Lindy

eﬀect tells us how fast.

If a book has been in print for forty years, I can expect it

to be in print for another forty years. But, and that is the

main diﬀerence, if it survives another decade, then it will

be expected to be in print another ﬁfty years. This, simply,

as a rule, tells you why things that have been around for

a long time are not "aging" like persons, but "aging" in

reverse. Every year that passes without extinction doubles

returns roughly 4,230,000 results. The ﬁgure is indicative only: duplicates and

near-duplicates inﬂate it, while missed items deﬂate it. Either way, a quick

audit conﬁrms that the correct order of magnitude is indeed “millions”.

In classical mechanics, momentum equals mass times velocity. It is a vector

quantity.

282 CHAPTER 7. THE FUTURE

the additional life expectancy. This is an indicator of some

robustness. The robustness of an item is proportional to its

life! Antifragile: Things That Gain from Disorder (2012),

Nassim Nicholas Taleb.

The Lindy eﬀect governs habits as surely as objects, and in a

shifting world it is critical in managing allocation risk. Substantial

investments demand momentum that is both strong and durable.

Practitioners use the Lindy eﬀect to assess the lifespan of a given

momentum.

For example, a two-week surge in breakfast-cereal sales might

suﬃce for a supermarket to expand its inventory. However, the

brand observing the same surge might delay capacity expansion

by several months. The supermarket’s bet is modest; if the surge

is transient, the overstock will work oﬀ over time. By contrast, the

brand’s capacity investment is far larger and, if the surge fades,

must be written oﬀ.

Cyclicities capture the human tendency to anchor decisions

to the calendar. In practice, the usable calendar regularities are

few and stable: hour of day, day of week, day of month, and day of

year. Everything else of lasting value is a variant or composition

of these four, plus a handful of quasi-cyclical events that do not

track the Gregorian calendar exactly.

Hour of day. Stores have opening hours; e

commerce has order

cut

oﬀs and late

evening spikes. Sub

daily ledgers reveal sharp

intraday patterns (e.g., lunch

break lulls, end

day surges). With

daily data, intraday cycles mostly vanish from view except through

induced day-of-week eﬀects—for example, Sunday closures zero

out retail demand.

Day of week. Households shop on weekends; B2B demand

concentrates Monday through Friday; returns cluster on Mondays.

For many assortments, the weekly proﬁle explains more variance

than any other single factor.

Day of month. Payroll, beneﬁts, bill runs, and end

period

targets create month

end “hockey sticks”—especially in shipments.

Note the distinction: a month-end shipping spike is usually a

7.4. PREDICTABILITY OF HUMAN AFFAIRS 283

bureaucratic gating artifact, not genuine consumption. Treat it as

a calendar eﬀect on observed ﬂows rather than a persistent lift in

underlying demand.

Day of year. Seasons, vacations, and civic holidays dominate

the annual proﬁle. “Seasonality” here is no mystery; it is the

calendar’s imprint on habits (heaters in winter, sandals in summer).

Retail “events” are likewise calendar-bound; Black Friday (the day

after the fourth Thursday of November in the USA)

reliably

realigns baskets every year.

Beyond the four base frequencies sit a few quasi

cycles: reli-

gious or lunar events (Ramadan, Chinese New Year, Easter) that

drift relative to the Gregorian year and must be handled with ex-

plicit, locale-speciﬁc calendars rather than naive monthly indices.

School calendars and local festivals are of the same kind: discrete

calendars with predictable date lists, not free-ﬂoating “patterns”

to be discovered.

From a modeling standpoint, the most economical treatment

is a small set of multiplicative indices—one per relevant calendar

factor—regularized toward 1 under weak evidence. Geography

and channel merit separate indices (what moves on Saturday in

retail often does not in B2B); closures imply structural zeros rather

than imputation; sparse SKUs borrow strength from their peers

through hierarchical pooling. This keeps the calendar explicit—and

portable—across assortments.

Put plainly, once momentum (the short

run persistence of

habits) and a compact calendar (the four base cyclicities plus a

few explicit quasi

cycles) are in place, almost all of the practical

signal that time

series models can harvest has been harvested.

Elaborate decompositions—Fourier ﬁreworks, exotic lags, baroque

state machines—rarely survive out

sample in business settings.

Everything beyond momentum and calendar belongs to other

Black Friday and its derivatives (e.g., Cyber Monday) are deﬁned relative

to civic holidays, not by astronomical cycles.

Ramadan migrates roughly 10–11 days earlier each Gregorian year; Chinese

New Year falls between late January and mid-February. Use dedicated binary

calendars for such events rather than smearing their eﬀects across months.

284 CHAPTER 7. THE FUTURE

instruments (pricing, promotion, substitution, lead-time modeling),

not to the time-series lens itself.

The next section shows that, after momentum and calendar

are accounted for, the remaining tail-chasing within the time-series

paradigm yields vanishing returns for supply chains.

7.4.2 The dead end

Despite tens of thousands of new papers each year, time-series

forecasting has proved a technological dead end for supply chains

for years. The clearest demonstration of this claim appears in the

M5 results, a large public forecasting competition held in 2020—the

largest to date—using a store-level sales dataset from Walmart.

In this competition, a team from Lokad—a supply chain soft-

ware specialist—placed ﬁrst at the SKU level

—the most disag-

gregated and, from a supply chain perspective, the only level that

ultimately matters for ﬂow decisions. Their winning forecasting

model

was a simple multiplicative parametric model, using only

momentum and cyclicities as introduced in the previous section.

Furthermore, the vast majority of those parameters were basic

cyclicity indices, i.e., multiplicative coeﬃcients reﬂecting the “day

Our argument only applies to supply chain settings. Natural sciences oﬀer

all sort of periodic measurements which can also be approached through the time-

series paradigm. Those time series may exhibit all sort of sophisticated patterns

that can be used to anticipate future events with great accuracy. Conversely, in

pure ﬁnance, when no physical goods are involved, even a minuscule pattern

can be relentlessly exploited by an actor to arbitrage the market and generate

proﬁts in the process. In contrast, in supply chain, statistical patterns need to

be nontrivial to be harnessed.

See The M5 Uncertainty competition: Results, ﬁndings and conclusions

(2020), by Spyros Makridakis, Evangelos Spiliotis, Vassilis Assimakopoulos, Zhi

Chen. In the overall score, which involved forecasts performed over a dozen of

aggregation levels, the Lokad team was initially ranked No. 6, but this ranking

was later revised to No. 5, as one of the better-ranked teams was found guilty

of cheating the competition.

See A white-boxed ISSM approach to estimate uncertainty distributions of

Walmart sales (2021) by Rafael de Rezende, Katharina Egert, Ignacio Marin,

Guilherme Thompson, December 2021

7.4. PREDICTABILITY OF HUMAN AFFAIRS 285

of the week”, “day of the month”, and the “month of the year”

factors.

At the SKU level, this model outperformed more sophisticated

approaches, including deep learning models (with millions of pa-

rameters) and gradient-boosted machines (with thousands of trees).

At other aggregation levels, this simple model remained within a

narrow margin of the winners’ accuracy.

With 909 teams worldwide in the M5, every notable time-series

method was surely tried. Yet, in the end, a simple model achieved

comparable—if not better—accuracy than its more sophisticated

counterparts. The reason is simple: no model managed to capture

valuable information from the historical time series beyond mo-

mentum and cyclicities. No model managed this feat because such

information is not present in the M5 time series.

For the Lokad team, far from being a surprise, this outcome

conﬁrmed a realization formed about a decade earlier, after running

numerous time-series forecasting benchmarks on datasets from

dozens of corporate clients. These datasets hide no undiscovered

cyclicities, no latent lags where one series predicts another, and no

secret trends waiting to be found

. More generally, within business

records, there are no alternative patterns to exploit through more

sophisticated time-series forecasting models.

The absence of evidence isn’t evidence of absence. While al-

ternative time-series patterns—i.e., neither momentum nor cyclici-

ties—remain elusive, such patterns may yet be discovered. Yet the

odds of a breakthrough after such a staggering volume of research

are low. By the end of WWII, the predictive power of momentum

and cyclicities had already been identiﬁed. The M5 competition

then illustrated that, 70 years and millions of papers later, no

further principles have been uncovered. Most of what has passed

for “progress” since then has merely reﬁned the parameterization

of those two eﬀects—smoother trend terms for momentum and

A steady trend over multiple years can be seen as a particular case of

momentum, where the business is expected to keep growing as it did in the

recent past. Introducing a trend factor in a time-series model is straightforward,

and numerous techniques exist to do so.

286 CHAPTER 7. THE FUTURE

ever-ﬁner calendar indices for cyclicities—yielding marginal gains

with steep diminishing returns once a clean multiplicative baseline

is in place.

There are numerous patterns to exploit beyond momentum

and cyclicities, but they lie outside the narrow conﬁnes of the

time-series paradigm. Thus, to take advantage of those patterns,

the time-series paradigm must be abandoned.

The persistence of ever-new time-series “advances” owes less to

misunderstanding than to incentives: novelty markets better than

admitting that, beyond momentum and cyclicities, little signal

remains to harvest. Hence the parade of variations decade after

decade, each adopting whatever technique happens to be fashion-

able in computer science

. For supply-chain forecasting, these

waves add little of substance, as the M5 results above illustrate.

7.4.3 The law of small numbers

Momentum and cyclicities, as predictive principles, are undeniably

powerful. When large numbers are involved, human aﬀairs become

quite predictable using nothing but those two principles. For

example, traﬃc-jam dates in France can be predicted years ahead

with near-certainty. Those traﬃc jams reﬂect tens of millions

of individual decisions entrenched in lifetime habits. Transport

infrastructure is still evolving, but the progress is slow enough not

to interfere much with such predictions a few years out.

Unfortunately, supply chains are fundamentally about small

numbers. Whenever a number characterizing the ﬂow gets large,

say exceeds 20, batching occurs and the number is lowered, owing

to economies of scale along the way.

Let us illustrate this principle with a local shipment of small

mechanical parts. A single urgent part goes by scooter. As quan-

New time-series forecasting models have been introduced steadily for seven

decades. Dominant trends include parametric models (1950–1980), hidden

Markov models (1990s), support vector machines (2000s), gradient-boosted

machines (2000s), deep learning (2010s), and generative pretrained large models

(2020s).

7.4. PREDICTABILITY OF HUMAN AFFAIRS 287

tities rise, the mode changes in discrete steps: a few dozen parts

are shrink-wrapped into one bundle and ride on the next courier

run; several bundles are packed into a carton and sent by delivery

van; a stack of cartons becomes a pallet moved by a medium-duty

truck; a handful of pallets justify scheduling a full-truckload. In

practice, the ﬂow advances in quanta—unit → bundle → carton

→ pallet → truckload—rather than one unit at a time.

As a result, supply-chain time series are almost invariably sparse

(observations are mostly zeros) and erratic (non-zero observations

vary signiﬁcantly).

Day

Qty

0 2 4 6 8 10 12 14

Figure 7.2: A smooth, high-volume series with a weekly cycle (solid line)

contrasted with a sparse, erratic series (gray bars).

Time-series methods perform poorly on sparse series: they yield

fractional daily volumes (e.g., 0.3 units/day) that lack operational

meaning at the point of use and, unsurprisingly, prove inaccurate

against real decisions. A common palliative is to “hide” sparsity

through aggregation—lengthen the time bucket (days

→

months)

or broaden the scope (SKU → category).

Yet even if aggregation technically yields more accurate time-

series forecasts, it is little more than an optical illusion. The

decision’s structure—resource allocation—dictates the prediction’s

granularity, not the other way around.

For example, consider a store manager selling a few jars of a

given mustard brand each month. In his case, the replenishment

decision has two ﬁelds: a date (when to reorder) and a quantity

288 CHAPTER 7. THE FUTURE

(how many units). The manager needs to know if today is a good

day to place the reorder, not whether this month is the right

month to reorder. Similarly, he needs to know how many jars

(same brand, same size) to reorder. An assessment in condiment

units—the product category—is useless to the manager.

The law of small numbers explains why the hope of engineering

precise time-series forecasts is greatly misplaced—even considering

situations that aren’t entangled in decisions that have yet to be

made. Small numbers render the data irreducibly erratic. This is

not a transient defect of present-day statistical instruments, soon

to be ﬁxed by superior ones. On the contrary, whatever numerical

instruments we bring to supply chain must be tailored to small

numbers and otherwise limited datasets.

These constraints do not call for a ﬁner time grid or a more

elaborate smoother; they require a diﬀerent grammar. When

counts are small and actions are discrete, the meaningful unit of

prediction is not “one number for next Tuesday” but a probability

law over admissible outcomes—how many, if any, and how late.

The ﬁrst instrument in that grammar is the probabilistic forecast:

a distribution that accommodates sparsity, integer quantities, and

fat-tailed surprises, and applies equally to demand, lead times,

returns, and prices. We start with lead times and demand to ﬁx

ideas.

7.4.4 Probabilistic forecasts

Probabilistic forecasting considers all possible future outcomes for

an event and assigns probabilities to each. A probabilistic forecast

is the complete set of those probabilities. A non-probabilistic

(classic) forecast that pinpoints one outcome is a point forecast;

the term is rarely used, since “forecast” usually implies “point

forecast”. Probabilistic forecasting is fundamental for supply chain

because it embraces the law of small numbers, both theoretically

and empirically. It marks our ﬁrst major departure from the

equispaced point time-series paradigm.

Probabilistic forecasts quantify uncertainty across the entire

7.4. PREDICTABILITY OF HUMAN AFFAIRS 289

ﬂow, not just demand. This is our second break with mainstream

practice: we attach distributions to every uncertain driver that

moves cash and atoms—lead times, inbound ﬁll rates, supplier

reliability, purchase and selling prices, returns, scrap, and available

capacities. Throughout this book, “probabilistic” is used in this

broad sense. To ﬁx ideas, consider varying lead times. Neither

supplier nor carrier is perfectly reliable; unscheduled delays happen.

What we need is the distribution of the time-to-receive for a just-

placed order. A probabilistic lead-time forecast provides exactly

that distribution. The histogram below summarizes the day-by-day

probabilities.

Lead time (days)

Probability

0 1 2 3 4 5 6 7 8 9 10 11 12

Figure 7.3: Illustrative lead-time distribution: impossible at

≤

2 days

on the left; peak at the nominal 3-day delivery; fat-tailed risk of longer

delays.

The histogram shows that delivery in under three days has zero

probability. This reﬂects the selected shipping method’s minimum

transit time. The distribution’s mode occurs on the third day,

where the probability is highest. This is the nominal lead time. It

is the duration expected when both supplier and carrier perform as

planned. Small but non-zero probabilities stretch far to the right;

they represent disruptions that inﬂate lead times well beyond the

nominal. In the worst case, the delivery never occurs.

Like point forecasts, probabilistic forecasts can be computed

from historical data. Setting modeling technicalities aside, let us

Strictly speaking, if there is a nonzero probability that delivery never occurs

(e.g., loss, destruction, seizure), the mean lead time is inﬁnite: some probability

mass sits at “never”, so the expected lead time diverges. In practice, medians

and quantiles are more meaningful than means for such distributions.

290 CHAPTER 7. THE FUTURE

clarify what probabilistic forecasts bring to supply-chain decision-

making.

Probabilistic forecasts convey richer information than point

forecasts. However, they are not intrinsically more accurate. Prob-

abilities add an information dimension absent from point forecasts.

In particular, one can collapse a probabilistic forecast into a point

forecast by taking the distribution’s mean or median. Thus, a

more accurate probabilistic forecast will mechanically yield a more

accurate point forecast. Given a probabilistic model and a point

model, empirical validation is required to determine which is more

accurate. As classes, “probabilistic” and “point” models confer no

systematic accuracy advantage. Nevertheless, the extra informa-

tion provided by probabilistic models is often critical for supply

chain decisions.

Revisiting the varying-lead-time example above, note that the

distribution’s ﬁne print is critical for managing late-delivery risk.

Assuming goods arrive at the median lead time would be a grave er-

ror: by deﬁnition, half the shipments would arrive late. Conversely,

basing operations on a far-right value of the distribution—a high

quantile—is equally unreasonable. Most goods would arrive much

sooner, creating problems such as excessive inventories.

Lead times are only one of many examples where variability

and uncertainty critically impact supply chain decisions.

Revisiting the earlier single-buyer variant from Limits of plan-

ning. Suppose a factory has shipped 1,000 units per day for years

to a single customer. In time-series terms, the point forecast is

a ﬂat line at 1,000. Now add information the time series ignores:

the account is long-lived but not immortal—the weekly churn

probability is about 0.1%. One year out (52 weeks), the expected

daily demand is (1

−

001)

1000

≈

934 units. Mathematically

correct, perhaps, but operationally nonsensical. A year from now,

daily demand will be either 1,000 or 0.

Any capacity plan based

1,000 units per day, or 0 units per day, or something else entirely. Indeed,

the customer can also simply change its ordering volume; we ignore that third

option here for clarity, though real settings would require considering it.

7.4. PREDICTABILITY OF HUMAN AFFAIRS 291

on “934 units per day” will misallocate resources.

A probabilistic forecast, by contrast, makes the collapse risk

explicit: most probability mass remains on “still 1,000”, with a

smaller mass on “drops to 0” over the horizon. That view drives

a two-pronged policy: secure current service while the account

persists, and prepare a contingency for its eventual loss. The point

forecast attempts to predict a single “true” future and averages

away risk; the probabilistic view prices alternatives and prevents

the planning failure the ﬂat line invites.

Thus, probabilistic forecasting reveals things that point lenses

cannot show. Attempting to capture a “one true future”, the point

forecast remains blind to otherwise obvious patterns. Moreover,

the probabilistic perspective typically permits leveraging additional

data beyond the narrow historical time series, or extracting more

signal from the same data. We illustrate this point with another

example.

Consider a DIY store with a product that sells on average 0.5

units per day. Although unrealistic, for clarity assume demand is

constant with no cyclicity. The store is replenished daily from a

warehouse that serves similar stores. The demand forecast is again

a ﬂat series of 0.5 units per day. Now consider the probabilistic

forecast for daily demand, illustrated below.

Count

Probability

0 1 2 3 4 5 6 7 8 9 10

Figure 7.4: Discrete demand histogram: overall decline with pronounced

peaks at 4 and 8 units.

The product is indeed a corner protector intended for child-

prooﬁng. Although protectors can be purchased individually, most

customers buy them in multiples of 4. The histogram above shows

292 CHAPTER 7. THE FUTURE

this: the probabilities of daily demand at 0, 4, 8, and 12 units

are much higher than for other counts. This probabilistic demand

forecast makes clear that the store should hold 8 units in stock

— perhaps 12 — to deliver high service quality. This stock level

could not have been anticipated by merely considering an average

demand of 0.5 units per day.

Probabilistic forecasts not only embrace the law of small num-

bers but also eliminate the need to handle nonsensical fractional

projections. While 0.5 units per day may serve as a convenient

abstraction to summarize demand for a packaged product, cus-

tomers cannot buy fractional units: only integer quantities are

admissible. Many authors and software solutions shrug oﬀ this

issue, but that attitude is misguided. Stock can only be replen-

ished in integer quantities. Accepting a fractional point forecast

introduces large approximations that can occasionally generate

substantial overheads for the company.

For example, if cycle demand is intermittent—say 0.1 unit

per period—there is a real risk it will be rounded to zero. When

this happens, no inventory is replenished; the observed demand

disappears and the ﬂow halts. Far from a theoretical edge case,

this situation—an inventory freeze—is a routine problem for inven-

tory policies governed by point forecasts rather than probabilistic

ones. More generally, probabilistic forecasts naturally match the

dominantly discrete nature of the ﬂow, a consequence of the stan-

dardization principle discussed earlier.

Although any distribution can be reduced to a single num-

ber—mean, median, or mode

—doing so discards the information

that gives the probabilistic view its value. The beneﬁt lies in the

entire shape—its asymmetries, tails, and multimodality—not in a

hand-picked “central” value. No single summary can preserve that

information. The next chapter shows how to make decisions that

exploit the full distribution rather than collapsing it back into a

single point.

The mode of a probabilistic distribution is the point where the probability

mass function attains its maximum. The mode need not be unique.

7.4. PREDICTABILITY OF HUMAN AFFAIRS 293

Generating probabilistic forecasts is arguably more technical

than producing point forecasts; however, in this age of powerful

computers the overhead is largely inconsequential, provided ap-

propriate methods are used. Because of this hurdle, one might

be tempted to do the reverse: ﬁrst compute a time-series point

forecast, then convert it into a probabilistic forecast. For exam-

ple, one may approximate a sum over a segment with a normal

distribution, as is sometimes done for safety stocks.

Unfortunately, this approach yields none of the intended bene-

ﬁts. Probabilistic forecasts are superior because they carry more

information, but that extra information does not appear out of

thin air. That extra information is extracted by processes speciﬁ-

cally designed to draw more information from historical data than

methods aimed at producing point forecasts.

A further subtlety: a probabilistic forecast is not vindicated

merely because the realized outcome had non-zero probability under

the distribution. What matters is the weight the model assigns

to the realized outcome. A good model concentrates probability

where reality tends to fall and withholds it elsewhere. A bad

model does the reverse—sprinkling tiny probabilities everywhere

or assigning 1% to events that occur weekly. Thus probabilistic

forecasts should be judged not by binary hits but by how much

probability they allocate to what actually occurs, while remaining

as concentrated as the evidence permits. This calls for proper

scoring rules that reward well-calibrated, sharp distributions and

penalize misplaced conﬁdence.

Accordingly, we assess probabilistic models using metrics tai-

lored to distributions. Although probabilistic metrics—e.g. log-

likelihood—are less familiar than point-forecast counterparts—e.g.

mean absolute error—they are not mysterious. The ﬁne print of

these metrics is technical and not critical to the present discus-

sion. For now, we can proceed knowing those metrics exist and

that, given two probabilistic models, they indicate which model is

deemed “best”.

294 CHAPTER 7. THE FUTURE

7.4.5 Fat tails

In the natural sciences, many phenomena follow normal (Gaussian)

distributions — a remarkable fact. For example, across the world’s

population, men’s heights are approximately normally distributed.

Similarly, men’s weights are approximately normally distributed.

Even if we ﬁlter the sample—for example to blue-eyed men or to

men living north of the 45th parallel—the distribution remains

approximately normal. We obtain similar normal distributions for

body temperature, bone density, or sleep-cycle durations.

Value

Density

µ − σ µ + σ

Figure 7.5: Thin-tailed normal distribution centered at 3 with standard

deviation 1. Compare with the lead-time histogram.

Normal distributions are ubiquitous across the natural world.

Air temperatures, rainfall, and wind speeds over weekly or longer

horizons follow normal distributions. They also appear in devia-

tions of apparent stellar brightness and in gene-expression levels

across human and nonhuman populations.

As mathematical tools, normal distributions are exceptionally

convenient. They beneﬁt from simple analytical expressions. They

enjoy simple closed forms, ubiquitous open-source libraries, and a

rich algebra: sums of normals remain normal, and appropriately

scaled sums of many random variables converge to a normal, etc.

Unsurprisingly, most supply-chain authors embrace normal dis-

tributions; yet the view is fundamentally wrong. Once human

decisions enter the picture, thin tails are the exception. Most

business-relevant variables are fat-tailed: extreme outcomes occur

far more often than the bell curve allows; distributions are typ-

ically skewed; tails dominate totals. Modeling them as normal

7.4. PREDICTABILITY OF HUMAN AFFAIRS 295

systematically understates risk and misprices opportunity.

Consider a stadium with 10,000 spectators. No single person

can account for even 0.1 % of the crowd’s total body mass; human

weight is thin-tailed as a matter of biology. Net worth is the

opposite: one attendee may plausibly own more than the wealth

of everyone else combined. Wealth is fat-tailed

More generally, anything akin to entrepreneurial success yields

fat-tailed outcomes. The top 10 smartphone brands sell more

units than all other brands combined. In any given year, the

top 100 musical artists worldwide sell more songs than all other

artists combined. In French presidential elections, the top three

candidates have always received more votes than the two dozen

candidates that trail them.

Supply-chain ﬂows are likewise prone to fat-tailed shocks: de-

mand can jump to implausible levels with little warning. In De-

cember 1973, a throwaway joke by late-night host Johnny Carson

on The Tonight Show about an impending toilet-paper shortage

triggered a nationwide run; shelves emptied not because supply

changed but because expectations did. Nearly ﬁve decades later,

the same SKU showed the same tail from a diﬀerent cause: in

March 2020, amid lockdown uncertainty, households stockpiled

staples and U.S. toilet-paper sales spiked roughly eightfold

—a

dramatic swing for an item that is usually ﬂat. Diﬀerent triggers—

rumor versus policy shock—produced the same operational reality:

a long, thin tail became a sudden, overwhelming wave.

Sales can also grow markedly as a result of deliberate marketing

campaigns. For example, with its “Just Do It” campaign and strong

product, Nike lifted its domestic sports-shoe share from 18% to

Wealth’s fat-tailed inequality is not peculiar to capitalism. Feudalism, social-

ism, and other authoritarian regimes yield even greater inequality than capital-

ism. Laws and customs do not change the nature of a phenomenon—speciﬁcally,

whether it is thin- or fat-tailed. Naturally, this has never stopped anyone from

promising the opposite to his fellow men.

On March 12, 2020, sales of toilet paper were up 845% week-over-week

versus the prior month, according to NCSolutions, which aggregates U.S. retail

purchase data.

296 CHAPTER 7. THE FUTURE

43% and grew worldwide sales from $877 million to $9.2 billion over

1988–1998. By 2021, the ﬁgure reached $44.5 billion—a roughly

ﬁftyfold increase over a few decades.

Engineering feats can likewise leave one company utterly domi-

nating its market. In 2024, SpaceX placed about 90% of the world’s

tonnage into orbit. Even more remarkable, it achieved this with

only a tiny fraction of the investment made by its competitors—

almost entirely government programs—to build orbital launch

capabilities.

Demand is not the only phenomenon subject to fat tails; most

ﬂow variables are as well. Prices can swing to astounding levels—

in both directions. For example, on April 20, 2020, the price of

West Texas Intermediate crude fell below zero for the ﬁrst time,

reaching $-37.63 per barrel. The negative price resulted from

expiring futures: holders were obligated to take physical delivery,

storage was full, and they dumped contracts at any price to avoid

logistical and ﬁnancial consequences.

Lead times also exhibit extreme behavior. As of 2023, Airbus

reported a backlog of over 7,000 aircraft—roughly 7–8 years of

output at current capacity. High demand for fuel-eﬃcient models

such as the A320neo family has signiﬁcantly contributed to this

backlog. The backlog also reﬂects the high unit capital cost and

lengthy production times. Moreover, other notable airframers were

suﬀering comparably large backlogs.

From a supply chain perspective, fat tails matter because

proﬁts and losses are dominated by extreme events. Far from being

unpredictable, ubiquitous fat-tailed distributions guarantee that

extreme events will routinely appear. Thus, probabilistic forecasts

are not suﬃcient; models must reﬂect the fat-tailed nature of

supply chain phenomena.

Conversely, any supply-chain model that adopts normal—or

otherwise thin-tailed—distributions is guaranteed to understate

risk and, consequently, to produce fragile decisions. Such fragility

puts the ﬁrm’s survival in jeopardy: markets—especially when

more prudent or opportunistic competitors exist—punish it when

extremes arrive.

7.4. PREDICTABILITY OF HUMAN AFFAIRS 297

The presence of normal distributions in supply chain mate-

rials—whether publications or software—should be treated as a

litmus test for incompetence. This includes all thin-tail methods,

such as safety stocks. Indeed, a casual look at empirical distribu-

tions from real-world supply chains shows the normal distribution

is nowhere close to a reasonable ﬁt

. Thus, by advocating such

methods, authors reveal they have not even bothered to look at

any real-world supply chain. A company embracing this nonsense,

in any guise, only does its competitors a favor.

7.4.6 The price of knowledge

Probabilistic forecasts braid together two distinct kinds of uncer-

tainty; separating them clariﬁes both modeling and action.

Deﬁnition (Aleatory uncertainty).

The inherent randomness or variability in the phenomenon

itself—i.e., even with perfect knowledge and inﬁnite data, the

outcome still exhibits randomness.

For overseas lead times, weather systems and port congestion

inject variability no party can eliminate; even with perfect infor-

mation and inﬁnite history, the day a container clears the terminal

still varies. That irreducible variability is aleatory.

Deﬁnition (Epistemic uncertainty).

The uncertainty arising from incomplete information or in-

suﬃcient data about the phenomenon—i.e., uncertainty about

the parameters or even the form of the probabilistic model.

By contrast, when the ﬁrm has placed only a handful of or-

A telltale misuse is modeling lead times as normal. Beyond thin tails, a

Gaussian assigns nonzero probability to negative delays—implying shipments

arrive before they are sent. Any model that lets time run backward is unﬁt for

operations.

298 CHAPTER 7. THE FUTURE

ders with a new supplier, the shape of the lead-time distribu-

tion is poorly known. Each additional order, clearer timestamps,

and tighter Incoterms shrink that ignorance. This is epistemic

uncertainty—uncertainty about our model of the phenomenon, not

the phenomenon itself.

he distinction matters because, in supply chain, past observa-

tions used to generate point or probabilistic forecasts do not fall

from the sky. They result from past decisions—that is, from past

allocations of resources. Thus, knowledge about the future is paid

for. For example, a retail network may place an extra product on

the shelves of selected stores to assess demand potential. This is

an investment—both the cost of goods and the shelf space—for

uncertain returns.

Investing resources to reduce epistemic uncertainty—that is,

to acquire knowledge—is fundamental in supply chains. To those

who hold a rugged vision, continuously investing scarce resources

to probe the market is so self-evident it scarcely merits mention.

It is simply the natural way of conducting business.

Yet for mainstream supply chain authors — who mostly adhere

to the teleological view — this entanglement between allocations

and knowledge is denied rather than merely left unsaid. While

most authors realize that market knowledge does not appear out

of thin air, they turn to “market studies” and similar bureaucratic

instruments

to acquire knowledge beyond what historical data

oﬀers.

From the teleological perspective, the chief beneﬁt of the market

study is that it leaves the planning paradigm wholly unchallenged.

The study is expected to be completed before planning begins

and to furnish the information needed to produce accurate time-

series forecasts where historical data is lacking. Intellectually,

market studies neatly separate the acquisition of knowledge from

A “market study” is the bureaucrat’s comfort blanket: slow, expensive, and

low-signal. It produces slides and committees, not knowledge and decisions.

Accountability diﬀuses to the point of vanishing while time-to-learning stretches

by quarters. If the goal is to learn, small, instrumented ﬁeld trials beat purchased

reports almost every time.

7.4. PREDICTABILITY OF HUMAN AFFAIRS 299

its exploitation. Yet, like many good-sounding ideas, it fails in

practice.

We return to this link in Chapter Decisions, where we show

how decisions can—and often should—be tuned to harvest the

value of reduced epistemic uncertainty.

7.4.7 High-dimensional forecasts

Because “forecast” is almost always taken to mean “time-series

forecast”, other forms are overlooked. The time-series formalism

is restrictive and covers only a fraction of real-world supply-chain

situations. Yet only a lack of imagination keeps us from exploring

broader kinds of forecasts.

In practice, a time-series forecast is one-dimensional. Even

when the horizon spans many periods, each date’s error is evaluated

in isolation; the multi-period forecast collapses into a stack of

independent one-period forecasts. Whether the numbers come

from one shared model or unrelated formulas is immaterial to

evaluation: standard accuracy metrics treat them as separate

guesses and ignore how errors co-move across the horizon. That

blindness matters as soon as decisions couple dates, items, or

locations.

To show why high-dimensional accuracy matters, consider an

idealized two-product case. Consider a store selling two distinct

products that are a perfect pair of substitutes

. The products

expire at the end of the day, and are replenished daily. Demand

for the two products is 10 units per day; customers pick randomly

between them while stock remains.

Three candidate daily forecasts illustrate the point:

• (A) 5 units and 5 units

• (B) 10 units and 0 units

If the products were indeed perfect substitutes, then the store manager

would have no reason to keep both products as part of the store’s assortment.

Let’s suspend our disbelief for a second, a more convincing, yet more complicated

example will be provided afterward.

300 CHAPTER 7. THE FUTURE

• (C) 6 units and 6 units

Given substitution, a suitable accuracy metric must assign the

same loss or value to (A) and (B). Indeed, both anticipations are

equally good—indeed perfect under our simplistic assumptions.

However, the metric must score (C) worse, since it overestimates

total demand by 2 units. No one-dimensional metric can satisfy

both requirements. Only a measure that looks at the two items

jointly recognizes that (A) and (B) tie while (C) is worse. All

classic scores (mean absolute error, mean square error, and so on)

wrongly rank (C) above (B). The only way forward is to broaden

the perspective to a 2-dimensional accuracy metric.

The example exposes the limits of a one-dimensional view; to

see why they matter, we now turn to a more realistic, probabilistic

example.

Consider a grocery store and its assortment problem: the

choice of the exact list of distinct articles to put on the shelves.

Customers rarely buy a single item; most leave with a basket of

several products. A superior assortment yields bigger or more

proﬁtable baskets on average.

Every assortment still faces stockouts: when a shelf is empty,

customers may pick a substitute. Substitution mitigates the stock-

out. Conversely, the customer may forgo not only the missing

article but also the complementary articles he intended to buy.

Contagion worsens the stockout.

Only by examining historical market-basket data can we study

these substitutions and contagions. Indeed, when market-basket

data are available, we have a natural entry point to assess how

products relate to one another from the customer’s viewpoint.

Furthermore, if loyalty cards are used, sequences of market baskets

— each attributed to a distinct customer — can be reconstructed.

Loyalty data supplements market-basket data for such analysis.

The technical details are not needed here. What matters is

that the relevant information lies in market-basket data. Without

it, no matter the sophistication of our endeavor, the behavioral

analysis fails for lack of it.

7.4. PREDICTABILITY OF HUMAN AFFAIRS 301

In contrast, when sales data are ﬂattened into time-series, we

do not even know how many distinct customers walked into the

store on any given day. We cannot see substitutions and contagions

because all that is left are tentative correlations between time-series

that are both sparse and erratic. Time-series alone cannot support

a behavioral analysis.

Circling back to the assortment challenge, note that a market-

basket forecast — conditioned on the chosen assortment — oﬀers

a straightforward solution. If we can predict future market baskets

under a revised assortment, we can assess whether it will be superior

to the current one — at least in terms of sales volume.

Interpreted as a point forecast, a “reliable” market-basket

forecast is a category error. A single basket often mixes ten or

more SKUs; combinations explode, and shoppers freely substitute

at the shelf. Expecting to name the exact basket in advance

seems—and is—impossible; hence the idea is routinely dismissed.

The mistake is the framing: what matters is not one guessed basket

but the probabilities of co-occurrence and the lift items exert on

one another—precisely the structure we turn to next.

Yet, viewed probabilistically, the outlook is starkly diﬀerent.

Clearly, not every conceivable market basket is equally likely. For

example, any basket containing a pair of products never observed

together in history is improbable. Establishing a probability distri-

bution over the market-basket space may look technically daunting,

but the machine-learning community has developed a sizable “bag

of tricks” for such predictions.

The point-forecast perspective, by ﬂattening everything around

the “one true future”, gives the misguided impression that the

quality of an anticipation can be assessed pointwise, i.e., one

dimension at a time; yet this ignores the dependencies within the

system. Under a probabilistic perspective, those dependencies

become evident through the implausibility of certain outcomes.

Demand can rise or fall, but it is implausible for it to rise in odd-

numbered postal-code areas while falling in the even-numbered

ones. This observation implies a high-dimensional perspective.

Within supply chains, such dependencies are ubiquitous, pre-

302 CHAPTER 7. THE FUTURE

cisely because—as we have seen—they are systems. One product’s

success can cannibalize its substitutes; a grounded jet can cascade

into missed connections; cheaper energy can lift an entire sector’s

margins. The opening of a ﬂagship superstore may attract new

patrons who marginally improve the business of all nearby stores,

whatever they sell. Similarly, a successful marketing campaign can

lift the sales of all the products of a given brand.

Designing high-dimensional accuracy metrics is technical and

lies outside this chapter. What matters is the principle: joint

forecasts must be judged in the geometry of the decision—as a

whole, not coordinate by coordinate. Proper scoring rules exist for

multivariate distributions and let us compare models on a simple

criterion: do they concentrate probability where reality tends to

fall, while preserving the dependencies that drive economics? With

that foundation in place, the remaining gap is agency: a forecast

that ignores our own levers still projects the status quo. The

cure is to condition predictions on the policy the ﬁrm intends to

apply. We call such policy-conditioned anticipations functional

forecasts—the subject of the next section.

7.4.8 Functional forecasts

The future state of the supply chain depends on decisions yet to be

made. Decisions by third parties—customers, suppliers, competi-

tors—should be forecast like any other uncertainty. Indeed, those

decision-making processes may well forever remain black boxes for

the company. By contrast, the company’s own forthcoming deci-

sions warrant diﬀerent treatment. After all, the company needn’t

guess its own decisions; it controls its decision-making.

As commonly used, forecasts grant the ﬁrm little agency. They

describe what is likely under frozen policies, not what could happen

under alternatives. They condition on decisions already taken

when the baseline is computed and remain blind to those still

on the table. A manufacturer may cut prices today and, quite

properly, see forecast demand lift; yet the same forecast cannot

speak to next month’s price move or a promotion not yet chosen.

7.4. PREDICTABILITY OF HUMAN AFFAIRS 303

In short, the baseline projects the status quo—a “do-nothing-new”

counterfactual—useful for orientation, not a substitute for deciding.

To see why unﬁnalized decisions matter, consider a fashion

brand. The brand runs two collections per year, with products

ordered ahead of the season. Assume no in-season replenishments.

The brand displays products at the season’s start at full price.

For each product, planned quantity matches expected seasonal

sales, with a stockout at season’s end. Quantities are set so no

end-of-season discount is expected.

Yet by season’s end, some products underperform expectations,

leaving unsold inventory. The company’s pricing policy dictates

steep end-of-season discounts on underperformers, precisely to

liquidate stock.

Under dynamic pricing, the brand can rationally open with

larger stocks than under ﬂat pricing. If demand proves higher than

forecast, the extra inventory yields proﬁt. Conversely, if demand

is overestimated, the policy still liquidates inventory with minimal

loss. Pricing is no technicality; it is integral to the brand’s ﬂow

strategy.

A “regular” demand forecast, probabilistic or not, cannot cap-

ture the sales trajectories induced by this policy. When the baseline

forecast is computed, there is no reason for projected demand to

exhibit—at season’s end—a late spike from discounts. Discounts

arise only when demand was overestimated; with accurate demand,

initial stock would be lower and no end-of-season discount would

be needed. The policy makes demand partly self-correcting: sales

align with stock levels only insofar as discounts are aggressive.

A functional forecast, by contrast, explicitly incorporates one

or more decision rules. Those policies are taken as given; they are

not learned from historical data. Moreover, those policies remain

isolated within the forecasting logic. Thus, for a model to be

“functional” (policy-conditioned), one should be able to swap a

policy for an alternative and directly obtain the revised forecast. In

that sense, a functional model is incomplete: without the required

policies, it cannot produce forecasts.

As we will see next, functional models need not be much harder

304 CHAPTER 7. THE FUTURE

to implement than their unconditioned counterparts. However,

assessing their validity is a thorny challenge. Indeed, as long as

the policies fed to the functional model remain close to those

historically practiced by the company, a well-engineered model

should behave sensibly. However, if a policy ventures into untested

territory, the model’s validity becomes uncertain.

Revisiting the case above: if the brand historically used end-of-

season discounts between 15% and 30%, the data support modeling

any combination within that range. But if the brand runs its

functional demand model with discounts between 30% and 60%,

any ﬁgures produced will be, at best, highly speculative. Those

predictions lack direct empirical support; extrapolating customer

response to untested prices may prove very surprising.

Unfortunately, beyond narrow situations, judging whether a

functional model will return sensible ﬁgures under an arbitrarily

novel policy is a problem of general intelligence. Assessment must

be tailored to the speciﬁcs. For example, trial the new policy on a

limited scale solely to acquire further empirical data. This process

is exploration, discussed in the next chapter.

Without functional forecasts, a company merely projects the

status quo, replicating its past policies. If the company wants

to evolve, it needs the capacity to project how envisioned policy

changes will aﬀect the ﬂow.

At small scale, functional forecasts are obvious, though seldom

named. Consider a neighborhood baker choosing his opening

hours. Longer hours enable more purchases—early commuters and

late shoppers can buy—yet consume a scarce resource: time with

his family and rest. The relevant forecast is not a single number

(“tomorrow’s demand is 180 loaves”) but a function linking decision

to outcome (“expected sales as a function of opening hours”). By

selecting the input—the schedule—the baker partly manufactures

the demand he intends to serve; this is the functional view.

At scale, when thousands of forecasts are generated, many ﬁrms

lapse into status quo projections driven by bureaucratic inertia.

Naturally, this attitude serves them poorly. Unfortunately, most

supply chain textbooks—and the software built on them—embrace

7.4. PREDICTABILITY OF HUMAN AFFAIRS 305

the same perspective.

Technically, functional forecasts matter for two reasons.

First, a software-engineering reason: they enforce a clean sepa-

ration between the predictive core (models that turn records into

probabilistic statements about demand, lead times, prices, returns,

and costs) and the reiﬁed policy (explicit code and parameters

that implement pricing, allocation, service rules, and other levers).

Once disentangled, the same predictive core can be exercised un-

der multiple admissible policies; policies can be swapped without

retraining; and the economic eﬀect of each rule becomes auditable.

Second, a statistical reason: predictors learn only what past

policies exposed. When a policy ventures outside that data sup-

port—new price ranges, promotion cadence, service promises—the

forecast becomes an extrapolation. Under such a policy shift, preci-

sion degrades and uncertainty widens; results should be treated as

provisional until measurements accumulate under the new policy.

With these constraints in view, functional forecasting builds

the bridge from “what is likely” to “what we would do if . . . ”. The

natural instrument to exercise that bridge at scale is a simulator—a

stochastic, discrete model that embeds policy and propagates

uncertainties. We now turn to it.

7.4.9 Stochastic simulations

A simulation is a model that imitates a real-world system and

lets us observe how its state evolves over time. As computing

power grew, simulators proliferated, including supply-chain ver-

sions often marketed as “digital twins”

. Properly understood, a

simulator is a high-dimensional probabilistic forecast: it produces

many plausible futures—with implicit frequencies—rather than

one ordained trajectory. Used this way, simulation is a legitimate

instrument for anticipating future supply-chain states. Yet because

this probabilistic nature is rarely made explicit, outputs are too

“Supply Chain Digital Twin” is a marketing term many vendors use for

their simulation oﬀerings.

306 CHAPTER 7. THE FUTURE

often taken at face value; assumptions go unexamined, and few

bother to check that the simulated distributions resemble what

operations actually deliver.

Supply-chain simulators vary widely, yet nearly all share three

traits: they are microanalytic, discrete, and stochastic.

A microanalytic (bottom-up) simulator decomposes the sys-

tem into minimal units governed by simple rules. Units include

agents—customers or suppliers—and inanimate entities such as

SKUs. The microanalytic view assumes system complexity is

merely apparent and arises from repeated application of simple

rules.

A discrete simulator (discrete-time or discrete-event) proceeds

through a sequence of events. Each event occurs at a particular

instant and marks a change of state in the system. Between

events, nothing changes—an expedient that vastly simpliﬁes the

simulator’s software implementation.

A stochastic simulator (often called a Monte Carlo simulator)

uses non-deterministic rules that produce random outcomes. Thus,

repeated runs from identical initial conditions produce diﬀerent

results. Use deterministic rules when possible; reserve stochastic

rules for unknowns, reﬂecting our imperfect knowledge of the

system’s evolution.

Simulators are instruments of functional forecasting: they map

explicit ﬂow policies to predicted outcomes. By encoding decision

rules—what to buy, make, move, and price—and when—along

with operational constraints, the simulator produces forecasts

conditioned on those rules. That conditioning is the point: it puts

alternative policies on equal footing and lets us quantify their

economic consequences.

Bypassing that conditioning almost always forces crude approx-

imation. For example, treating demand as unaﬀected by quality

of service—assuming repeated stockouts never depress observed

sales—blinds the simulator to the very behavior the policy in-

duces. Such a model systematically overstates attainable volumes,

misprices options, and steers decisions oﬀ course. Hereafter, “func-

tional forecast” denotes this policy-conditioned prediction rather

7.4. PREDICTABILITY OF HUMAN AFFAIRS 307

than an unconditional time series.

The software implementation of such simulators is conceptu-

ally straightforward. A main loop traverses the edges of a graph

representing the ﬂow. Nodes typically represent SKUs—or aggre-

gates when ﬁner granularity is too costly. Each node carries a

small set of rules, either deterministic—e.g., decrement stock per

outgoing unit—or stochastic—e.g., draw random demand for the

period. Each iteration of the main loop represents a discrete time

step—typically a day, possibly shorter.

If one doesn’t look too hard at validity, such a ﬂow simulator can

often be built in a few days. Hence its popularity with enterprise

software vendors. The functional design is appealing: it yields

arbitrarily ﬁne-grained policy predictions. Yet without rigorous

validation, that trust is misplaced.

In fact, simulator outputs can be—and often are—outright

nonsense. A rule’s plausibility does not guarantee its correctness,

nor does pairing two valid rules ensure the simulator combines

them correctly. Mundane coding errors also creep into the simula-

tor’s codebase. As noted earlier, novel policies may undermine the

stochastic rules if the simulation enters “uncharted” territory. Ulti-

mately, validity is empirical and must be settled by measurement.

A duality links probabilistic forecasts and stochastic simulations.

From a probabilistic forecast, it is possible to generate samples

according to the underlying probability distribution (such samples

are usually referred to as deviates). From a complete probabilistic

forecast, one can derive the stochastic rules that drive the system;

conversely, observing a simulator reveals the probabilities of all

outcomes. Thus a simulator can estimate the complete probability

distribution over all admissible states; hence a probabilistic forecast

can be derived from a simulator.

In practice, numerous technicalities hinder conversion between

a probabilistic forecast and a simulator. The probabilistic forecast

might not be suﬃciently ﬁne-grained to specify all the simulator’s

rules. Conversely, the number of runs needed to estimate all the

probabilities may be prohibitively large. Even so, the duality

implies that stochastic simulations share the accuracy metrics of

308 CHAPTER 7. THE FUTURE

probabilistic forecasts.

Therefore, treat a simulator like any forecast: quantify its

accuracy. The work is technical; ignoring quantitative assessment

invites costly mistakes.

Chapter 8

Decisions

Supply-chain practice is the continual allocation of scarce resources

under uncertainty. The aim is not a plan but a proﬁtable ﬂow:

convert cash into goods, goods into service, service back into

cash—while preserving the options that future information will

render valuable. Even mid-sized ﬁrms make thousands of such

allocations daily—and the largest, millions—so the decision system

cannot be ad hoc. It must make choices explicit, price them, and

then sequence and execute them so that unattended software can

issue them safely at scale. Systematization does more than guard

against a rogue buyer exhausting working capital; its purpose is to

produce decisions that, in expectation, earn superior risk-adjusted

returns.

To close the gap between principle and practice, we now sharpen

the notion of a decision ﬁrst sketched in Chapter 1.

Deﬁnition (Decision).

A ﬂow commitment that allocates scarce resources among

admissible options, foreclosing alternatives in pursuit of the

highest expected risk-adjusted rate of return.

A deﬁnition, however crisp, leaves two practical questions. First,

where does a decision begin and end once time and randomness

309

310 CHAPTER 8. DECISIONS

enter? Second, by what single criterion are alternatives compared

when they draw on the same scarce pools—cash, capacity, atten-

tion? In supply-chain settings, a decision is not a line on a plan. It

is a ﬂow commitment taken at a granularity ﬁne enough to change

economics, made under uncertainty, and recorded in a ledger that

future events will audit.

Rendering such commitments computable and auditable re-

quires bringing four ingredients to the surface. The frame comes

ﬁrst: the present sphere of agency—admissible moves, feasibility

tests, cadence, and the few ratchets that erase tomorrow’s op-

tions—must be written—ideally as code. Next comes a probabilistic

view of near futures: demand, lead times, returns, reliabilities, and

any other distributions that materially aﬀect outcomes. Third, all

penalties and rewards must be expressed in money and attached

to owners—stockout pain, obsolescence, capital charge per unit

time, congestion, late fees, the option value of waiting—so unlike

frictions become commensurable on one ledger. Finally, marginal

commitments are ranked by their expected, risk-adjusted aperiodic

rate of return; those that clear the hurdle are issued, logged, and

later judged against realized outcomes.

The chapters so far have prepared this stance: optionality and

variability (Chapter 1) supply the space of moves and explain why a

probabilistic lens is mandatory; economics (Chapter 4) provides the

coin-denominated objective; information and intelligence (Chap-

ters 5–6) delimit the roles of records, reports, and decision engines.

The present chapter turns these pieces into practice. It begins

by thinking the decision: surfacing the frame, banishing artifacts,

opening the economics to inspection, and restoring reasoning at the

margin. Subsequent sections examine unattended execution and

the optimization machinery suited to repeated, uncertainty-laden

choices, so that each commitment becomes a small, priced wager

the ﬁrm can both defend ex ante and audit ex post.

8.1. THINKING THE DECISION 311

8.1 Thinking the decision

A decision, as deﬁned above, is a commitment that erases alter-

natives. Thinking the decision therefore begins one step earlier,

with the act that selects which alternatives are even seen and

how they will be priced. In most ﬁrms, this prior act remains

tacit. People talk in the grammar of familiar “policies”—FIFO,

full trucks, minimum order quantities, shelf plans—so the choice

appears obvious and the unchosen, invisible. The danger is not

clerical error but metaphysical slippage: a policy silently substi-

tutes for the economic question it was meant to answer. Reversing

that slippage—bringing the question back to the surface, keeping

artifacts in their place, making the economics explicit, and rea-

soning at the margin—turns decision

making from ritual into a

scientiﬁc practice ﬁt for automation.

8.1.1 Surfacing the frame

Managers are tempted to think with solutions—not a folly, but a

correct and useful heuristic. The space of conceivable “problems” is

boundless—designing an anti

gravity engine is a problem too—and

without the scent of a solution most inquiry would be wasted. Yet

policy

ﬁrst thinking fossilizes quickly. “Serve stores FIFO” begins

as a decent rule of thumb and, over time, hardens into doctrine;

each exception is patched by another rule, and soon the ﬁrm steers

by accreted case law.

What must be surfaced, before any policy, is the frame. It

is the concrete, inspectable boundary of agency for the present

decision cycle: what can be altered now and what cannot; which

options are admissible; which random elements will matter; which

commitments would ratchet the future; and at what prices this

slice of reality will be judged. The frame should be written, not

merely described. In a modern organization, it lives as code: a

short program that enumerates admissible moves, encodes the

feasibility tests, and calls the valuation function. If the program is

long, the frame is muddled; if the program is opaque, the frame is

312 CHAPTER 8. DECISIONS

political.

Once framed, the same situation admits diﬀerent moves. Con-

sider an importer that ﬁlls containers “product

product until

full”, deferring the rest to the next sailing. The policy sounds

prudent; the frame it hides is narrow indeed. Make the admissi-

ble options explicit—co-loading across suppliers, temporary cross-

docking to other regions, price nudges synchronized with sailing

dates, late binding between look-alike items—and then the opti-

mization no longer searches within one corridor but roams a small

atlas. The gain comes less from a cleverer solver than from a better

question. Problem design is not icing; it is the cake.

8.1.2 Banishing artifacts

Once a frame exists, numerical artifacts will appear in the ma-

chinery: forecasts, buﬀers, service levels, ABC tags, budgets, and

their innumerable cousins. They are often useful as intermediate

representations, just as a chemist uses reagents that never leave

the bench. Trouble begins when an artifact escapes the lab and

is enthroned as a target. Goodhart’s law then takes over: the

measure becomes the goal and ceases to measure.

Service level is the classic escapee. It began life as a proxy for a

money

denominated trade

oﬀ between shortage pain and carrying

cost; it ended as a percentage that commands the organization.

Planners raise it to feel safe, ﬁnance lowers it to free cash; neither

side is forced to state prices. Forecast accuracy plays the same role

for teleological planning: its improvement is celebrated even when

proﬁt falls.

Artifacts should be kept on a leash. Inside the decision en-

gine, they are acceptable as transient coordinates that make a

computation tractable; outside, they have no standing of their

own. A replenishment is not “good” because its SKU hit 97%

service; it is good because, given the options, constraints, and

probabilistic futures considered in the frame, it maximized the

ﬁrm’s risk-adjusted rate of return. The engine may well compute

a distribution over daily demand, a shadow “service level” implied

8.1. THINKING THE DECISION 313

by the shortage penalty, and a working

capital charge per unit per

day; it may even print them for inspection. But dashboards and

incentives must anchor on decisions and their realized economics,

not on the laboratory glassware that helped to compute them.

There is a second reason to banish artifacts from the altar:

they are systematically harder to value than the decision itself

is. A safety stock number has no invariant economic meaning

across items, seasons, or geographies; its virtue is parasitic on

the hidden penalties and opportunities it stands in for. Turning

such a chameleon into a KPI invites bureaucracy to colonize the

ﬂow. The cure is to treat every artifact as a private, provisional

object, recomputed as needed from authoritative records, never

hand

maintained, never made a target, and discarded as soon as a

better internal representation presents itself.

Artifacts are not only numerical. Organizations also mint

bureaucratic artifacts—policies and partitions—that began as rough

safeguards for attended processes and later ossiﬁed into rules

that substitute themselves for the economic question. When such

artifacts leave the lab and acquire institutional standing, they

distort decisions exactly as service levels did: the means becomes

the end.

As British historian Cyril Northcote Parkinson cautioned in

Parkinson’s Law (1957), “work expands so as to ﬁll the time

available for its completion.” Bureaucracies grow by reﬂex; large

organizations running sizable supply chains are no exception. As

they swell, extraneous rules and internal partitions accumulate

in their decision-making processes, surfacing mainly as made-up

constraints and made-up silos—both of which sap eﬃciency and

blur accountability.

Made

up constraints typically arise after a mishap. Roger from

purchasing orders eighteen months’ worth of sales by mistake; in

response, management imposes a policy capping purchase orders at

“six months of sales”, measured by a moving average. In manual

settings, clerks enforce this cap with common sense: they ignore it

for new products with no history, for highly seasonal items whose

comparable is twelve months back, for fast

growers whose future

314 CHAPTER 8. DECISIONS

dwarfs the past, or for planned promotions whose lift is engineered.

An unattended engine cannot “sensibly ignore” rules; it either

obeys the letter or is declared noncompliant.

The remedy mirrors the treatment of numerical artifacts: keep

such rules private to the engine and express them as feasibility

tests and priced penalties, not as public commandments. If buying

above a naive “six

month” cap is occasionally rational, the cap

belongs in the objective as a coin

denominated nuisance, not as

a veto; newness, seasonality, and engineered lifts become explicit

features of the frame, not tribal exemptions. A rule written as

code, priced and owned, is legible and contestable; a rule written

as a decree invites gaming and stiﬂes automation.

Made-up silos fracture a single economic question into a proces-

sion of artifacts so that lightly tooled teams can cope. A fashion

retailer, for instance, runs the warehouse-to-store ﬂow through

three artifacts: an assortment list (what a store may carry), a

launch pack (how much at start), and a replenishment rule (what

to send later). The choreography looks tidy; it quietly manufac-

tures constraints. The assortment list pre-commits shelf real estate

and vetoes substitutions that early sell-through would justify; the

launch pack pre-allocates labor, line-haul, and facing capacity that

replenishment must then buy back—often at a premium; the re-

plenishment rule then pretends to optimize what is left. Money

is left on the table because the underlying question—where does

one more unit earn the highest risk-adjusted return, given current

constraints?—was broken into artifact-bounded procedures before

it could be asked end-to-end.

Here again, artifacts must stay on a leash. Silos may curate

The “Simple Sabotage Field Manual” (1944), published by the Oﬃce of

Strategic Services (precursor of the CIA), recommends bureaucratic sabotage.

The inﬁltrated agent must insist that the organization apply all policies, all

rules, and all regulations to the letter (no matter how trivial), thereby burying

the organization in red tape and slowing progress to a crawl. Far from being an

insight of historical interest, modern companies operating large supply chains are

more susceptible to bureaucratic sabotage than ever—so much so that accidental

bureaucratic self-sabotage has become part of the daily routine for many, if not

most, large companies.

8.1. THINKING THE DECISION 315

those artifacts; they must not own the decision. The engine should

see the end

end frame—admissible moves, shared constraints,

and prices—and produce a single ﬂow of commitments. If gover-

nance requires organizational boundaries, the interface must carry

prices and feasibility, not quotas and vetoes: the distribution center

(DC) publishes shadow prices for scarce resources; stores publish

shortage penalties; assortment is a proposal the engine may expand

when economics justify it. In other words, keep the ceremony

parochial and the economics uniﬁed.

Once artifacts—numerical and bureaucratic—are domesticated

as private, priced, and provisional objects, the stage is set for the

only debate that matters: the prices themselves. The next section

turns to that point directly.

8.1.3 System economics

The previous chapter established money as the ﬁrm’s common scale.

System economics requires that every decision be priced from the

vantage of the whole ﬁrm, not a departmental KPI. In practice,

this means the prices inside the objective function—stockout pain,

obsolescence, shelf congestion, late fees, working-capital cost, op-

tion value of waiting, goodwill loss—are stated once company-wide,

attached to named owners, versioned, and exposed. Concealed

prices do not become neutral; they bias the ﬂow toward whoever

smuggled them in. A hidden penalty for backorders is a private

agenda masquerading as science.

Making the economics system-wide does not require a plebiscite

for every coin; it requires numbers legible and contestable across

functions. Finance should publish the ﬁrm-wide cost of capital

and liquidity limits; operating teams should translate them into

per-unit, per-day charges reﬂecting inventory half-lives and sal-

vage values. Merchandising should not demand “98 % availability”

without pricing the next point in coins per day; logistics should

not insist on full-truck thresholds without pricing the congestion

and spillover they avert. Where markets exist, borrow external

prices; where they do not, mint private valuations and defend

316 CHAPTER 8. DECISIONS

them. To arbitrate across silos, scarce shared resources—dock

slots, shelf space, line time, capital—should carry shadow prices:

engine-computed, provisional numbers expressing the current op-

portunity cost of using one more unit. They replace quotas and

vetoes; they are not a new policy. We return to them in the discus-

sion of sequential decisions below. The goal is not unanimity but

comparability on the company P&L. Once penalties and rewards

are expressed in the common coin, options that used to live in

diﬀerent provinces—“grant a refund beyond policy”, “buy one

more unit of a long-tail SKU”, “hold a truck to top up a route”,

“extend the return window for this class of customers”—compete

on the same ledger.

This system view also clariﬁes accountability. If a decision sys-

tematically underperforms once outcomes materialize, the dossier

shows whether the failure lies in the frame (options omitted), the

forecasts (poorly calibrated tails), or the prices (penalties mis-

stated). Corrections then target the oﬀending ingredient rather

than diﬀusing into vague calls for “better collaboration”. Local

KPIs lose their alibi: a move that ﬂatters a departmental metric

but destroys coins elsewhere will be exposed by the aggregate

ledger. Hidden prices breed politics; open, system-level prices

breed performance. With company-wide prices in place, the engine

returns to its natural stance: reasoning at the margin under a

single ledger.

8.1.4 Embracing the margin

Economics is a marginalist discipline; so is supply chain when

practiced properly. The relevant question is almost never “what is

the right stock level for this SKU this season?” but “what is the

risk-adjusted return of one more unit—here, now—given everything

already committed?” The answer is a number in coins per unit per

unit time, and it shifts as the commitment shifts. Thinking at the

margin forces grain, time, and contingency into view.

Grain ﬁrst. Decisions should be computed at the ﬁnest granu-

larity that materially changes their economics. For commodities,

8.1. THINKING THE DECISION 317

the unit is a piece; for transport, a stop or a cubic meter; for

aviation rotables, a serial number whose cycles and hours render

two parts with the same P/N economically incomparable. An

airline can rightly sell a given unit to a rival’s AOG (Aircraft On

Ground) request while keeping another ostensibly identical one to

hedge its own tail risk; the marginal values diverge because the

insured futures diﬀer.

Time next. Marginal values are aperiodic: they must account

for the half-life of the load, not just the lead time. A crate that

clears in two days with deferred payment can be quite proﬁtable,

though the same crate, paid up front and sold over eight weeks,

is mediocre. When the engine ranks admissible moves by their

aperiodic rate of return—as argued in Chapter Economics—it

naturally prefers quick-cycling bets and penalizes long ratchets

unless their upside is commensurate.

Finally, contingency. Margins live on distributions, not on

points. The extra unit that looks proﬁtable under the median

forecast may be ruinous once a fat-tailed lead time is acknowl-

edged; conversely, a unit that seems wasteful under a thin-tailed

model becomes attractive when a rare but catastrophic stockout

is correctly priced. This is why a probabilistic forecast is not a

luxury: it is the substrate on which the marginal calculus operates.

At scale, embracing the margin turns the ﬁrm’s daily work

into a continual resorting of millions of micro

options. Each new

commitment reshapes the frontier: shadow prices emerge on tight

constraints, rival moves change the payoﬀs, yesterday’s best next

step becomes today’s third

best. A static plan cannot price moving

edges; a decision engine can. It does not conjure certainty; it

computes, at each instant, the best next wager and writes it into

the ledger with reasons. The rest is merely bookkeeping.

Thinking about the decision this way—frame explicit, artifacts

domesticated, economics open, margins sovereign—does not add

ceremony. It subtracts superstition. It also aligns with the rugged

vision of the previous chapter: decisions become small, auditable

wagers placed under uncertainty, guided by prices the ﬁrm is willing

to defend, and revised as reality reveals itself.

318 CHAPTER 8. DECISIONS

8.2 Why automate

Computers are unreliable, but humans are even more unre-

liable. Any system which depends on human reliability is

unreliable. One of the many Murphy’s Laws.

The previous pages deﬁned a decision as an auditable commit-

ment that erases alternatives and is justiﬁed only by its expected

risk

adjusted rate of return. Once the frame is made explicit

and the economics opened, a supply chain becomes what it has

always been in substance: a mill of innumerable small wagers

whose edge is earned at the margin. At modern scale, the relevant

edge is inaccessible to unaided clerks. What exhausts them is not

arithmetic—spreadsheets already shift that burden—but the sheer

combinatorics of admissible options, the demands of probabilistic

reasoning under fat tails, and the continual repricing of constraints

as reality moves. If the bottleneck is felt in SQL dashboards rather

than in steel and diesel, the ﬁrm has chosen to limit itself in the

one realm where limits are least binding—computation.

There is a second, quieter reason to automate: repetition under

control. A manual or semi-manual process changes every time

people change; staﬀ turnover, local ﬁxes, and shifting attention

make yesterday’s baseline incomparable with today’s. By contrast,

an unattended process can be duplicated, perturbed, and measured.

Two numerical recipes can run concurrently against the same

ledgers—one in authority, one in shadow—while drawing on the

shared staﬀ because neither demands daily supervision. Measured

this way, progress ceases to be a matter of opinion; it becomes an

audited delta in coins.

Spreadsheets made clerical work tolerable; they never made it

eﬀective. Their “logic” is a tangle of ad hoc formulas that neither

encode a frame nor price uncertainty. The result is familiar: last-

minute overrides, safety buﬀers stacked on safety buﬀers, and a

ritualized haste in which a clerk must ﬁnalize a hundred quantities

before lunch. It is tempting to imagine that granting everyone more

time would yield better answers. In practice, the pause inﬂates

headcount, deepens silos, and delays commitments—each delay an

8.2. WHY AUTOMATE 319

opportunity cost that rarely appears on the dashboard but always

appears on the P&L.

Automation, in the present sense, is not “using more soft-

ware”. It is the institutional decision to let systems of intelli-

gence—introduced earlier—compute, issue, and log decisions unat-

tended under normal conditions. What follows examines this

stance from several angles: as an asset, as a discipline for over-

rides, as a cultural turning point, and as the natural outcome of a

century-long trajectory from management to machinery.

8.2.1 A productive asset

Automation turns decision

making from perishable labor into a

capital good. A system of intelligence is not a report that “assists”

a planner; it is an engine that commits coins on the ﬁrm’s behalf

according to an objective it can defend. The expense proﬁle ﬂips:

what was OPEX—hours spent nudging cells—becomes CAPEX—a

codebase whose operating cost is negligible relative to the volume

of choices it processes. Each engineering day spent improving this

asset is accretive: either decisions get better, compute gets cheaper,

or upkeep gets lighter. If none of these occurs, no further money

should be spent.

This asset is productive because it encodes the frame and

the prices. Unstated heuristics that once lived in memos and

meetings migrate into version

controlled code. The engine reads

authoritative records, infers the distributions that matter (demand,

lead times, returns, repair times), evaluates admissible options

under those distributions, and writes back auditable commitments.

It does so at the cadence reality demands, not at the pace a

committee can endure. Dual-run becomes routine: the incumbent

recipe holds the pen while a challenger produces shadow decisions,

and a clean A/B delta accumulates in the ledger. When conﬁdence

drops—the tails look miscalibrated, a constraint begins to bind

unexpectedly—halting heuristics suspend emission; the fallback is

not a thousand exceptions but a stop and a ﬁx.

Software is cheap to run and, if kept small and legible, cheap

320 CHAPTER 8. DECISIONS

to maintain. Its performance limit is not human patience but the

economics encoded in it. Any genuine insight—about substitution,

deferring or expediting, rerouting, the option value of waiting—can

be folded into code and thereafter applied at machine scale. The

clerical ceiling disappears; what remains is the strategic ceiling set

by the ﬁrm’s ability to price what it values.

8.2.2 Manual overrides

Automation always raises the question of when to intervene. An

emergency stop belongs in any powered machine; routine “manual

corrections” usually signal a design ﬂaw.

Vendors have long softened brittle automation by deputizing

users as human coprocessors. Alerts and “exceptions” ﬂood inboxes;

planners are asked to inspect most outputs and override many.

This pattern defeats the purpose. If a system needs a person to

catch bad decisions, that person will soon distrust it, and the

organization will revert to spreadsheet theatre with extra steps.

The cure is architectural rather than hortatory. First, the

engine must be built so that every decision it emits is sound on

its own terms: framed within admissible options, priced in coins,

and justiﬁed under a probabilistic view of the future. Second,

when unsure, the engine must halt—not pester. Halting is not

abdication; it buys time for the only interventions that durably

improve outcomes: edits to the frame, the prices, or the forecast

semantics. Third, trust is earned by design: dual-run before

cutover; visible dossiers for every commitment; explicit, versioned

valuations; and measured deltas after the fact. Chapter Engineering

will call this discipline experimental optimization.

Overrides do not vanish; they change nature. A rare, targeted

override edits a valuation (“the shortage penalty for this class of

customers was understated”), narrows the option set (“do not ship

lithium batteries by air this month”), or repairs a semantic fault

(“this ﬁeld changed meaning after the ERP patch”). It does not

scribble a diﬀerent number on a purchase order while leaving the

machine untouched.

8.2. WHY AUTOMATE 321

8.2.3 Culture and unrest

Rising productivity invariably unsettles incumbents. Tiberius

refused cranes; the Luddites smashed looms. Modern ﬁrms are

spared neither anxiety nor politics, and white-collar workers face a

novel twist: they are asked to help automate their own jobs.

The paradox dissolves once roles are seen clearly. The work

to be mechanized is the clerical grind of enacting yesterday’s

heuristics. The work to be elevated is the articulation of frames

and prices, the stewardship of semantics, and the rapid rewiring of

logic when the world shifts. Software cultures solved this tension

decades ago. They prize people who make themselves obsolete at

the keyboard—because value moves to the next problem: the tool

becomes the asset; the engineer moves on. Where this culture is

resisted, markets—not memos—adjudicate. Firms that refuse to

compound the asset are displaced by those that do.

Executives cannot purchase this culture oﬀ-the-shelf, but they

can remove the incentives that poison it. Reward decision quality,

not seat counts. Pay vendors for outcomes, not logins. Promote

the contributors of better frames and valuations, not the curators

of larger dashboards. Above all, give the few who can actually

rewire the engine the mandate to do so quickly—their leverage

dwarfs a roomful of human schedulers.

Two recurrent patterns merit daylight. First, headcount is still

treated as a proxy for status. Managers who grow the number

of “seats” under them gain budget and inﬂuence; automation,

which collapses seats, feels like a demotion. Reverse the currency.

Tie prestige to uplift per person and per watt. Make shrinking

attended seats an explicit objective and celebrate the teams that

make themselves obsolete at the keyboard; in a software culture,

this is advancement, not abdication.

Second, per

seat licensing and “engagement” metrics make

vendors complicit in keeping humans in the loop. A system that

bills by logins, clicks, or “active users” is economically opposed

to unattended ﬂow. Contracts should be priced on outcomes the

ﬁrm wants—volume of unattended commitments issued with audit

322 CHAPTER 8. DECISIONS

trails, measured uplift against a dual-run baseline, time to rewire

logic after a frame change—not on the clerical motion the tool

induces. When money is paid for results rather than for screens,

incentives align with automation rather than against it.

Finally, beware of metric theater: when bonuses hinge on accu-

racy percentages or on-time scores detached from coin-denominated

valuations, energy ﬂows to improving the indicator rather than the

economics.

8.2.4 Human exceptionalism

The claim that “a computer must never make a management

decision because it cannot be held accountable”

seems humane;

it is, in fact, confused. Most large organizations already run vast

tracts of policy that leave no room for discretion. A credit hold on

overdue accounts is enforced whether a person clicks the button or

a program does. Accountability attaches to the policy’s authors

and the executives who endorsed the prices and constraints it

encodes, not to the clerk who executes it.

Insisting on a human hand for symbolism degrades both speed

and quality. It also invites a worse failure mode: invisible responsi-

bility. When a rule lives in a spreadsheet and a routine exception

is scribbled into a cell, no one learns. When the same rule lives in

code and an outlier forces a halt, the design is amended in daylight.

The relevant test is economic, not anthropomorphic: given the

same inputs, the same admissible options, and the same audit trail,

does the system yield higher risk

adjusted operating proﬁt than

the incumbent? If yes, it is “intelligent enough” for the task.

8.2.5 From management to machinery

The puzzle is not why automation is desirable, but why it took

so long to become routine outside a few niches. For most of

the 20

century, decision processes were necessarily managerial;

An IBM presentation from 1979

8.2. WHY AUTOMATE 323

only human minds could digest information and emit sensible

choices. The question then became how to divide the labor across

many minds. Consulting authors proposed elaborate fabrics of

committees and calendars to make the division bearable. The

fundamental ﬂaw was silent: what is “best” for a fragment seldom

aligns with what is best for the whole. A homeware division that

deploys one million coins at an expected return of ten percent can,

without malice, preempt the kitchenware division from deploying

the same million at ﬁfteen percent. However ﬁnely the organization

is sliced, all managers draw on the same ﬁnite pools of cash,

inventory, capacity, and attention.

Bureaucratic methods tried to heal this with more communica-

tion. Meetings, memos, and escalations transported fragments of

knowledge upward, then downward, hoping that alignment would

follow. Yet allocation is quantitative; without a common ledger of

prices, coordination reverts to hierarchy. Operations research, born

in wartime, promised a diﬀerent future: let algorithms optimize the

allocation end to end. Hardware constraints, crude data semantics,

and a taste for deterministic toys conspired to blunt that promise.

By the late 1970s, Russell Ackoﬀ was already writing its obituary.

Spreadsheets ﬁlled the vacuum. They multiplied clerical capac-

ity but froze decision

making at a pre

scientiﬁc stage. Attempts

to replace them with “planning” modules failed because those

modules inherited the same deterministic posture and the same

artifact worship. Users drifted back to ﬁles; vendors added more

screens. The decision load returned to white-collar ranks, now

larger than the blue-collar ranks that move cartons.

The economics never changed: repetitive supply

chain deci-

sions can and should be automated. The enablers matured quietly.

Systems of records stabilized; probabilistic methods, once exotic,

became pedestrian; hardware became cheap; and a new architec-

tural species—systems of intelligence—emerged to sit alongside

ledgers and reports rather than pretending to replace them. Fully

unattended decision engines have been in production for more than

324 CHAPTER 8. DECISIONS

a decade in ﬁrst-movers

. The pattern will spread, and spread-

sheets will keep their place as Swiss-Army knives—handy in a

pinch, absent from the production line.

Two further obstacles, often overlooked, explain the long delay.

First, distributing a decision across many heads is intrinsically

treacherous. No natural ordering exists that makes local choices

commute. Advertising budgets, shelf space, and factory capacity

for a new product constrain one another; whatever team “goes ﬁrst”

traps the others into an inferior corridor. The correct resolution is

not another committee, but a uniﬁed optimization instance that

draws on each team’s expertise where it belongs—in valuations,

constraints, and scenarios—while letting the solver synthesize a sin-

gle commitment. Yesterday’s commitments, once enacted, re

enter

as data; the next cycle proceeds on a narrower but clearer stage.

Second, organizations accumulate made

up constraints to protect

themselves from their own past errors. Manual processes survive

by ignoring these rules when common sense demands it; software

cannot. The cost of automation is therefore paid up-front in the

removal or re-pricing of such constraints; the dividend is paid

forever in cleaner frames and higher returns.

The broader arc is familiar. Over the last three centuries,

proﬁt

seeking entrepreneurs relentlessly substituted capital for

labor, and blue

collar shares fell accordingly. In many indus-

trialized regions, the headcount managing the ﬂow now exceeds

the headcount physically moving it

. The next increment of pro-

ductivity will come from automating away the clerical part of

white

collar work. Resistance will be spirited; the economics will

be patient—and decisive.

For example, Amazon’s Supply-Chain Optimization Technologies (SCOT)

team has end-to-end responsibility for orchestrating Amazon Store’s supply

chain—demand forecasting, buying, inventory placement, and customer-promise

decisions. Senior leadership decided in 2014 to make several product categories

“100% automated”, with subsequent rollout across the retail business.

Warehouses, once requiring sizable workforces, have been increasingly re-

placed by fully automated warehouses since the 2010s. Transportation will

follow. In 2024, there are over 2 million fully self-driving vehicles in operation

worldwide.

8.2. WHY AUTOMATE 325

Finally, a word on the technological “why not earlier”. Until

recently, three preconditions were missing. Ledgers were too fragile

to serve as authoritative ground truth; probabilistic reasoning was

too cumbersome to be embedded in everyday code; and vendors

were rewarded for selling CRUD at intelligence prices. All three

have shifted. Systems of records have plateaued in value and

reliability; probabilistic toolchains ﬁt on a laptop; and buyers have

learned to separate bookkeeping, reporting, and decision engines.

Once these are in place, the case for automation is not rhetorical;

it is arithmetical.

In short, we automate not to polish a plan but to restore eco-

nomics to the driver’s seat. Unattended decision engines put the

bottleneck where it belongs—in atoms and hours—while deliver-

ing the one precondition for sustained improvement: comparable

experiments that run daily, at scale, with coins on the line.

8.2.6 Where to start

Automation is not a slogan; it is an allocation problem: where

should the next coin of engineering eﬀort be spent so that unat-

tended decisions repay fastest? In practice, the operative notion

is the diﬃculty of a decision process, understood economically as

the inverse of the expected return on investments in improving

it. High expected return, low diﬃculty; low expected return, high

diﬃculty. Two regimes matter most. The base diﬃculty is the cost

of migrating a ﬂow from attended spreadsheets to an unattended

decision engine under an explicit frame. The marginal diﬃculty is

the cost of extracting the next increment of performance from a

ﬂow that is already automated.

The point is simple and vivid: scale lowers base diﬃculty.

When millions of small, similar commitments recur and the frame

can be written down—admissible options, feasibility tests, and

money-denominated prices—the ﬁrst automation passes typically

repay quickly. By contrast, small, idiosyncratic niches combine low

payoﬀ with high marginal diﬃculty; they are the worst places to

start and should be tackled last, however tempting their apparent

326 CHAPTER 8. DECISIONS

tractability.

Consider a grocer whose national distribution center feeds

800 stores nightly. Automating DC

store dispatch has a short,

legible frame: admissible moves are “ship 0/1/2 units of SKU

store

tonight”; feasibility tests are truck cube, door slots, and

store caps; prices are shortage pain, aging, line

haul and handling,

plus a shadow price on distribution center stock; distributions are

day sell

through and late

truck risk. One numerical recipe

then covers tens of thousands of homogeneous micro

decisions per

day, so a ﬁrst pass—greedy accumulation under these prices, with a

halt when conﬁdence drops—repays quickly. By contrast, starting

with a boutique ﬂow—quarterly pop

up assortments for twelve

ﬂagships entangled with visual

merchandising rules, ad hoc launch

events, and planogram politics—oﬀers little scale and murky prices;

the “frame” is mostly ceremony. The large river is easier than the

puddle: scale makes base diﬃculty low, while idiosyncrasy makes

it high.

Engineers often misread solver timing curves. For any ﬁxed tech-

nique, enlarging a model—more variables, more constraints—almost

invariably induces superlinear growth in CPU and memory.

That

curve is a property of the method, not of the business problem.

Economics supplies levers the curve ignores: coarsen the frame

wherever granularity does not move money; shorten horizons to

the next moment of control; replace brittle hard limits with priced

penalties; and help the solver with warm starts and decomposi-

tions so yesterday’s answer speeds today’s search. The goal is not

algebraic purity but risk-adjusted return within a sensible compute

budget, day after day. In practice, a rough-but-priced formulation

that runs reliably outperforms “exact” models that time out—and

both beat spreadsheet clericalism once scale is present.

A practical triage follows. Begin where the frame is short and

legible: options are enumerable without heroics, feasibility tests

are mechanical rather than political, and the prices that govern

An algorithm is superlinear when its time or memory rises faster than the

problem size; doubling the variables can more than double the cost.

8.3. DECIDING IS OPTIMIZING 327

the objective—stockout pain, obsolescence, capital charge per

unit time, the option value of waiting—can be stated, owned, and

versioned. Favor ﬂows whose cadence gives the engine time to think

(minutes to hours): overseas purchasing and consolidation, DC-to-

store dispatch, network stock balancing, repairables provisioning.

Defer micro-niches whose economics rest on unresolved, implicit

prices—governance problems before engineering ones. Above all,

let measurement decide: run challengers in dual-mode against

incumbents, attribute deltas in coins within a clear window of

responsibility, and promote only when the uplift pays for itself.

Seen this way, “where to start” is no mystery. Attack the

biggest rivers of decisions ﬁrst, accept approximate answers that

respect the economics and the frame, and let the unattended engine

compound from there. Small problems feel safer; large ones are

easier.

8.3 Deciding is optimizing

Mechanizing choices requires a formalism that tells a machine what

to prefer and when to stop. “Optimization”, a branch of applied

mathematics, supplies that formalism—but only once it is recast to

ﬁt the economic and iterative character of real decisions. The text-

book posture—state a model, hand it to a generic solver, await a

provably optimal plan—misﬁres in practice. What works is humbler

and more powerful: an optimization frame that exposes admissible

moves, prices their consequences in money, ingests probabilistic

views of tomorrow, and is engineered to run—unattended—again

and again as the world shifts.

8.3.1 The optimization paradigm

Classical mathematical optimization notation speaks of variables,

constraints, and an objective (see the annex). We keep the triad

but shift its meaning to match the decision grammar developed

earlier in this chapter and in Economics and Intelligence.

328 CHAPTER 8. DECISIONS

Variables are not blunt totals (“the quantity for SKU X this

month”) but marginal commitments at the ﬁnest grain that changes

economics. For some problems the grain is a unit, a stop, or a

cubic meter; for rotables it may be a speciﬁc serial number whose

history makes it economically unique. Thinking in marginals keeps

the engine aligned with the rate–of–return calculus: the question

is never “what is the right stock level?” but “is one more unit, here

and now, worth more than its next best use?”

Constraints are feasibility tests that decide which options may

be exercised. They should be written as code that inspects a

candidate move and either admits it or rejects it with a reason.

In supply-chain reality, very few limits are truly absolute: almost

any bottleneck can be relaxed for a price or a delay. It is therefore

prudent to move many “constraints” into the objective as priced

penalties, keeping only the handful that reﬂect physical impossi-

bilities or legal prohibitions. This makes unlike frictions—dock

congestion, crew overtime, shelf resets—comparable on the same

coin ledger and lets the engine ﬁnd the least-bad compromise under

uncertainty.

The objective is a money

denominated valuation consistent

with the ﬁrm’s goal: maximize expected, risk

adjusted rate of

return. It is not a percentage target masquerading as economics.

A useful objective must be explicit about the prices it assumes:

stockout pain, obsolescence, capital charge per unit

time, late fees,

goodwill loss, option value of waiting. Hidden prices breed politics;

open prices breed performance.

Forecasts enter as ﬁrst

class inputs. Because outcomes hinge on

futures that are partly unknowable, the engine does not score plans

against a single guessed trajectory but against distributions—over

demand, lead times, returns, reliabilities, and sometimes prices.

The objective remains a deterministic function of its inputs; the

randomness lives in the forecast objects that feed it. This separa-

tion keeps causality legible and the blame assignable when a bet

underperforms: was the frame narrow, the valuation misguided,

or the forecast miscalibrated? The heavy probabilistic machinery

is developed in the next section; here it suﬃces to insist that a

8.3. DECIDING IS OPTIMIZING 329

decision engine must be built from the start to ingest distributions,

not points.

Finally, a word on expression. Mid-century doctrine tried to

separate statement from resolution—one “declarative” ﬁle for the

problem, a black-box solver for the rest

. That separation ﬂatters

elegance; it handicaps practice. A decision engine that runs every

day beneﬁts when its model helps the method: variable orderings

that reﬂect economics; warm starts from yesterday’s solution;

decompositions aligned with geography or lifecycles; caches that

remember expensive subcomputations; feasibility tests that fail

fast. This is not impurity; it is mechanical sympathy between

problem and machine.

8.3.2 On optimality

Within a ﬁxed frame—ﬁnite resources, explicit admissible moves,

stated prices—there is a best allocation. The point matters: “good

enough” is not an abdication of rigor but a recognition that eco-

nomics, not algebra, decides when a search should stop.

Two lessons follow. First, supply-chain problems exhibit di-

minishing returns along most levers. Extra inventory improves

service until it no longer does; extra expedites tame lateness until

they no longer do; extra assortment widens reach until it clogs

attention. This curvature is good news: it turns our discrete search

into something that admits pseudo

gradients. Greedy steps, lo-

cal improvements, neighborhood exchanges, and short look

ahead

policies—when guided by proper prices and fed with probabilistic

inputs—climb quickly toward high ground. These problems are not

cryptographic; ﬂipping one bit does not turn feasible into impossi-

ble and collapse value to zero.

. Algorithms that rely exclusively

This separation was canonized by modeling languages such as AMPL: A

Modeling Language for Mathematical Programming (1993), Fourer et al. The

model/solve split is pedagogically clean and excellent for benchmarking; in day-

to-day supply-chain optimization it often impedes the “mechanical sympathy”

that practice demands—warm starts from yesterday’s plan, domain-aware de-

compositions, cached subcomputations, and explicit economic instrumentation.

This contrast is not accidental but the product of selection. Industrial

330 CHAPTER 8. DECISIONS

on splitting abstract spaces and tightening linear relaxations tend

to grind on the wrong geometry; the constraints of business are

rarely tight enough, early enough, for half-space pruning to pay.

Second, “the optimum” is conditional. Change the frame and

the summit moves. Add a liquidation channel; allow price nudges

tied to sailing dates; widen what may co

load; legalize late binding

between near

substitutes: yesterday’s paragon becomes today’s

local hill. This is not a failure of optimization; it is its purpose.

In Kuhn’s terms, routine progress happens within a paradigm;

step changes happen by changing the paradigm.

A living deci-

sion engine must therefore be built for experimental optimization:

dual

run challengers against incumbents, promote only when mea-

sured deltas justify it, revert cheaply when they do not. The

obsession with certiﬁcates of global optimality in toy worlds dies

here; the currency is uplift measured on the ﬁrm’s books.

8.3.3 The solver within

A solver is the software component that, given a frame, forecasts,

and prices, searches the admissible space and proposes commit-

ments. It is the heart of the engine but not the engine itself. Around

it sit the indispensable organs: semantic adapters to read author-

itative records; probabilistic modules to produce distributions;

valuation ledgers to expose prices and owners; halting heuristics

supply chains are continuously pruned by market pressure: conﬁgurations that

require brittle, all-or-nothing “perfect plans” fail disproportionately and are

retired; what remains is a population of routines, contracts, and physical layouts

that tolerate noise, delays, and partial failure. Robustness is seldom the result

of explicit design alone; it accumulates through survivorship. As a consequence,

most operational choices admit neighborhoods of near-equivalent moves that

deliver comparable returns, which is why approximate, greedy search guided

by correct prices often outperforms the pursuit of exact global optima in toy

models.

Thomas S. Kuhn, The Structure of Scientiﬁc Revolutions (1962). In business

settings, paradigm shifts are typically small and frequent: a new admissible

move, a repriced penalty, a diﬀerent cadence. Their eﬀect on the “best” plan is

nevertheless incommensurable with what came before.

8.3. DECIDING IS OPTIMIZING 331

to suspend emission when conﬁdence drops; and the cut

over ma-

chinery that promotes proposals to facts and re

ingests them on

the next cycle.

Because the engine runs under uncertainty and repeats, several

engineering stances prove decisive.

First, stochastic control beats deterministic planning. The solver

should rank admissible moves by their expected, risk-adjusted rate

of return under the supplied probability distributions. Sequential

dependence—today’s move shaping tomorrow’s options—can be

handled by optimizing policies rather than single acts, typically

within a receding

horizon loop in which each cycle recomputes with

fresher information. Functional forecasts—simulators that map

“what we would do if. . . ” to outcome distributions—are the natural

partner for such policy search. The mathematics is straightforward;

the novelty is to make it small, legible, and auditable enough to

run every day.

Second, penalties tame unenforceable limits. In the stochastic

world, “never exceed X” is either impossible or ruinous. Pricing

violations brings limits under one roof and aligns them with the

money objective. Chance-constraint language may be useful for

discourse; prices do the work. We return to unenforceable lim-

its—and to the relation between chance constraints and priced

penalties—in what follows.

Third, design for repeat resolution. Most supply-chain choices

are not one-oﬀ industrial design problems (e.g., siting warehouses or

ﬁxing lanes) but small, stochastic allocations made again tomorrow

under slightly diﬀerent facts. A solver that prospers here must

ingest distributions, price options in coins, and be cheap to halt

and rerun. The output is not a monolithic plan with a certiﬁcate;

it is a steady stream of better wagers under a common ledger. This

stance runs against the dominant OR posture—one-shot global

models with static constraints—which is why only a handful of

ﬁrms use solvers day-to-day in operations.

Fourth, instrument for trust. Every emitted decision must come

with a dossier: the admissible options considered, the prices and

distributions used, the marginal deltas that justiﬁed the choice,

332 CHAPTER 8. DECISIONS

and the tests passed on the way out. Dual

run should be the

default: the incumbent recipe holds the pen while a challenger

writes shadow decisions; measured diﬀerences accumulate in coins.

When something smells wrong—tails miscalibrated, a constraint

starting to bind—halt. Flooding users with “exceptions” deputizes

them as unpaid coprocessors and teaches them to distrust the

system. A clean stop invites the only interventions that help: edits

to the frame, the prices, or the semantics.

Finally, a sober word about the toolbox sold under the OR

label. After seven decades of research and two more of ever

faster

hardware, generic linear

and mixed

integer solvers remain marginal

in day

day supply

chain operations. They show up for one

oﬀ

design work—network siting, long

horizon capacity studies—and

for a few tightly bounded niches. They almost never govern the

recurrent, uncertainty

laden choices that move coins every day.

This is not managerial stubbornness; it is a mismatch between

instrument and phenomenon.

Classical OR technology presumes a world that supply chains

do not inhabit. Uncertainty is compressed into point surrogates

or thin

tailed “safety” terms; objectives are written in artifact

units—service levels, accuracy percentages, utilization—rather

than in money; and a monolithic plan is produced as if execution

were a one

shot plan rather than a stochastic control loop that

repeats at operational cadence. The modeling creed that separates

“pure statement” from “impure method” then forbids the very

hints that practice requires: priced penalties for unenforceable

limits, explicit shadow prices on shared resources, warm starts

from yesterday’s commitments, and greedy

accumulation rules

steered by windows of responsibility. Faced with fat tails, ratchets,

and option values, the global MILP either times out, optimizes

the wrong thing, or yields a brittle schedule that collapses at ﬁrst

contact with reality.

The empirical resolution is simple and unglamorous. Treat

solvers as subroutines inside a larger, economics

aware, stochastic

search rather than as sovereigns over the decision. Let probabilis-

tic forecasts supply the futures; let the objective speak only in

8.3. DECIDING IS OPTIMIZING 333

coins; move most “hard” limits into priced penalties; recompute

marginal commitments at the cadence reality allows; halt when

conﬁdence drops; run challengers against incumbents in parallel;

and embed domain structure wherever it buys return. Classical

primitives—assignment, min

cost ﬂow, knapsack, routing—remain

invaluable, but only when they are fed proper prices and glued

into a loop that respects uncertainty, repetition, and ratchets.

Seen this way, the verdict on OR in operations is not that

the mathematics is wrong, but that the posture is misaligned.

Tools built for stationary toys and one

shot plans cannot manage

the combinatorics of options under fat

tailed uncertainty where

proﬁt lives in the tails. Compute should therefore be spent where

it purchases coins: on better frames, better prices, and better

probabilistic views, not on certiﬁcates of optimality for the wrong

question. The next section puts a price on compute itself.

8.3.4 The economics of compute

Optimization has a cost. Minutes of CPU, hours of wall

clock,

days of engineering—each must be justiﬁed the same way any other

resource is: by its expected, risk

adjusted return. The relevant

horizon is operational. Rescheduling pick robots invites millisecond

cadences; overseas buying does not. Vendors peddle “real

time” as

if time itself were a virtue; it is not. The right cadence is the one

at which an extra unit of compute no longer repays itself in coins.

Two practical consequences follow. First, accept that some deci-

sion cycles are “slow by design”. Large, stochastic, high

dimensional

searches cannot be driven to hundred

millisecond latencies without

either gutting the frame or trivializing the objective. Amdahl’s

law

bites as soon as the engine must touch broad contexts to

avoid local stupidity. Second, engineer graceful degradation. Run

a quick pass that captures 80 % of the value with cheap heuristics

Gene Amdahl, 1967: the speedup of a system is bounded by the fraction

that cannot be parallelized. In decision engines the non

parallelizable parts

are often I/O, semantic checks, or global reconciliations that keep the solution

coherent.

334 CHAPTER 8. DECISIONS

and good prices; spend the remaining budget where shadow prices

scream for attention; stop when expected uplift per minute falls

below the minute’s cost. Because the engine repeats, tomorrow’s

run will harvest today’s leftovers with fresher information and a

warmer start.

The same calculus governs model size. Bigger instances are not

“harder” in any metaphysical sense; they are only more expensive

with a ﬁxed technique. If a method blows past a sensible budget,

change the method, coarsen the grain where economics permits, or

shrink the horizon. Measured uplift, not aesthetic purity, decides.

In sum, optimization is the language through which a decision

engine expresses preferences under uncertainty. When reframed

around marginals, prices, and admissible moves; instrumented for

repetition, halting, and audit; and engineered with sympathy for

both hardware and economics, it becomes what the automation

program needs: not an oracle that dictates a plan, but a tireless

clerk of the ﬁrm’s intentions, forever proposing the next

best wager

and writing it—legibly—into the ledger.

8.4 Deciding under uncertainty

Decisions are priced wagers on futures we do not control. Earlier

in this chapter, we adopted the stance that makes such wagers

computable: the objective is deterministic and money

denominated,

while uncertainty enters through probabilistic forecasts—distributions

for the facts that matter (demand, lead times, reliabilities, returns,

sometimes prices). We do not bury randomness inside the objec-

tive; we feed the engine distributions and ask it to rank admissible

commitments by their expected, risk-adjusted rate of return.

This posture departs from the teleological vision (Chapter The

Future), which treats uncertainty as a defect to be engineered

away by better point forecasts. Even small errors in such point

estimates can have discontinuous consequences: a turbine grounded

for one missing vane is worth far less than “99.98 % of a turbine,”

and a rival that is 1 % cheaper can erase 100 % of your sales.

8.4. DECIDING UNDER UNCERTAINTY 335

In supply chains, proﬁt and loss live in the tails. Methods that

optimize against a single guessed trajectory therefore manufacture

conﬁdence in place of robustness.

Nothing here hinges on vocabulary. Calling the problem “stochas-

tic optimization” simply acknowledges that futures are represented

by distributions and that valuation is taken in expectation (and,

when warranted, with explicit risk adjustments). What matters

in practice is where uncertainty is carried and how it is priced.

As argued above, the right place is in the forecasts that enter the

objective, not in the objective itself; and the right prices are the

ﬁrm’s own coin

denominated penalties and rewards—stockout pain,

obsolescence, capital charge per unit time, congestion, late fees,

the option value of waiting.

One might be tempted to conclude that once expectations are

taken, the “deterministic” and the “stochastic” cases collapse to the

same mathematics. They do not, in practice. Deterministic recipes

encourage brittle plans, ignore fat tails, and hide trade

oﬀs behind

artifact KPIs; an engine designed for distributions must, instead,

price tails, expose valuations, halt when conﬁdence drops, and

repeat at the cadence reality allows. This diﬀerence of posture—not

a change of symbols—drives results.

With this foundation in place, the remainder of the section

turns to operational consequences of deciding under uncertainty.

First, we examine how point

based methods systematically pro-

duce fragile decisions—choices that look lean on paper and then

hemorrhage coins when the world wanders oﬀ the median. We then

treat unenforceable constraints, which must be handled by prices

or explicitly bounded risks rather than by edicts; we put a price on

learning via the exploration–exploitation trade

oﬀ; and we justify

funding options that are unproﬁtable today but hedge tomorrow’s

regimes. Each topic applies the same grammar: admissible moves,

probabilistic views, and a single money ledger.

336 CHAPTER 8. DECISIONS

8.4.1 Fragile decisions

In the present book, a decision is fragile when a small deviation

from the assumed “typical case” produces a disproportionate loss

in its risk

adjusted rate of return. Operationally, fragility appears

as both a higher variance of realized returns and—once fat tails and

ratchets are correctly priced—a lower expectation than competing

moves that keep options open. Fragility is not about cataclysms;

it is about the humdrum surprises that visit every ﬂow: a supplier

that ships short, a truck that misses a slot, a store that closes for

a day, a post that goes viral and empties a shelf. Each event is

surprising locally; taken in aggregate, they are not.

Fragility is largely manufactured by policy. The teleological

posture—one ordained future, a plan to enact it, and performance

indicators that police compliance—breeds decisions that look won-

derfully lean on paper and hemorrhage coins when the world takes

even a modest detour. Point forecasts compress futures to a single

trajectory; “hard” limits are treated as edicts; utilization is pushed

toward saturation; buﬀers are justiﬁed by formulas whose hidden

prices no one owns. Such procedures do not merely face variability;

they amplify its costs by removing the very options that variability

makes valuable. It is therefore more precise to speak of fragile

policies—rules that systematically produce fragile decisions.

Two structural features make these policies brittle. First, most

supply

chain payoﬀs are lopsided: the downside lives in the tails

while the upside is bounded. One missing component can ground an

aircraft worth millions; being 1 % dearer than a rival can wipe out

the sale entirely. Thin

tailed surrogates and median

case planning

hide these cliﬀs and thus underprice the option to avoid them.

Second, operational ratchets erase alternatives. A premature store

allocation that looked harmless under a baseline forecast prevents

serving a better request tomorrow; a “full

truck” routine that ships

on the calendar locks tomorrow’s consolidation out. Decisions

that maximize paper “eﬃciency” by binding early are, in practice,

decisions that sell options cheaply and buy trouble dearly.

The optics are treacherous. Fragile decisions look eﬃcient:

8.4. DECIDING UNDER UNCERTAINTY 337

trucks depart visibly full, distribution centers look empty, head-

count looks thin, and inventory sits very close to the edge. Non

fragile

decisions look, at a glance, a bit “wasteful”: a sliver of capacity is

kept available, a pool of stock is held back at the distribution center,

a second supplier is retained, a few repairables are prepositioned.

Yet once tails and ratchets are priced in money, the apparently con-

servative stance delivers a higher expected, risk

adjusted aperiodic

rate of return. The premium that buys slack is an option premium;

it repays itself whenever the world misses the median—which it

reliably does.

A mundane illustration clariﬁes the point. Consider line main-

tenance for a jetliner on a tight turn. The work scope is uncertain

until the ﬁrst panels open; one missing vane can strand the aircraft

and cascade missed connections. A deterministic routine that

provisions a kit sized to the “average job” will look lean ex ante

and fail expensively whenever demand spikes on a few parts. A

rugged routine begins with distributions for the uncertain work

scope and for repair–return delays, attaches a coin

denominated

penalty to schedule slip, and prices each extra part as an option

against that penalty. The resulting kit is not “minimal”; it is prof-

itable. Unused parts go back to stock; their carrying charge is small

next to the loss avoided when tails bite. The same logic governs

any high

penalty, low

probability shortage: fat tails dominate and

thin-tailed arithmetic manufactures fragility.

A second illustration lies inbound. Suppose a warehouse faces

random supplier drift and a ﬁnite number of receiving doors. A

weekly, plan

compliant ordering calendar will occasionally stack

sailings and overload the dock; the remedy becomes ﬁreﬁght-

ing—deferrals, driver detention and demurrage

, overtime crews,

and oﬀ-contract priority premiums for scarce dock slots.

A rugged stance treats arrivals and dock times as distribu-

tions, prices overload through explicit congestion penalties (the

“Detention” compensates a carrier when a truck waits beyond free time at

loading/unloading; “demurrage” is the terminal or storage fee charged when a

container overstays its free time.

338 CHAPTER 8. DECISIONS

same economics that surface as detention, demurrage, overtime,

or priority

slot premiums), and lets the decision engine defer or

advance orders until the expected, risk

adjusted return of the next

batch clears the ﬁrm’s shadow cost of capital plus the option value

of waiting. The cadence becomes an output of economics, not an

input from the calendar; the dock is not “always busy,” but coins

stop leaking.

Downstream, premature allocation is another quiet source of

fragility. Shipping scarce units early to weak stores freezes them

where they rotate slowly and prevents serving the winners that

emerge a few days later. A rugged routine attaches a visible shadow

price to distribution center stock under pressure—coins per unit

per day that represent the option to serve a better request tomor-

row—and releases a unit to a store only when that store’s marginal

return overtakes the distribution center’s hold price. Throughput

usually increases where it matters most, even though more units

sit at the distribution center for a little longer. The optics of

“busyness” improve under fragile policies; the economics improve

under robust ones.

Fragility also imposes a labor cost that escapes dashboards.

Fragile policies collide with reality, generating long exception

queues and conscripting planners as human coprocessors. The

organization normalizes chronic expediting, manual resequencing,

and after-the-fact overrides. This ﬁreﬁghting absorbs the attention

needed to build the capital good at hand: a small, legible decision

engine that encodes frames, prices, and probabilistic views, runs

unattended, and compounds improvements. Fragile policies not

only lose coins on bad days; they also keep the ﬁrm from investing

the time that would make bad days rarer and cheaper.

Engineering non-fragile decisions does not require heroics; it

requires a change in posture. The rugged view treats uncertainty

as the raw material of proﬁt. Practically, this means (i) admitting

distributions—over demand, lead times, reliabilities, returns, and

sometimes prices—as ﬁrst-class inputs; (ii) expressing all frictions

in coins—stockout pain, congestion, aging, late fees, the option

value of waiting—and owning those prices company-wide; and (iii)

8.4. DECIDING UNDER UNCERTAINTY 339

letting a stochastic optimizer rank marginal commitments by their

expected, risk-adjusted aperiodic rate of return. Small, priced slack

follows naturally: a bit of pooled inventory at the distribution

center, a few retained alternatives in transport, measured headroom

at critical resources, modest over-provisioning of kits where tail

penalties are sharp. These are not talismans against bad luck; they

are options purchased because the expected payout exceeds the

premium.

A simple test exposes fragility in practice. If a recommen-

dation collapses under small, realistic perturbations of inputs;

relies on calendar rituals rather than priced cadences; expresses

“excellence” in artifacts—accuracy percentages, service targets,

utilization scores—rather than in coins; and demands constant

human “exceptions” to remain tolerable, it is fragile. Conversely,

if a routine can articulate its frame, show its distributions, name

its prices, defend its cutoﬀ for action versus waiting, and produce

dossiers that survive contact with realized outcomes, it is likely to

be robust—even if, to the untrained eye, it looks a little “wasteful”.

Once uncertainty is admitted, one last ingredient becomes

unavoidable: many limits cannot be guaranteed under all draws

of the dice. Dock slots, driver rosters, pick faces, and battery

charge cycles will occasionally be stressed by unlucky coincidences

even when decisions are sensible. The right treatment is not to

deny randomness but to price it. The next section turns to these

unenforceable constraints and shows how to handle them with

chance bounds—or better, with explicit penalties stated in the

same coin that governs every other trade-oﬀ.

8.4.2 Unenforceable constraints

In a stochastic world, most “constraints” are not laws of physics

but preferences backed by penalties. Treating them as edicts

that must hold under every draw of the dice produces brittle

plans and expensive ﬁreﬁghting. The frame introduced earlier

already suggests the right split. A few limits are truly hard—legal

prohibitions, physical impossibilities, safety barriers—and belong

340 CHAPTER 8. DECISIONS

in feasibility tests that reject candidate moves outright. Everything

else is soft: it can be violated for a price or delay and therefore

belongs in the objective as an explicit, coin-denominated nuisance.

Let’s consider, for example, a company passing replenishment

orders for a warehouse. The warehouse has a maximum inbound

capacity. If too many trucks arrive on the same day, the staﬀ must

turn some away to return later. This is costly and disruptive, tying

up trucks planned for later deliveries. Thus, replenishment orders

are intentionally spread out to avoid excessive inbound shipments

on any given day. However, suppliers may face diﬃculties. Unfore-

seen delays may, infrequently, cause deliveries—initially planned

for distinct dates—to collide on the same day. Because supplier

delays are random—and often fat-tailed—such clashes can never

be ruled out. This would require impractically long delays between

successive orders. Spacing orders mitigates the risk of exceeding

inbound capacity, but it cannot eliminate it.

More broadly, most supply-chain constraints carry a real risk of

violation even when handled carefully. By deﬁnition, the company

has no control over its customers or its suppliers. Nor does it have

complete control over its own employees, who may fail to show up

for various reasons. Yet, while perfection is not of this world, the

company can steer its decision-making to respect those constraints.

Consider two approaches.

Chance constraints oﬀer a less stringent criterion. Instead of

insisting a constraint be met for all outcomes, a chance constraint

requires it to hold with a minimum probability. One pragmatic

approach is to allow a small, explicitly quantiﬁed risk of breaking

a rule—for example, keeping the chance of dock overload below

5%. This keeps violations rare and controlled.

The main weakness of chance constraints, however, is that they

are fundamentally noneconomic—or rather, that the economic

trade-oﬀ is hidden inside an arbitrary percentage deemed accept-

able—a dimensionless number. While one can tune this percentage

to reﬂect the economics of the situation, doing so creates a new

problem to solve.

Penalized constraints are a simpler alternative: move the

8.4. DECIDING UNDER UNCERTAINTY 341

constraints into the objective through penalties. They treat every

breach as a costly nuisance and let the optimizer weigh that penalty

against the decision’s expected beneﬁt. Each violation incurs

a large cost. The optimization then minimizes expected total

cost, naturally encouraging feasible or near-feasible solutions. A

suﬃciently high penalty deters violations and steers the optimizer

toward nearly feasible solutions.

Penalized constraints suit supply-chain optimization because

they express operational limits in the same monetary terms that

govern the model. Almost any bottleneck can, in principle, be

relaxed for a price, and the penalty term captures that price

explicitly. The optimizer therefore searches for plans that avoid

ruinous outlays without being boxed in by rigid rules. Crucially,

because each penalty is stated in currency, dozens of heterogeneous

limits sum seamlessly into a single objective, allowing the model

to scale gracefully in high-complexity environments.

8.4.3 Exploration vs. exploitation

Decisions repeat. Each cycle updates our beliefs, and those beliefs

steer the next cycle. Historical data are therefore not neutral; they

are the by

product of yesterday’s choices. If a retailer has never

priced a SKU above 9.99, the ledger contains no evidence about

demand at 10.99. A solver that only “exploits”—always choosing

today what looks best under the current model—quietly freezes the

model’s blind spots. Proﬁts then plateau not because the world

is exhausted but because the ﬁrm no longer learns where returns

might be higher.

The rugged stance (Chapter The Future) treats this endogeneity

as a design constraint. Occasionally, we must pick a move that

is slightly inferior on today’s ledger because the observation it

generates will improve tomorrow’s ledger by more than the foregone

coins. In economic terms, the ﬁrm buys a small option on knowledge.

The practical question is not philosophical—whether to “try new

things”—but quantitative: when does the expected value of the

information exceed the opportunity cost of the experiment?

342 CHAPTER 8. DECISIONS

Two kinds of uncertainty matter. Aleatory uncertainty is in-

trinsic randomness; no amount of eﬀort will erase it. Epistemic

uncertainty is ignorance; it can be reduced by gathering the right

observations (cf. Chapter The Future). Exploration is the delib-

erate reduction of epistemic uncertainty when its expected divi-

dend—better decisions over the relevant horizon—outweighs the

immediate shortfall of the probe.

A small, concrete illustration makes the endogeneity point plain.

Consider a sports

drink line sold across one hundred stores. The

ﬁrm has long held an informal doctrine that “ten-coin” is the sweet

spot for impulse purchases; as a result, the price database is rich

around 9.99 and almost empty above 10.49. The demand model

inherits this bias and, when asked, advises once more to stay near

9.99. A rugged routine counteracts this inertia without gambling

the brand. It designates a tiny test cell—say, eight stores with

typical footfall—for two weeks, raises the price to 10.49 there, and

estimates the local price response. The cost is the estimated

margin lost (or customers deterred) against the status quo price

during the test period; the dividend is the uplift the new response

unlocks when rolled to the other ninety

two stores. If the expected

dividend, stated in coins, exceeds the experiment’s cost, the price

probe is rational—even if the eight stores underperform during the

test.

Exploration is not conﬁned to pricing. A buyer facing a new

supplier with uncertain reliability can place a handful of deliber-

ately small, early orders to learn the tail of the lead

time distri-

bution before committing volume. A network planner can try a

modest cross-dock scheme in one region to measure congestion

penalties rather than assuming them. A merchandiser can let a

few low

sell

through items stock out on purpose (on a safe scale) to

learn true substitution rates instead of extrapolating from censored

data. In each case, the engine prices the probe like any other

move: admissible options are stated, futures are probabilistic, and

all eﬀects—shortage pain, aging, congestion, capital charge per

unit-time—are expressed on the same coin ledger.

This trade

oﬀ is known in the literature as “exploration vs.

8.4. DECIDING UNDER UNCERTAINTY 343

exploitation”.

The classical bandit imagery is useful, provided

we keep the economics in view. The goal is not to chase abstract

“regret bounds” but to decide, at operational cadence, whether a

marginal probe clears the same bar that governs every commitment:

expected, risk-adjusted aperiodic rate of return. Three design

habits keep the process legible and auditable:

First, keep probes small and bounded. The window of respon-

sibility limits attribution; scope (number of stores, lanes, SKUs)

limits downside. Smallness is not timidity; it is how we price and

compare learning options cleanly against their dividends.

Second, make the learning dividend explicit: the expected

uplift—over the window during which the revised rule will be in

force—net of aging, markdowns, congestion, and capital charge.

Hidden “beneﬁts” count as zero; only coins on the ledger count.

Third, embed exploration within the same unattended engine.

A probe is not a special project; it is another admissible move

with a dossier: the prior, the proposed perturbation, the window,

the prices, and the rule for promotion or rollback once outcomes

arrive. If the engine’s conﬁdence drops, halting heuristics suspend

the probe exactly as they suspend ordinary emissions.

This stance also clariﬁes why the teleological view cannot

accommodate exploration. Under forecast

plan

comply, decisions

are only the last line of a deterministic program; forecasts live

apart from actions; and “accuracy” is policed as a percentage

detached from coins. In such a world, there is no budget for buying

information because the procedure presumes it already knows what

matters. In the rugged world, by contrast, quick, priced probes

are a source of edge: they narrow epistemic uncertainty where it

pays and let the ﬁrm move ﬁrst where a small insight compounds.

We now turn from buying information to buying insurance.

Exploration funds observations that change the model; nurturing

options fund capabilities that are unproﬁtable today but would

For an example of an algorithmic strategy leveraging the exploration vs.

exploitation trade

oﬀ from an economic perspective, see “Multi

armed Bandit

Algorithms and Empirical Evaluation” (2005) by Joannes Vermorel and Mehryar

Mohri.

344 CHAPTER 8. DECISIONS

pay—handsomely—under speciﬁc regimes. The grammar remains

the same: admissible moves, probabilistic futures, one coin ledger.

8.4.4 Nurturing options

Resilience means funding options that are unproﬁtable today yet

could preserve future proﬁts if conditions shift. Indeed, whether

a given range of options—oﬀered by a supplier, a carrier, or an

assembly line—is valuable to the business depends on prevailing

market conditions. If those conditions change, certain options may

sharply appreciate or depreciate. The company can hedge such

risks by nurturing the right options.

For example, a company based in Westonia may presently

source a part from a supplier in Ruritania. Local alternatives in

Westonia are, unfortunately, not competitive with the Ruritanian

supplier. Moreover, their parts do not fully meet the company’s

speciﬁcations. The company is fully satisﬁed with its Ruritanian

supplier. Yet Westonia’s government may impose a massive tariﬀ

on imports from Ruritania. Indeed, several rising political ﬁgures

have been outspoken in favor of such tariﬀs. If enacted, such tariﬀs

would severely reduce proﬁts, and the ﬁrm could not switch to

local suppliers because their parts fail on speciﬁcations. Thus, to

insure against this risk, the company selects a local supplier and

helps upgrade its production so the parts meet speciﬁcations. Most

orders still go to the Ruritanian supplier, yet the ﬁrm nurtures

the local alternative, which may prove a proﬁt-saver if the tariﬀ is

imposed.

Nurturing options take many forms. The company might fund

unused capacity—manpower, storage, production, or transport.

The company might pay high service fees in exchange for favorable

termination clauses (to surrender a lease of a vehicle, a building,

or a piece of industrial equipment). The company might lower

assembly-line eﬃciency to increase reconﬁgurability, broadening

the range of designs the line can output.

The point of nurturing options is to prepare the company

for conditions that are unlikely yet far from impossible. Unlike

8.5. SEQUENTIAL DECISIONS 345

the exploration–exploitation trade-oﬀ discussed previously, the

investment’s goal is not discovery. From the outset, the option is

understood to be nonviable so long as present market conditions

persist. The investment is a hedge.

Assessing viability requires quantifying three elements: (1)

the probability of the trigger, (2) the savings the option would

yield under those conditions, and (3) the cost of nurturing it. If

probability-weighted savings exceed nurturing costs, hedging is

proﬁtable.

Again, nurturing options make sense only under the rugged

view. Under the teleological view, the future is treated as a known

quantity. As a result, options not immediately economically viable

are dismissed. Indeed, without a supporting rationale, management

struggles to justify “unnecessary” expenses—especially those tied

to highly pessimistic conditions that would invalidate the current

course favored by the management team.

8.5 Sequential decisions

Many supply-chain choices are sequential: the payoﬀ of a move

taken today depends on moves the ﬁrm will choose tomorrow.

Present commitments erase alternatives and reshape next cycle’s

option set—a ratchet eﬀect noted in “Asymmetry of time” (Chapter

The Future). The apparent paradox—“how can I optimize now

when tomorrow’s decision is undecided?”—is routine: overseas

buying that must ﬁll a container, store dispatches that preempt

later allocations, production batches that tie up capacity until the

next changeover.

In this chapter, we treat such situations not as isolated one-oﬀ

optimizations but as rules for acting over time. The clean formal

object is a policy.

Deﬁnition (Policy).

An algorithm that maps states to admissible actions—and

346 CHAPTER 8. DECISIONS

their timing—under stated prices and probabilistic forecasts, to

issue ﬂow commitments that maximize expected risk-adjusted

rate of return.

Reifying a policy does two things. First, it brings the frame

into view: admissible moves and feasibility tests; the money-

denominated prices (penalties and rewards) the ﬁrm is willing

to defend; and the probabilistic view of futures that informs each

choice. Second, it enables closed

loop evaluation via a functional

forecast—a simulator that answers, “if we follow this policy, what

distribution of outcomes should we expect?” This pair—policy plus

functional forecast—lets us compare candidate rule

sets by the

same yardstick used for one

shot decisions: expected, risk

adjusted

rate of return.

That said, turning every sequential problem into a full policy

search rarely justiﬁes the opacity and compute it entails. The space

of possible rules is vast; scoring them only at the level of whole

trajectories obscures marginal attributions; and naïve look

ahead

becomes too expensive for day

day operations. In practice we

keep the marginalist stance—rank small, auditable commitments

by coins per unit time—while using a few disciplined instruments

that capture the sequential coupling without reifying a full policy.

A running example ﬁxes ideas. Suppose a buyer must raise an

overseas purchase order that is economical only when rounded to

a full container. The mix can combine many SKUs, but every unit

loaded today must cover demand until the next container from the

same supplier arrives. Yet that arrival date depends on a decision

the ﬁrm has not yet made. A literal, single

shot optimization

cannot value today’s order without assuming tomorrow’s. A policy

resolves the dependency (“order when the shadow price of the next

container exceeds

; allocate units by marginal return subject to

weight/volume constraints”), and a functional forecast projects the

distributions of lead times, demands, and dock capacities under

that rule.

The remainder of this section shows how to handle most of

this coupling with lighter means: a window of responsibility that

8.5. SEQUENTIAL DECISIONS 347

bounds the horizon over which today’s order is held to account

(next subsection); the economics of waiting, which prices the option

to defer and sets a cutoﬀ rate of return for action; permutation

invariance, which justiﬁes greedy accumulation of batched allo-

cations—with caveats where order truly matters (vehicle routing,

job-shop scheduling); and the postponement principle, which favors

late binding to keep future options open. Only when latency or

scale demands it do we perform an explicit, parameterized policy

search.

8.5.1 Window of responsibility

Sequential decisions create an attribution problem: today’s move

reshapes tomorrow’s options, and tomorrow’s move, in turn, will

repair or amplify today’s consequences. An engine that must

run every day needs a rule that assigns consequences to each

commitment without simulating the entire future. The practical

device is a window of responsibility: a bounded horizon over which

the present decision is held to account, while later decisions inherit

the rest.

The window is not a planning bucket; it is an accounting prin-

ciple. It answers two questions: from when do we start attributing

outcomes to today’s commitment, and until when does this respon-

sibility last before it is handed over to the next decision of the

same class?

For an overseas purchase order that must ﬁll a container, the

natural start is not the calendar day the order is entered, but

the day the goods become usable—after production and trans-

port—because only then can the decision begin to earn or lose

coins. The end is the ﬁrst later moment after which a subsequent

order can reasonably take over responsibility. In practice, a robust

convention is to end the window at the arrival of the next–next

container from the same source. The immediate “next” container

is still partly correcting or amplifying today’s choice; after the

“next–next”, responsibility has genuinely migrated. By contrast,

for daily store replenishment, the window typically runs from

348 CHAPTER 8. DECISIONS

tonight’s delivery to tomorrow night’s; the cadence itself supplies

the boundary.

Within the window, the decision is scored in money terms

consistent with the ﬁrm’s objective. Sales (or consumption) realized

during the window are credited; shortages that occur within the

window are charged according to an explicit, coin-denominated

penalty that reﬂects lost margin, goodwill, and any cascading

eﬀects on adjacent items. Inventory left at the end of the window

must not be swept under the rug. Two treatments are practical

and, depending on the case, equivalent in spirit. The ﬁrst is a

terminal valuation: treat the residual stock as if it were immediately

liquidated at its expected salvage value, less the expected carrying

and handling costs to reach that liquidation. The second—often

simpler and more conservative for long tails—is to charge the entire

expected stream of future holding and obsolescence costs to the

decision that created the stock. This “ratchet” treatment recognizes

that later choices cannot undo an over-large purchase without

paying for markdowns, storage, and write-oﬀs; the load rightly

stays with the originator.

Deﬁning the window’s bounds is a probabilistic exercise. Two

distributions usually suﬃce. First, a lead-time distribution places

mass on the dates when the commitment begins to matter. Second,

a distribution for the next opportunity to re

decide marks the

hand

over date. This second forecast is “impure” in the sense that

it projects our own future behavior; yet at the batching scales that

matter—container sailings, factory changeovers, weekly dispatch

slots—the cadence is largely governed by externalities (cutoﬀs,

vessel schedules, MOQs) and proves stable enough to forecast with

useful accuracy. Where cycles are rigid—nightly store dispatches,

end

shift production releases—the calendar itself supplies the

end.

Truncating the horizon obliges us to price what lies beyond

it. The stockout penalty already mentioned is one example: it

stands in for repeat purchase erosion and substitution dynamics

that we choose not to simulate across seasons. Similar shadow

valuations are sometimes needed elsewhere. A fashion brand that

8.5. SEQUENTIAL DECISIONS 349

discounts aggressively at season’s end may ﬁnd that heavy rebates

teach customers to wait; the long

run demand damage is real,

yet diﬃcult to model across future collections that are not even

designed. A small, per

unit discount malus—expressed in coins

and applied inside the window—keeps the engine honest without

turning the simulator into a multi

year saga. As always, prices

must be explicit, owned, versioned, and exposed; hidden penalties

do not become neutral—they become capricious.

A brief illustration ﬁxes ideas. Suppose an importer orders

a full container today. The evaluation window starts when the

container clears and goods are available; it ends at the arrival of

the next–next container from the same lane. All sales made in

between are credited to today’s order; all stockouts in the same

period are charged according to the shortage penalty. Residual

units at the window’s end receive a terminal valuation—or, in the

ratchet approach, the expected future holding and markdown costs

are fully charged to today’s order. The next container is evaluated

under its own window, with the same rules; responsibility does not

double

count. This convention makes the daily engine tractable

and auditable while preserving economic realism where it matters:

in the tails and in the irreversible costs that later choices cannot

erase.

The window of responsibility harmonizes with the marginalist

stance developed earlier. It lets the solver reason one step ahead

with probabilistic inputs, attribute consequences cleanly, and move

on. Its validity is empirical, not theoretical: choose the boundaries,

run dual

mode against incumbents, measure the deltas in coins,

and adjust. In practice, this device succeeds across a wide range of

sequential settings—overseas buying, warehouse inbound smooth-

ing, store dispatches—because it keeps the focus where economics

lives: on priced options and fat

tailed futures, not on speculative

multi-year narratives.

350 CHAPTER 8. DECISIONS

The economics of waiting

Every sequential problem contains a silent option: do nothing yet.

Waiting preserves options and lets information accumulate; acting

now locks in a rate of return and erases alternatives. The engine

should price delay explicitly and adopt a cut-oﬀ rule. Act only

when the expected, risk-adjusted aperiodic rate of return of the

best admissible move exceeds the shadow cost of capital plus the

option value of waiting. Below that bar, defer.

Operationally, this becomes a simple, repeatable discipline.

Recompute, daily or at a reality-driven cadence, the best next

move within the current window of responsibility; if its rate of

return clears the threshold, take it and record the commitment;

otherwise, wait. After a container has just been ordered, the very

next day’s container is usually unproﬁtable: demand has not yet

accumulated, consolidation opportunities are thin, and inbound

congestion penalties loom. As days pass, volumes aggregate, co-

loading improves, and the expected return rises; once it crosses

the bar, the order is placed. The policy is not “weekly cycles by

tradition”; it is “whenever economics justiﬁes it”.

The same logic governs downstream dispatch. Consider a

fashion DC holding a handful of units of a runaway bestseller.

Allocating one unit to a weak store today may look harmless under

a point forecast; tomorrow it becomes an expensive ratchet when

a strong store asks for it and none is left. Instead of attempting

to script the entire season, attach a shadow price to DC stock

under pressure—a coin

per

unit

per

day opportunity charge that

represents the option to serve a better request later. Release

to a store only when that store’s marginal return—computed

under the current probabilistic view of sell

through and shortage

penalty—overtakes the DC’s shadow price. Far from hurting

throughput, such patience tends to increase it where it pays most;

premature allocation often reduces realized throughput by freezing

stock where it will rotate slowly.

Waiting is not dithering; it is a priced decision like any other.

The cut-oﬀ embeds both the ﬁrm’s cost of capital and its tolerance

8.5. SEQUENTIAL DECISIONS 351

for tail risk. It also carries the halting heuristics discussed earlier:

when the engine’s conﬁdence drops (miscalibrated tails, unusual

constraint binding), emissions pause until the frame, the prices, or

the semantics are repaired. In this way, the window of responsibility

and the economics of waiting combine to turn many sequential

problems into a one-step greedy accumulation that stays aligned

with proﬁt. The next subsection, on permutation invariance, shows

why such greedy accumulation often works—and where order truly

does matter.

8.5.2 Permutation invariance

Many supply

chain allocations are enacted in batches: a container

is ﬁlled with a mix of SKUs; a line produces a run of units; a truck

leaves the DC loaded for several stores. In these settings, what

matters economically is the ﬁnal multiset of marginal commitments

in the batch and the constraints it satisﬁes (weight, cube, slots,

cut-oﬀs)—not the notional order in which the atomic commitments

were picked. When feasibility tests depend only on totals and

penalties and rewards are priced symmetrically for the items in

the batch, the objective function is—up to negligible bookkeeping

eﬀects—invariant to any permutation of the micro-allocations that

compose the batch.

This observation is powerful when combined with the instru-

ments introduced earlier. The window of responsibility bounds the

horizon over which a batch is held to account; the economics of

waiting supplies a cut

oﬀ: act only when the best admissible move

clears the shadow cost of capital plus the option value of delay.

Given both, the batch can be assembled greedily: at each step,

recompute shadow prices for the scarce resources and pick the next

admissible unit with the highest expected, risk-adjusted aperiodic

rate of return; stop when capacity binds or when the cut-oﬀ fails.

Because the batch valuation is insensitive to the permutation of

its constituents, this stepwise construction targets the set that

matters (the content of the batch), not a ceremonial sequence that

has no economic standing.

352 CHAPTER 8. DECISIONS

Two caveats clarify what is—and is not—claimed. First, while

the ﬁnal value of a batch is order

insensitive, the marginal value of

any given unit is path

dependent: early picks face laxer constraints

than late picks. This is precisely why the greedy accumulation must

recompute shadow prices after each addition; the path dependence

is handled by repricing, while the set that emerges remains the goal.

Second, permutation invariance is not a proof of global optimality;

it is the engineering rationale for a search that climbs quickly

toward high ground in problems dominated by diminishing returns

and priced penalties—exactly the geometry discussed earlier in

On optimality. The engine repeats at an operational cadence;

tomorrow’s run starts warmer and narrower, and the window

mechanism attributes consequences without simulating the entire

future.

Concrete cases make the point. When ﬁlling an overseas con-

tainer, feasibility cares about totals (weight/volume) and cut

oﬀ

dates, while value is earned by the mix that will sell within the eval-

uation window. It is sensible to add units—possibly across many

SKUs—in decreasing order of marginal return, pausing whenever

the economics of waiting says “not yet”, and resuming as demand

and co-loading opportunities accumulate. In DC

store dispatch,

the DC stock carries a shadow price that reﬂects the option to

serve a superior request tomorrow (cf. The economics of waiting).

A store receives a unit only when its marginal return overtakes

that DC shadow price; within a departure wave, units are then

added greedily until the truck’s priced penalties and capacities

advise stopping. The result is often a higher realized throughput

where it matters most. Premature allocations freeze stock in low-

yield locations, a bustle that depresses throughput once the season

reveals genuine winners.

Permutation invariance also helps keep models small and au-

ditable. Batch constraints—MOQs, full

truck incentives, dock

congestion—are better expressed as feasibility tests and priced

penalties over the set that ships than as procedural rules that

script a picking order. The former yield clear prices and clean

halts; the latter breed artifacts and exception handling. Because

8.5. SEQUENTIAL DECISIONS 353

the batch’s value does not depend on a ceremonial sequence, the

solver is free to propose the next best marginal commitment, log

its dossier, and repeat until the cut-oﬀ bites.

Order-sensitive allocations

Not all sequential problems admit permutation invariance. Some

classes are intrinsically order-sensitive: the route of a vehicle, the

sequence of jobs in a shop with changeover times and scarce skills,

the traversal of a picker through an automatic store. There, the

distance traveled, the queuing and blocking, or the changeover

losses depend directly on the ordering of tasks. Swapping two

stops in a milk

run can add an hour of driving; permuting two

steps in an MRO work

scope can lengthen turnaround time by

a day. In such cases, the sequence is economically real, and the

model must say so.

The methodology, however, remains consistent with this chap-

ter. We keep the frame explicit (admissible moves and feasibility

tests), keep the economics open (priced penalties for lateness, over-

time, missed windows, and aging), and ingest probabilistic views

of demand, travel times, repair durations, and reliabilities. The

solver’s job is then to propose a sequence, not merely a set. Win-

dows of responsibility still bound attribution—end of shift, end of

day, return

depot—and priced penalties bring unenforceable lim-

its into the objective. The principle remains: make order explicit

only where it changes money; elsewhere, treat it as an artifact and

let permutation invariance justify greedy accumulation.

Seen together, permutation invariance and the economics of

waiting encourage late binding. When the valuation of a batch

depends on its contents but not on the ceremony of their selection,

it is prudent to defer commitments until the marginal returns clear

the threshold, and then assemble the batch quickly by greedy accu-

mulation. The next section, the postponement principle, formalizes

this stance and shows how to design ﬂows so that binding choices

are taken as late as practicable, preserving options and improving

returns under uncertainty.

354 CHAPTER 8. DECISIONS

8.5.3 Postponement principle

Permutation invariance and the economics of waiting point to a

general rule for sequential work: bind late. The ﬁrm earns its

edge by deferring irreversible choices until information arrives that

materially changes the ranking of options, while charging a fair

price for the delay. Under the teleological vision (Chapter The

Future), deferral is a nuisance that complicates plan compliance.

Under the rugged vision, deferral is a priced option: it preserves

alternatives and is exercised only when its expected, risk

adjusted

return clears the relevant bar.

Postponement—also called late binding—means arranging the

ﬂow so that binding choices are made no sooner than the economics

justify. Keep goods, capacities, and commitments in generic, re-

assignable states until the expected risk-adjusted aperiodic rate of

return on binding exceeds (i) the shadow cost of capital, (ii) the

option value of waiting, and (iii) the postponement premium—the

incremental costs paid to keep options open.

The premium is tangible: carrying semiﬁnished inventory that

can be specialized later; keeping retainers with alternate suppliers;

reserving dye-house or kitting capacity; paying extra handling to

keep stock pooled at the distribution center; and investing in more

sophisticated orchestration. Postponement is not “free ﬂexibility”;

it is a bet that the value of information and optionality will more

than repay the premium and any aging risk within the window of

responsibility. Properly applied, postponement tends to shorten

the half-life of decisions (see Changing course below) by moving

commitments closer to the moment they start to pay back and by

reducing costly misallocations that would otherwise linger.

Late binding takes several recurring forms, all governed by the

same economic test.

Form postponement. A fashion importer may commit early

to greige fabric and trims (cheap, generic inputs) and defer the

expensive specializations—dye, wash, label, size mix, geographic

split—until early sell

through reveals which articles and colors

carry fat tails. The admissible options become explicit: ﬁnish

8.5. SEQUENTIAL DECISIONS 355

now and ship broadly, or wait a few weeks and ﬁnish what the

winners will actually absorb. The decision engine prices the salvage

value of the laggards, the shortage penalty on the winners, the

specialization costs, and the premium paid to keep the dye-house

on call; it then binds only when the expected aperiodic rate of

return clears the cut-oﬀ.

Allocation postponement. Shipping scarce units to stores too

early freezes them where they rotate slowly. Holding them at the

DC with a visible shadow price (coins per unit per day) preserves

the option to serve a superior request tomorrow. A store receives

a unit only when its marginal return—computed under the current

probabilistic view of sell

through and shortage penalty—overtakes

the DC’s hold price (see The economics of waiting). Throughput

typically increases where it matters most, because winners are fed

ﬁrst and laggards do not hoard.

Finish/kitting postponement. Coloring, kitting, packaging, or

regionalizing variants early commits the ﬁrm to a demand split

not yet known. Keeping WIP generic and ﬁnishing late lets orders

reveal the mix. The premium is the extra handling and the capacity

retainer at the ﬁnishing step; the beneﬁt is fewer write

oﬀs on the

wrong variants and a shorter decision half

life as stock is specialized

only when a taker exists.

Temporal postponement. On inbound lanes, a container ordered

“because it’s Tuesday” fails the economic test more often than

not. As days pass, volumes accumulate, co

loading improves, and

inbound congestion penalties evolve. Recompute daily; issue the

order on the day the best admissible batch clears the threshold

deﬁned by the shadow cost of capital, the option value of waiting,

and the postponement premium. The cadence becomes an outcome

of economics, not of the calendar.

Price postponement. Prices shape demand; postponing mark-

downs or promotions until uncertainty has narrowed can dominate

a scripted price path. Functional forecasts (Chapter The Future)

make the pricing policy explicit and let the engine compare “mark-

down now” with “wait one more week” under the same money

ledger. The premium here is the risk of being left with aged stock if

356 CHAPTER 8. DECISIONS

the window closes; the beneﬁt is extracting more from the winners

without teaching customers to wait.

Operationally, postponement is the art of unbundling commit-

ments into decisions that can be bound late. Break the monolith

(“all articles, quantities, destinations, dates decided nine months

ahead”) into staged micro-decisions that match the ﬂow’s physics.

Commit early to low-information, high-lead-time inputs; defer the

high-information, low-lead-time specializations; keep stock pooled

until assignment pays; and contract for ﬂexible volumes and late

speciﬁcations where the premium is tolerable. Managers often

“simplify” sequential problems by rigidifying them—freezing assort-

ments, banning mid

season reallocations, locking price paths—not

because physics requires it but because attended processes cannot

cope. With unattended systems of intelligence, the bottleneck

should sit in atoms and hours, not in the decision stack; late

binding then becomes not only feasible but routine.

Like any heuristic, postponement has limits of jurisdiction.

Where order is intrinsically consequential—vehicle routing, job-

shop sequencing, picker traversal—binding early is the problem,

and sequence must be optimized rather than deferred. Regulatory

pre-clearance, scarce changeover skills, or perishable goods can also

set hard fronts beyond which waiting no longer pays. The remedy

is not to abandon postponement but to push the late-binding

frontier upstream as far as economics and feasibility allow, and

to price the remainder explicitly in the objective: penalties for

lateness, aging, overtime, or missed windows.

In practice, the rule is simple and repeatable. At the cadence

dictated by reality, recompute the best admissible binding move

under the current window of responsibility. Bind only when its

expected, risk-adjusted aperiodic rate of return clears the sum of

the shadow cost of capital, the option value of waiting, and the

postponement premium. Otherwise, wait. Where late binding

cannot be engineered or where latency must be kept in the tens of

milliseconds, precomputed, parameterized rules are warranted—the

topic of the next subsection, policy search.

8.5. SEQUENTIAL DECISIONS 357

8.5.4 Policy search

The previous pages handled sequential decisions without reifying

a “policy”. A window of responsibility bounded attribution; the

economics of waiting priced inaction; permutation invariance justi-

ﬁed greedy accumulation; late binding preserved options. In many

ﬂows this is suﬃcient and superior: it keeps the grain marginal,

the prices explicit, the reasoning legible. Yet there are cases where

we must compress computation into a rule that can be applied at

millisecond cadence, or where the physics of the problem makes

order intrinsic rather than ceremonial. In those cases we do not

abandon the economic stance; we codify it into a policy and search

its space.

A policy, as deﬁned earlier, is an algorithm that maps states

to admissible actions under stated prices and probabilistic fore-

casts. Turning that deﬁnition into practice serves a single purpose:

to shift heavy computation upstream—in an oﬄine, stochastic

search—so that inference at decision time is fast and dependable.

Where latency is generous (minutes to hours), recomputing the best

marginal commitment with fresh forecasts is usually better than

carrying a policy. Where latency is tight (tens of milliseconds),

or where thousands of independent agents must act concurrently

under local information—picker ﬂeets in an automated warehouse,

dynamic slotting inside a shuttle store, order-sensitive job sequences

with scarce changeover skills—a well-designed policy becomes the

right vehicle for the economics already developed in this chapter.

Beware the lure of “stationary proofs”. The literature is rich in

analytical policies that are “optimal” only in toy worlds—stationary

demand, thin tails, additive costs, no ratchets. Our world is rugged:

distributions shift, tails dominate, and commitments erase options.

Optimality in symbols should never outrank measured uplift under

a faithful simulator and, later, in dual-run production.

When, then, is policy search warranted? Whenever order truly

changes money and cannot be recomputed fast enough; whenever

computation must be pushed oﬀ the critical path without sacri-

ﬁcing economics; whenever thousands of small agents must act

358 CHAPTER 8. DECISIONS

coherently from local views; and whenever a simple, parameterized

rule can carry the spirit of postponement, marginal valuation, and

priced constraints with orders of magnitude less latency. When

these conditions do not hold, the lighter instruments already intro-

duced—windowed attribution, the economics of waiting, permuta-

tion invariance, and late binding—remain preferable: they move

the bottleneck back to atoms, keep options open until binding

pays, and avoid encasing yesterday’s beliefs in rules that will age.

Properly placed, policy search completes the picture rather

than displacing it outright. It is how a ﬁrm converts deep, slow

computation into fast, repeated action while preserving the same

economic ground: decisions remain marginal wagers, priced in

coins, justiﬁed under probabilistic futures, and revised as reality

reveals itself. The next section turns from rules to inertia and

quantiﬁes how burdensome a commitment really is—its load and

its half-life—so that, between two equally proﬁtable moves, the

ﬁrm prefers the one that lets it change course sooner.

8.6 Changing course

In a free market, the only constant is change. Being able to

change course is a matter of survival for any company. Yet any

supply chain decision comes with a ratchet eﬀect: once resources

are allocated, they are no longer immediately available for other

uses. Some decisions appear ﬁnal, such as a manufacturing process

consuming raw materials, while others appear reversible, such as

moving stock between warehouses. In any case, from an economic

perspective, those decisions can be undone, though the required

time and money vary.

When considering decisions with equal projected rates of return,

a company should favor those with smaller underlying commit-

ments, in time or in money. Avoiding unnecessarily large commit-

ments is essential to keeping the company agile and, through agility,

to withstanding the market shocks that will inevitably come.

Under the teleological view—forecast, plan, comply—there is no

8.6. CHANGING COURSE 359

operative notion of agility at all. The future is treated as a schedule

to be enforced, and the instruments gravitate toward plan-policing

indicators: forecast accuracy, service-level percentages, utilization,

inventory turns. Such gauges say little about the ﬁrm’s ability

to change course; they reward adherence to a guessed trajectory

and remain blind to the time required for commitments to unwind.

Agility, however, is the capacity to shed or reassign commitments

quickly when new information arrives, at tolerable cost.

To discuss agility in economic terms, we must replace plan-

compliance surrogates with measures that price commitments

over time. The classical trio—lead time, working capital, and

inventory turnover—remains in view, but we recast them through

two operational quantities that make duration explicit: the load

a decision imposes (money

time) and the half-life of that load

(the time required to release half the resources committed by that

decision).

Lead time, in a broad sense, is the delay between a decision

and the renewed availability of a resource. Working capital is the

diﬀerence between current assets and current liabilities

. Inventory

turnover measures the number of times inventory is sold or used

during a year. Taken together, these indicators tell us how much

capital is tied up but largely ignore for how long; the forthcoming

notions of load and half-life address that blind spot.

All else equal, shorter lead time, lower working capital, and

higher inventory turnover increase the capacity to change course.

Indeed, these concepts characterize the company’s general inertia:

its incapacity to change course immediately, as it must ﬁrst liqui-

date the mass of assets presently ﬂowing through while preserving

their value.

However, these concepts are of limited use in understanding

the company’s inertia. This is not to say these concepts are

wrong, or even misguided; rather, the matter—the characterization

In supply chain settings, raw materials, work in progress, and ﬁnished goods

held in stock frequently dominate short- and midterm variations in working

capital. The higher the working capital, the more ﬁnancing the company requires

to operate.

360 CHAPTER 8. DECISIONS

of the company’s inertia—admits alternative concepts that are

operationally superior.

8.6.1 The load of a decision

The (ﬁnancing) load of a ﬂow decision quantiﬁes the underlying

resource commitment as the product of a resource’s value and the

lifespan of the goods it creates, with units of money × time.

Consider a simple illustration. If the company buys a batch

worth 10 coins and resells it 5 days later, the load is

= 10

5 = 50.

Alternatively, if the company were to sell half the batch 5 days later

and the other half 20 days later, the load is

5 +

20 =

125.

The second situation yields a load more than twice as large as

the ﬁrst because the company holds inventory for more than twice

as long.

Now suppose payment for the batch is deferred by 12 days.

The load of the ﬁrst situation is eﬀectively zero, as no ﬁnancing is

required: the supplier is paid after the goods are sold. The load of

the second situation is L =

× (20 − 12) = 40.

Deferred payment can substantially reduce the ﬁnancing pres-

sure that sustains the ﬂow.

Load measures the working capital tied up over the entire life

of a decision, not just at a reporting date. Whereas ordinary

working-capital ﬁgures give a snapshot, load attaches a duration

to every coin committed. For a ﬁrm under ﬁnancing pressure,

this time-weighted metric—read alongside expected return—is the

sharper guide for ranking options and steering scarce capital to its

best uses.

Many ﬁrms require managerial approval once a purchase order

passes a threshold. The rule checks size and potential liquidity

impact, yet it overlooks the goods’ lifecycle. A small order can

immobilize capital for months, while a large backorder may clear

overnight. Load captures this hidden, time-based burden directly.

Firms often plot inventory value against turnover to spot capital

traps, but load already fuses both into a single metric.

8.6. CHANGING COURSE 361

8.6.2 The half-life of a decision

The half-life of a decision is the elapsed time—typically measured in

days—until its load halves through sales, consumption, or service.

Consider an overseas order where delivery takes 10 weeks and

a full container lasts 20 weeks before half the stock is sold. Thus

the half-life is 7 (10 + 20) = 210 days.

Borrowed from physics, half-life measures the time for a deci-

sion’s load to halve; it supplies a time dimension that lead time

alone obscures. Indeed, lead time understates the consequential

duration of a decision whenever batching is involved. A similar

critique applies to cycle time and inventory turnover. Naturally,

the “half” threshold is arbitrary; any quantile could be used instead.

Revisiting the example, the 75%-life of the purchase would be 280

days, assuming constant demand.

Half-life gauges how quickly a decision can be unwound; long

half-lives trap the company on its current course and erode agility.

Moreover, half-life resists manipulation better than lead time:

gaming an indicator—making the number look good without im-

proving reality—becomes easier when the metric ignores how long

capital remains locked. This often stems from misaligned incen-

tives that reward the indicator itself rather than the outcomes it is

meant to represent. Indeed, companies seeking agility often tackle

the problem by compressing lead times. However, a frequent pitfall

is reducing lead times at the expense of half-lives.

362 CHAPTER 8. DECISIONS

Chapter 9

Engineering

Modern supply chain practice operates with at least one degree

of indirection: people engineer decision-making processes, and

those processes emit myriad routine decisions that steer the ﬂow.

The emitting artifacts are numerical recipes: programs that in-

gest authoritative records, carry explicit economics, and issue

commitments that can be enacted and audited

. In preceding

chapters—Information, Intelligence, and Decisions—we argued

that such engines must run unattended if the ﬁrm is to turn judg-

ment into a productive asset rather than an endless clerical burden.

This chapter turns to the engineering principles that let these

engines exist and compound.

The scope is narrow by design. We will not rehearse generic soft-

ware topics—UI

frameworks, authentication, CI/CD plumbing

control planes, message buses, or the wiring to and from systems

of records. There is ample literature on such auxiliary sciences.

Our concern is the junction where economic reasoning meets code:

how one frames admissible options, carries uncertainty without

ﬂattening, prices trade-oﬀs in money, and preserves auditability so

A second degree of indirection is now emerging as large language models

(LLMs) assist in writing and refactoring those recipes under the supervision of

human experts. The responsibility does not move; only the typing accelerates.

User Interface

Continuous Integration, Continuous Delivery

363

364 CHAPTER 9. ENGINEERING

a decision’s lineage remains legible months later. The tone remains

deliberately non-technical—no algebra, no code listings—so we

can be clear about the foundational choices that make a system

of intelligence emit unattended decisions safely, repeatedly, and

proﬁtably.

Two clariﬁcations anchor what follows. First, “numerical recipe”

denotes the whole apparatus that matters in practice: not only

predictors and optimizers, but also the instrumentation that re-

veals why a recommendation is sane, the halting conditions that

stop emissions when conﬁdence falls, and the dossiers that let prac-

titioners understand, in money terms, what the engine believed at

the time. Second, while the application landscape of every ﬁrm

is idiosyncratic, the engineering stance advocated here is general:

prefer explicit frames over tacit conventions, probabilistic views

over point surrogates, priced penalties over hard edicts that cannot

be enforced, and reproducibility over heroics.

LLMs warrant a brief comment. Their role is increasing and

will become critical as they compress the cost of drafting, refac-

toring, testing, and documenting numerical recipes. Yet they are

not a substitute for technical proﬁciency. LLMs do not supply

economic framing, resolve data semantics, or enforce determinism

and auditability; they merely speed the hands that perform the

work. Treated as able scribes—instrumented, reviewed, and con-

strained—they shorten the iteration loop that experimental work

requires. Treated as oracles, they invite subtle, costly errors.

Finally, a call to action: competitive advantage no longer rests

on a thicker stack of dashboards—assuming it ever did—but on

owning a small, engineering-minded capability that writes the

ﬁrm’s economics into software and lets it run unattended. The

next section explains why this capability cannot be purchased as

a packaged “planning” layer and why, for systems of intelligence,

programmability is not a taste but a necessity for safety and proﬁt.

9.1. WHY PROGRAM 365

9.1 Why program

Before proceeding, a scope note. In Chapter Information, we dis-

tinguished systems of records, systems of reports, and systems of

intelligence. What follows concerns the last class. Ledgers and

workﬂow engines—ERPs, WMSs, CRMs—are packaged success-

fully because they record discrete events and enforce rule-based

routes; entities are largely decoupled by construction. Conﬁgura-

tion can express the modest couplings that workﬂows introduce,

and COTS (Commercial oﬀ-the-shelf) suﬃces. What resists pack-

aging is the engine that composes those records into unattended,

economically priced decisions.

The emergence of enterprise software in the late 1970s extended

the postwar hope—nurtured by operations research—that the quan-

titative problems of supply chain would admit a closed, permanent

formulation. Vendors and academics aimed to crystallize those

resolutions into reusable frameworks so that decision-making could

be “conﬁgured” once and for all by non-technical users. This ambi-

tion, sensible for ledgers, was transplanted wholesale into systems

of intelligence.

Half a century on, this promise has not materialized for systems

of intelligence. COTS “planning” stacks take months or years to

install, mobilize armies of specialists, and still degenerate into

spreadsheet workarounds when they meet the grain of a living ﬂow.

The failure is structural—a mismatch between generic templates

and business-speciﬁc couplings.

Closed analytical resolutions are brittle because they presup-

pose that what must be decided can be fully enumerated in ad-

vance. Reality refuses to comply. The only durable advantage

that spreadsheets enjoy—despite their many defects—is precisely

their programmability. They let practitioners encode idiosyncrasies

the moment those matter economically. Programmability is not a

convenience; it is the essential property of a system of intelligence.

Innocent

looking quantities expose the brittleness. Consider

bulk cable. A ledger that says “10,000 meters on hand” hides what

actually governs feasibility and value: composition and cut. Ten

366 CHAPTER 9. ENGINEERING

coils of one kilometer and a thousand coils of ten meters both sum

to 10,000, yet they are opposite economic assets once customer

length proﬁles, waste on cutting, and residuals are priced. The

unit—meter—obscures the state that decisions must reason about.

Nothing about cable is exotic. Similar, perfectly ordinary quirks

pervade every vertical. In aviation, rotables and repairables exhibit

asymmetric compatibilities—parts that substitute one

way but not

the reverse once serialized revisions and life

limited components

are considered. In fashion, explicit and implicit substitutions shift

with the merchandising calendar and size-color ladders. In home

improvement, items are bound by project

level dependencies: a

faucet without the matching trap kit or connectors is dead stock.

In groceries, short shelf

lives meet aggressive markdown policies,

secondary channels, and supplier rebates that reprice inventory

within days. E

commerce couples SKUs at dispatch through pack-

aging, carrier rules, and delivery cut

oﬀs. Each domain’s “cables”

are commonplace inside the trade and fatal to generic templates.

Because supply chains are systems, such quirks do not remain

at the margins. They couple to the rest of the ﬂow through shared

capacities, baskets, and customer expectations. Ring-fencing them

for manual treatment merely shifts cost and risk. If they could

be quarantined, a small, semi-autonomous process would do; they

cannot. A constraint unearthed in one corner—coil lengths, serial

compatibilities, size-color ladders—alters the feasible assortment,

reprices options, shifts inbound cadence, and reshapes order as-

sembly and the service promise.

Software vendors face a stark engineering law when they try to

absorb these couplings into a packaged product: interactions grow

faster than capabilities grow. Each new “core” capability that truly

interacts with the others multiplies test surfaces and edge cases; a

modest 25 % expansion in such capabilities can easily double the

engineering burden. The cost curve is super-linear because the

product must cover many ﬁrms’ idiosyncrasies at once, whereas

each ﬁrm only needs its own.

In contrast, the crude programmatic instruments at hand—above

all spreadsheets—let practitioners focus on the few complications

9.2. EXPERIMENTAL OPTIMIZATION 367

that actually exist in their context. By narrowing scope to their

own economics, they sidestep the diseconomies that sink generic

resolutions. This, more than habit, explains why teams fall back

to spreadsheets after expensive “planning” deployments.

None of this indicts systems of records. CRUD applications that

store authoritative events and enforce rule

based workﬂows can be

packaged; their entities are largely decoupled by design and their

couplings are explicit, local, and auditable. The trouble begins

only when the same packaging posture is extended to a system

of intelligence, whose very purpose is to navigate ﬁrm

speciﬁc

couplings under uncertainty.

Packaged decision engines would be a comforting prospect for

non-technical managers; they would demote programming to a

second-class skill set. Unfortunately, the nature of systems of

intelligence rules this out. Short of general artiﬁcial intelligence, no

ﬁnite catalog of templates can anticipate the couplings that matter

to a given ﬁrm at a given time. The quest for conﬁgurability in

place of programmability is wishful thinking.

Thus, we are left with the prospect of leveraging ad hoc pro-

grammatic resolutions for supply chain. The task is less daunting

than it might look because spreadsheets are programmable, and

this aspect has not prevented spreadsheets from enjoying consid-

erable success within supply chain circles. However, it raises the

question of the adequacy of the tools we have for this undertaking,

starting with the choice of programming paradigms. Thousands

of programming languages have been devised since the 1950s. It

is unreasonable to assume that they are all equally suitable for

tackling supply chain challenges. In particular, the popularity a

language or tool enjoys beyond supply chain circles has little to do

with its suitability for addressing supply chain challenges.

9.2 Experimental optimization

Supply chain challenges are at the very least opaque, and occa-

sionally wicked as well. An opaque challenge is characterized by

368 CHAPTER 9. ENGINEERING

high epistemic uncertainty: we face known unknowns, and often

unknown unknowns. A wicked challenge is worse: the challenge

shifts as one attempts to address it—even when the actor is a third

party, typically a competitor. Wicked challenges do not abide by

any static formal resolution.

Opacity is an inherent property of the systems of records that

underlie the execution of the ﬂow. As discussed in Chapter Infor-

mation, merely establishing correct semantics for all the relevant

data represents a vast and error-prone undertaking. In practice,

even a modest numerical recipe cherry-picks hundreds of ﬁelds

from among tens of thousands of ﬁelds spread over hundreds of

tables.

Wickedness arises whenever the adequacy of the response de-

pends on what competitors are doing or not doing. For example,

a premium-price, high-availability stance can thrive when most

rivals chase low prices and thin stocks; it may falter as soon as

they converge on the same premium, high-service posture.

Experimental optimization

is the cornerstone of numerical

engineering for supply chain. It addresses, head-on, both the opac-

ity—and the occasional wickedness—of supply chain challenges.

Experimental optimization leverages “real-world” feedback to it-

eratively improve the numerical recipe. First, it weeds out the

mundane coding mistakes of economic import. Those mistakes

are both varied and frequent, and cannot be avoided by merely

scrutinizing the code. Second, it paves the way for discovering

the adequate optimization criteria. Without experimental opti-

mization, attempts at deploying superior software technologies for

supply chain invariably fail, and practitioners end up reverting to

their spreadsheets.

This methodology progressively emerged at Lokad in the early 2010s.

Joannes Vermorel coined the term “experimental optimization” in 2021 to

formalize the approach.

9.2. EXPERIMENTAL OPTIMIZATION 369

9.2.1 Cartesianism

Supply chain authors—and, by extension, most enterprise software

vendors—treat numerical recipes as a top-down, purely intellectual

undertaking. They begin by positing the situation—SKU counts,

network topology, constraints, metrics to be optimized, and simi-

lar particulars. From this baggage, the expert derives a solution.

The solution typically takes the form of a framework for compos-

ing the ﬁnal numerical recipes. The framework is introduced to

accommodate variation and to be reusable across many situations.

This approach is known as the Cartesian perspective, named for

the 17

-century French philosopher René Descartes. Cartesianism

places heavy emphasis on pure reason and the power of the mind.

It holds that arriving at truth is, at bottom, a strictly intellectual

exercise. From a scant set of self-evident truths, careful, methodical

reasoning should reach deeper, more complex conclusions.

While reason is certainly preferable

to authorities, mysticism,

corrupt incentives, or drug-induced visions, Cartesianism is not

without defects—especially in supply chain. Indeed, nothing is self-

evident about the initial baggage that purportedly characterizes

a given supply chain situation. In practice, this baggage is a

haphazard, inordinate assortment of items of questionable validity.

Authors usually know that a purely intellectual framework

is an unsatisfying proposition. Accordingly, they implement the

framework and run numerous in silico experiments on it. These

take the form of benchmarks, back-tests, and other simulations.

These “experiments” are intended to demonstrate the framework’s

soundness and performance under plausible conditions.

Unfortunately, subjecting a framework to a “plausible” bat-

tery of tests proves little—certainly not its soundness in operating

a real-world supply chain. At best, such tests catch gross is-

sues—scalability woes, for instance—that would otherwise impair

operations. Yet these in silico validations are nowhere near the

“Strange women lying in ponds distributing swords is no basis for a system

of government.” comes to mind as a famous line from the British comedy ﬁlm

“Monty Python and the Holy Grail” (1975).

370 CHAPTER 9. ENGINEERING

proof we seek. We seek strong guarantees that deploying the

resulting numerical recipe will be net positive for the company.

Such guarantees come not from added formalism but from

falsiﬁability in the Popperian sense (see Chapter Epistemology).

Each numerical recipe is a conjecture about how the ﬁrm should

commit scarce resources; its premises must be exposed to failure

in the world it intends to steer. Benchmarks and back-tests are

useful pre-ﬂight checks—they probe internal consistency and scala-

bility—but they do not confront adversarial incentives, semantic

drift, and fat-tailed events that govern live ﬂow. The appropriate

remedy for the Cartesian posture is, therefore, methodological, not

algebraic: engineer the environment so the recipe meets reality

early and often, and make refutation the main source of progress.

9.2.2 Mundane issues

When a supply chain practitioner implements a numerical recipe,

the ﬁrst batch of generated decisions is all but guaranteed to be un-

satisfactory. Many ﬁgures the recipe produces will be “insane”—a

term we revisit shortly. The root causes of those insane decisions

are usually simple. Five classes commonly undermine numerical

recipes: mundane bugs, data semantics, constraints, economic

drivers, and strategy.

First, mundane bugs. A numerical recipe worthy of a real-

world supply chain spans thousands of lines of code—to say nothing

of the millions in generic dependencies. Seven decades of software

engineering have taught one lesson: the only code without bugs

is the code unwritten. Suitable paradigms may improve matters,

but complete elimination is nowhere in sight—especially when

combining software written by many hands, where issues emerge

at the boundaries.

Second, data semantics woes. This class has already been

discussed in Chapter Information. Fundamentally, the semantics

of any database column—say, “Order Date”—are in the eye of the

operator. If an operator takes “Order Date” to mean the date the

supplier acknowledged his willingness to serve the order, then that

9.2. EXPERIMENTAL OPTIMIZATION 371

is the semantics, whatever the system-of-records documentation

says. Given that a modern system of records has tens of thou-

sands of columns, many of them interpretable in multiple ways,

misunderstandings are unavoidable. Such misinterpretations can

severely distort the generated decisions.

Third, constraints are often either untold or invented. An

untold constraint is one not properly documented. For example,

Bob, who handles warehouse replenishments, knows that above

5,000 inbound units per day the team struggles and must resort

to expensive overtime to keep up. Bob learned this a year ago

while spending a few months on the warehousing team. Yet this

throughput limit was never documented, so a numerical recipe

ignores it entirely. Conversely, invented constraints arise whenever

the real constraint is deemed too convoluted for semi-manual

resolution. For example, price breaks oﬀered by a supplier are

often replaced with an MOQ (minimum order quantity) reﬂecting

whatever per-unit price the purchasing team deems acceptable.

Yet the constraint is fabricated: it is possible to order below the

so-called MOQ, provided the company accepts a higher per-unit

price. A recipe that misses this nuance will often overstock by

forcibly reaching for the MOQ.

Fourth, economic drivers can be incorrectly assessed. As

seen in Chapter Economics, valuation is hard. Analyzing the ﬂow

economically requires numerous heuristics for spending and rev-

enue attribution. Moreover, many long-term eﬀects can only be

estimated crudely. For example, discounts are likely to erode the

customer’s willingness to pay, making him increasingly opportunis-

tic about waiting for the next promotion. Quantifying this eﬀect

is challenging, as the relevant time spans are measured in years.

A recipe driven by incorrect economics generates decisions with

the optics of prudence that, over time, fail to deliver the expected

returns.

Fifth, the company’s strategy may look good on paper but

not in execution. For example, leadership may prioritize “ultimate

customer satisfaction”, only to learn that leniencies—liberal returns

or overnight shipments—are abused by bad actors. More generally,

372 CHAPTER 9. ENGINEERING

strategy usually reﬂects, to a large degree, an intuitive grasp of the

company’s underlying economics. Thus, the understanding can be

directionally correct yet miss the ﬁne print, which may demand

selective use of alternative strategies. More broadly, the nature

of markets—and their unrelenting change—guarantees that, with

time, any given strategy will be undermined by competitors.

The odds of obtaining suitable decisions from a newly drafted

numerical recipe are vanishingly small. Chasing “First-Time-

Right”—a perspective common in manufacturing—is wishful think-

ing in supply chain. Ambient complexity and opacity are too high

for this. It is, however, very much possible to improve this original

draft. It boils down to identifying—and then eliminating—the

“insane” decisions.

9.2.3 Insane decisions

A supply chain decision is “insane” when it will, most likely, gener-

ate unintended losses for the company. A freshly drafted numerical

recipe invariably generates numerous insane decisions. Beyond

immediate ﬁnancial damage, insane decisions rightly undermine

practitioners’ trust in the automation itself. Left unaddressed,

their very presence in the outputs guarantees that practitioners

will ditch the recipe and revert to spreadsheets.

Note ﬁrst that while identifying insane decisions is a problem of

general intelligence (cf. Chapter Intelligence), it is not a problem

of great intelligence. A careful manual review—one ﬁgure at a

time—by a practitioner suﬃces to catch most oﬀenders. The

primary hurdle is not the practitioner’s capacity but the sheer time

required to perform such a review at scale.

The diﬃculty is akin to identifying implausible plot twists in

a novel. Guidelines may expedite the task, but there is no hope

of fully addressing this challenge within the conﬁnes of a narrow

formalization. Yet most readers can readily tell when a story

ventures beyond what can be considered “plausible”.

Counterintuitively, insane decisions are often the only way to

surface ﬂaws in the numerical recipe. Confronted with an insane

9.2. EXPERIMENTAL OPTIMIZATION 373

decision, one must reverse-engineer the sequence of calculations

that led to it. In software, a defect almost invariably reﬂects a

mistaken belief. Something is believed true when, in fact, it isn’t.

By its very existence, the insane decision helps the practitioner

pinpoint which belief is incorrect.

9.2.4 Iterative falsiﬁcation

Experimental optimization is a methodology that seeks to falsify

the numerical recipe as it presently stands. It recognizes that

supply chain decisions must be subjected to repeated trials against

reality, and that these trials play a fundamental role in exposing

hidden mistakes in even the most carefully conceived solution.

Rather than pretending the problem is fully pinned down in ad-

vance, experimental optimization seeks to uncover and ﬁx problems

through an iterative, hands-on loop.

The process unfolds as follows:

Propose a numerical recipe—a software routine or script—

that converts available data (forecasts, costs, constraints) into

explicit supply chain decisions (purchase orders, production

runs, allocations).

Run the recipe in an environment stable enough to generate

real or near-real decisions, forcing it to face the raw com-

plexity of a live dataset—with no pretense that “diﬃcult”

products or sites can be conveniently excluded.

Identify “insane” decisions; this task typically demands hu-

man scrutiny: managers or analysts inspect outliers, suspi-

cious allocations, or jarringly large orders until they locate

the root cause. The cause may lie in incorrect data semantics,

an overlooked cost driver, or a newly emerged constraint.

Reﬁne the instrumentation and revise the recipe: missing

data or features (e.g., lead-time variance, new categories of

supplier constraints) drive the next revision. The explicit

374 CHAPTER 9. ENGINEERING

cost structure may also be adjusted if the iteration reveals

that the penalty for stockouts is unrealistically low or induces

wasteful overstock.

Repeat until each loop ceases to generate new “insane” re-

sults; over time, glaring defects diminish and solutions be-

come more robust and more aligned with the economic reality

of the supply chain.

A distinctive strength of this iterative approach is that it

invites direct falsiﬁcation. If the recipe’s proposed decisions look

problematic—even for a few SKUs or sites—some assumption

must be challenged and corrected. This contrasts with the purely

mathematical optimization view, where proofs of optimality oﬀer

little recourse when the real world fails to match the original model.

Experimental optimization welcomes such mismatches: each defect

or “insane” recommendation is an opportunity to expose deeper

errors.

9.2.5 Instrumentation

Experimental optimization depends heavily on instrumentation.

The data pipeline must allow swift preparation and execution of

each new iteration. If each revision takes weeks to deploy, the

process cannot keep pace with its environment. Hence the need for

carefully versioned data, reproducible workﬂows, and the ability

to “time-travel” by inspecting past states and results.

One might expect metrics—inventory turns, ﬁll rates, forecast

accuracy—to be the ﬁnal arbiters of success or failure. These mea-

sures matter, but they lag events and can be warped by changing

conditions. Moreover, metrics can be gamed. Companies under

pressure to boost service levels, for example, may simply over-

stock across the board. A few months later, the inﬂated stock

triggers cost overruns. In response, new constraints are layered

atop the original system. This back-and-forth between metrics and

behaviors can devolve into a tangle of shifting rules that obscure

performance.

9.2. EXPERIMENTAL OPTIMIZATION 375

In experimental optimization, the clearest signal of progress

is the absence of glaring missteps. Each day the software’s pro-

posed decisions appear sane without manual ﬁxes or last-minute

overrides. If the supply chain teams no longer need to “ﬁreﬁght”

for half their working hours, the business is already capturing

better performance: fewer disruptions, fewer frantic reshipments,

fewer odd workarounds on the warehouse ﬂoor. Over time, these

operational gains reﬂect deeper alignment of decisions with correct

cost structures and more reliable data semantics—improvements

that will eventually show up in standard metrics as well. Even if

top-line ﬁgures take time to adjust, the disappearance of “insane”

decisions is an unmistakable sign of success.

9.2.6 Continuous improvement

Unlike a closed-form approach that purports to “solve” the supply

chain once and for all, experimental optimization does not claim

convergence to a stable optimum. Supply chains evolve, constraints

change, the product mix shifts, new manufacturing plants open,

promotions or price wars erupt. Thus the loop never closes. A

well-tuned recipe running with minimal insanity today may re-

quire substantial updates tomorrow if a global pandemic, a new

competitor, or a shift in the ﬁrm’s strategic posture arises. Even

without external shocks, fresh insights—such as newly discovered

cost drivers—warrant further adjustments.

This perpetual ﬂux is no defect. On the contrary, it highlights

that supply chain mastery is about orchestrating changes before

they cascade out of control. By structuring each change as a small,

veriﬁable experiment, the organization gains both resilience and

clarity of purpose. Instead of proclaiming a ﬁnal formula, supply

chain specialists become guardians of a living process—one that

recognizes inevitable disruptions as fresh rounds of input.

Experimental optimization merges two ideas that are often kept

separate. First, it embraces quantitative ambition: a supply chain

is best guided by numbers, systematically handled and recombined

by algorithms. Second, it acknowledges falsiﬁability: the surest

376 CHAPTER 9. ENGINEERING

way to conﬁrm or refute a method is to subject its output to

real conditions. Whether a given plan “makes sense” is known

only once the plan confronts raw data on the shop ﬂoor, in the

warehouse, or at the retail shelf.

By insisting that every proposal runs the gauntlet of real-

ity, experimental optimization transforms errors into catalysts for

learning. The cyclical process—instrument, decide, spot insanity,

correct, and rerun—does not guarantee that all hidden constraints

and cost drivers will be discovered at once. Rather, it ensures

that the entire approach, however incomplete or provisional, stays

moored to the mission of supply chain: the best possible decisions

within irreducibly complex ﬂows. Through repeated trials, the do-

main’s richness (and unruliness) becomes an engine for incremental

progress rather than an insurmountable barrier.

9.3 Whiteboxing

An opaque numerical recipe produces outputs—prices, reorder

quantities, replenishment plans—yet remains impenetrable to the

very practitioners who depend on them. In modern supply chains,

any nontrivial data pipeline tends to exhibit this “black box”

behavior, in which the numerical recipe involves layers of automa-

tion or obscure mathematical models. Even solutions based on

straightforward formulas—such as safety stocks derived from “nor-

mal demand” assumptions—become black boxes in practice once

they handle thousands of items, each with its own nuances and

constraints.

Whiteboxing counters this opacity. It recognizes that no ad-

vanced data pipeline is intrinsically transparent. Without deliber-

ate eﬀort, the logic of a numerical recipe remains buried within the

code, forcing practitioners to choose between two unsatisfactory

options: blindly trust the system, or fall back on spreadsheets they

can fully control that lack the power or scalability to address the

actual complexity of the ﬂow. Whiteboxing provides a third path,

preserving strong automation while revealing the “why” behind

9.3. WHITEBOXING 377

each recommendation.

A core principle of whiteboxing is that clarity must serve the

people on the front line of supply chain decisions. Obfuscation can

take many forms—massive data tables, unveriﬁed constraints, or

advanced machine learning algorithms that only a handful of data

scientists can interpret. The response is not to simplify all calcula-

tions into toy formulas, which would squander the computational

capabilities needed to handle intricate ﬂows. Rather, whiteboxing

takes the more challenging route of exposing each decision’s main

economic drivers. In addition to raw allocation ﬁgures, the recipe

should display a short list of maximally relevant economic indica-

tors. For an order recommendation, these indicators could include

current inventory, lead time, projected stockout risk (in monetary

terms), and the cost of overage if the proposed quantity proves

excessive. This handful of columns oﬀers two essential beneﬁts:

ﬁrst, they serve as quick sanity checks for the practitioner; second,

they pinpoint where the decision might go awry if any assumption

or piece of data proves incorrect.

Such transparency relies on bringing the data pipeline close

to the user. Practitioners must be able to add, remove, or ad-

just the explanatory metrics without launching a months-long IT

project. Just as experimental optimization requires rapid iteration

to expose and eliminate “insane” decisions, whiteboxing requires

rapid iteration in the dashboards themselves. If a decision looks

oﬀ, the team reﬁnes or adds an explanatory indicator, reruns the

pipeline with time-travel reproducibility, and studies whether the

newly revealed driver explains the decision or exposes a bug. This

approach means that every new indicator—be it a lost-sales penalty

or a container-ﬁll cost—can be swiftly tested and adjusted. Over

time, these indicators cohere into a compact suite of cost and

risk measures that shed light on each recommendation without

inundating users with trivial metrics.

Crucially, whiteboxing guarantees neither universal consensus

nor perfect modeling of all constraints. Instead, it seeks to ensure

that decisions do not trigger abrupt disbelief or “panic overrides” by

practitioners who cannot see the logic behind them. Transparency

378 CHAPTER 9. ENGINEERING

lowers the threshold of trust needed to adopt automation. When

supply chain teams can tie a proposed production run or reorder

quantity to a visible trade-oﬀ—say, the cost of a potential line

stoppage versus the holding cost of extra parts—they more readily

accept the numeric result, even if it contrasts with past rules

of thumb. Conversely, when an outcome deﬁes intuitive checks,

whiteboxing directs attention to which driver may be missing or

mislabeled, guiding the next improvement.

Another vital dimension of whiteboxing is its capacity to reduce

organizational friction. In many companies, forecasts or replenish-

ment plans move from data science teams to supply chain managers,

then to ﬁnance or warehouse operations, each group harboring

its own metrics, data deﬁnitions, and constraints. This complex

handover often multiplies confusion: a manager seeing “buy 1,200

units” in one system might not know that a quantity-based dis-

count was factored in elsewhere. By revealing the principal costs

and constraints directly in the ﬁnal decisions, whiteboxing bridges

these handoﬀs. Stakeholders can see, in monetary terms, how each

driver was weighed. They can correct or reﬁne those values without

dismantling the entire pipeline.

Far from a one-oﬀ task, whiteboxing serves as an ongoing design

principle. As soon as a supply chain evolves—through new suppli-

ers, changed lead times, or adjusted strategic goals—so must the

transparency mechanisms. If not, hidden or outdated assumptions

resurface, creating new black-box zones. Sustaining whiteboxing

demands regular collaboration between domain experts and the

teams responsible for engineering the numerical recipes, ensuring

that whenever an aspect of the ﬂow changes, the explanatory

metrics change as well.

Whiteboxing thus stands at the intersection of reliability and

credibility. It addresses the practical realities of modern supply

chains, where advanced numerical methods are indispensable, yet so

is the trust of the people who put those methods to work. Without

whiteboxing, the best-engineered system remains unusable; with

it, even sophisticated processes become comprehensible enough for

practitioners to adopt, reﬁne, and operate day after day.

9.4. FERMI-IZATION OF THE ELUSIVE 379

9.4 Fermi-ization of the elusive

No matter how one approaches the numerical recipe, supply chain

teams inevitably encounter elusive parameters. These are quanti-

ties or forces that do not readily appear in any system of record and

thus elude direct measurement. They can be strategic—for exam-

ple, the expected release date of a promising, still-in-development

technology on which a product roadmap implicitly depends. Or

they can be operational—such as the probability that a large but

ﬁnancially stressed supplier will ﬁle for bankruptcy within the next

few quarters. When confronted with such factors, the traditional

approach is to bypass the problem. Practitioners might leave

these parameters out of the automation altogether and revert to a

semi-manual process guided by a mixture of opinion, guesswork,

and cautionary hedges. That workaround dooms the numerical

recipe to dependence on continuous human intervention, which

undermines any prospect of genuine automation.

A better approach treats these “unmeasurables” as prime tar-

gets for a Fermi-style analysis, akin to what Philip E. Tetlock and

Dan Gardner describe in “Superforecasting: The Art and Science

of Prediction” (2015). The key insight, popularized by the physi-

cist Enrico Fermi, is that a seemingly impossible quantity can be

broken down into a series of more tractable estimates that, when

multiplied or added, produce a credible estimate of the original

unknown. Fermi himself famously asked students to estimate how

many piano tuners worked in Chicago—a ﬁgure most would deem

unknowable absent an oﬃcial listing. Yet by dividing the problem

into small, separate pieces (such as the approximate population of

the city, the fraction of households with pianos, and how often a

piano gets tuned), the ﬁnal estimate can land surprisingly close to

the truth.

In supply-chain practice, a more telling illustration is the prob-

ability that import tariﬀs on goods from Ruritania will double

over the next ﬁve years. This quantity appears in no ledger, yet

it directly governs buying cadence, sourcing mix, and pricing. A

Fermi decomposition renders the estimate tractable. Start with an

380 CHAPTER 9. ENGINEERING

outside view: tally, over the last century of Westonia–Ruritania

relations, how often tariﬀs of comparable magnitude were raised or

lowered, and over what horizons. This historical base rate anchors

the prior ﬁrmly. Then incorporate the inside view: read the present

political discourse in Westonia; judge whether the party in power

argues for higher or lower tariﬀs and whether the topic is salient or

peripheral to its platform, and adjust the prior accordingly. Next,

weigh plausible election outcomes over the relevant horizon—say,

two years ahead—using polling or market odds. Conditional on

each outcome, assess the likelihood that the winning party would

pursue a doubling. Finally, consider retaliation pathways: a tariﬀ

initiated by Ruritania may trigger reciprocal measures by West-

onia. Mirror the same reasoning from the Ruritanian side—its

domestic politics, its historical propensity to use tariﬀs, and the

salience of trade tensions—to price the chance that Westonia’s

move is reactive rather than proactive. The end product is not a

point estimate but a calibrated probability that can be revised as

information accrues.

Once this probability exists, it can be translated into money-

valued decisions: expected duty uplift on aﬀected SKUs; the option

value of accelerating or deferring imports ahead of cutoﬀs; the

premium justiﬁed for alternative origins with lower exposure; and

the merit of postponement (late binding) to keep tariﬀ-sensitive

transformations oﬀshore. The estimate can be rough; what matters

is that it is explicit, auditable, and placed on the same footing as

purchase and holding costs, enabling unattended software to weigh

it alongside other drivers.

Crucially, this decomposition of the elusive parameter should

not stop once the initial guess has been coded. Under experimental

optimization, these newly introduced parameters remain subject to

the same falsiﬁcation loops as everything else. The fact that “tariﬀ-

doubling probability” was not in the system of records does not

exempt it from scrutiny; on the contrary, its novelty calls for vigilant

watchfulness. If an insane decision emerges—for instance, one that

systematically rushes oversized inbound orders and starves working

capital to preempt a shock that never materializes—this signals that

9.4. FERMI-IZATION OF THE ELUSIVE 381

the probability (or its economic translation) is inﬂated. Conversely,

if duties do jump and the numerical recipe shows no sign of prior

hedging—no pulled-forward shipments, no origin diversiﬁcation, no

postponement—then the probability was understated. Reﬁnement

is iterative: each pass, guided by real outcomes or near misses,

tightens the logic until no obviously insane decisions arise.

In practice, the Fermi approach also becomes a powerful guardrail

against the cognitive biases that attend any guess. These guesses

are not conjured from a vague impression but anchored in a chain

of smaller, more tangible estimates. The process fosters “active

open-mindedness”, a trait documented among Tetlock’s superfore-

casters, who methodically break down a problem and then weigh

outside perspectives to calibrate each piece. The entire supply

chain team can see—directly in the code or in a white-boxed dash-

board—how this tariﬀ-doubling probability was computed, which

fosters alignment and invites collaborative ﬁne-tuning rather than

unilateral overrides. If an import manager judges that the election

weights, or the salience of trade policy in a manifesto, is oﬀ, the

team can revise that single sub-estimate rather than tear apart

the method.

Although it is laborious, Fermi-ization is a straightforward

extension of the techniques already needed to keep a numerical

recipe fully automated. Once supply chain practitioners accept

that no parameter is too intangible to be broken down—even if it

must be done with rough heuristics—the path to including those

elusive elements in the system becomes clear. Just as with “normal”

parameters, the guess is best introduced under rigorous instru-

mentation, so that each production run can conﬁrm or challenge

the initial logic. Supply chain mastery thrives on iteration rather

than one-oﬀ determinations. When confronted with the intangible,

the biggest mistake is not misestimating initially; it is refusing to

produce any estimate at all, thus dooming the recipe to partial

manual operation.

By fully embracing the Fermi principle, supply chain practition-

ers can tackle exactly those domains previously deemed “beyond

382 CHAPTER 9. ENGINEERING

measurement”

. Instead of bowing to intangible elements that

incapacitate automation, they transform them into a set of smaller,

more tractable pieces that can be tested and reﬁned. As with all

parts of a living numerical recipe, each approximation is linked to

the real-world results it produces, allowing supply chain to integrate

what it learns over time. The alternative—silently discarding such

elusive parameters as hopeless—ensures that automation remains

little more than an aspiration. Fermi-ization oﬀers a resolutely

practical path to making even the most elusive parameters explicit,

ensuring that all critical drivers of the ﬂow, intangible or not, can

be folded into a holistic automated system.

9.5 On programmability

In previous chapters, we argued that modern supply chains must be

steered by unattended software that issues auditable, economically

priced commitments. Once we accept a programmatic resolution to

supply-chain problems, a question that is mundane in appearance

but foundational in fact arises: in which language should these

decisions be expressed? This is no tooling whim. A language ﬁxes

which ideas can be made explicit, which options can be enumerated

without contortion, how uncertainty is priced, and how resulting

commitments are audited years later.

The ﬁeld has mostly dodged this question since the end of the

Recent progress indicates that much of the clerical work behind Fermi-

style decompositions can be delegated to large language models operating in

“deep research” mode: the model proposes factorizations, retrieves order-of-

magnitude anchors with citations, and drafts calibrated ranges for human vet-

ting. Benchmarks centered on decision-oriented forecasting—e.g., ForecastBench

(2024), a continuously updated evaluation of models on probability-bearing

questions—report competitive performance by modern models; in parallel,

methodological studies show that careful prompting materially improves cal-

ibration and prediction intervals. Economics remains the binding constraint:

producing millions of such Fermi-ized ranges per month is not yet sensible for

most ﬁrms, yet generating a few thousand well-documented ranges monthly is

already operationally straightforward with today’s APIs and agentic tooling.

9.5. ON PROGRAMMABILITY 383

4GL episode.

The vacuum has been ﬁlled by spreadsheets, rule

engines glued to ERPs, and scripting in general

purpose languages.

Each of these instruments has its place, but none is adequate to

serve as the native medium for unattended decision-making at

supply-chain scale.

Systems of records speak SQL because they must preserve

facts, not invent them. SQL excels at stating what is inside a

ledger—rows and their relations—not what should be done next

in the face of variability and trade

oﬀs. Rule engines mounted

on top of transactional systems try to bridge the gap with a drip

of if

then clauses; they age into brittle accretions that no one

fully understands, least of all when incentives change. Scripting

languages—today most often Python, yesterday VBA

, earlier

still proprietary macros—are powerful prostheses for analysts, but

their very freedom undermines the properties we need most in

production: determinism, reproducibility, and strict control of

side eﬀects. They produce artisanal tooling; unattended decision

engines must be industrial.

A suitable language for supply-chain decision automation must

align with ﬂow economics and the physics of computers. Its ob-

jects should be the ones practitioners actually manipulate: typed

tables keyed by SKUs, locations, and dates; arrays over those

tables; and—crucially—random variables that carry demand, lead

time, and return distributions. Uncertainty must be ﬁrst-class,

together with an algebra that converts distributions into currency-

denominated payoﬀs so that the expected, risk-adjusted return

of any move can be written as a short formula rather than an

after-the-fact simulation. Prices must be explicit: currency con-

versions and rounding as built-in functions. Time must also be

ﬁrst-class: calendars and calendar algebra. When these primitives

exist, the engine expresses the decision directly; when they are

“Fourth-generation languages” promised, from the late 1970s onward, to

let analysts describe business logic declaratively—report writers and data-

manipulation languages tethered to a database. The label faded by the early

2000s, but the underlying need did not.

Visual Basic for Applications

384 CHAPTER 9. ENGINEERING

missing, practitioners smuggle them in piecemeal and the codebase

degenerates into workarounds.

Because unattended engines live or die by auditability, execu-

tion must be deterministic. The same inputs must yield the same

outputs—down to the last cent—even when computation is paral-

lelized. Randomness, when needed for stochastic simulation, must

be explicit and reproducible. Side eﬀects must be quarantined at

the boundary: read authoritative records, compute decisions in a

pure core, then write the resulting commitments back to the ledger.

This separation of concerns is not academic tidiness; it is the only

way to make ex post audits and dual

run comparisons meaningful.

Decision logic over millions of SKUs, sites, and dates cannot

be written as a procession of row-by-row loops without buckling

under its own weight. The language therefore needs array or table

semantics—vectorized operations over large, typed collections—so

most programs read as algebra over sets rather than choreography

over iterators. Such designs encourage mechanical sympathy: they

map well to caches and parallel units and make large problems

cheap enough to solve often.

The question declarative or imperative? is ill-posed in the

abstract. We need an imperative shell to stage the ﬂow—ingest,

compute, emit—and a mostly declarative core to express valuations

and constraints as formulas over tables and distributions. In

practice, this means feasibility tests, penalties, and prices are

written as expressions the engine can analyze, reorder, and partially

recompute. The resulting transparency enables halting heuristics:

when conﬁdence drops, the engine stops on its own terms instead

of pestering humans with exceptions it cannot resolve.

None of the above is about syntactic taste. Braces versus inden-

tation is trivial. What matters is semantics: ﬁrst-class uncertainty,

money and time as citizens rather than afterthoughts, side-eﬀect

isolation, determinism, vectorized evaluation, and a runtime that

produces dossiers ﬁt for audit. General-purpose languages can

be bent toward these aims via conventions and discipline; expe-

rience shows that at scale, and over time, conventions decay. A

supply

chain language worthy of the name bakes these properties

9.5. ON PROGRAMMABILITY 385

into its very grammar.

The remainder of this section operationalizes the selection cri-

teria. We examine how language semantics bear on unattended

execution (determinism and reproducibility), on economics (native

money, prices, and penalties), on uncertainty (probabilistic prim-

itives and stochastic simulation), and on maintainability (small

cores, legible programs, diﬀ-friendly artifacts). The goal is simple:

choose a language that lets the ﬁrm write its economics down once,

run it at machine speed, and defend every commitment ex post in

coins.

9.5.1 Programming paradigms

A programming paradigm is the framework that shapes how soft-

ware is conceptualized, structured, and executed. It ﬁxes the

principles for organizing data, deﬁning operations, and determin-

ing control ﬂow, guiding both design and execution.

Developing suitable programming paradigms for supply chain

is arguably the single most important technical contribution to

the ﬁeld. Indeed, a suitable programming paradigm facilitates the

resolution of most—if not all—supply-chain challenges, irrespec-

tive of their speciﬁc complications. In this regard, programming

paradigms are wholly dissimilar to “models” or any other kind

of closed analytical solutions, which are, by design, brittle when

confronted with unplanned complications. While many techniques

are of broad interest—stochastic gradient descent would be a

perfect example—programming paradigms are unrivaled in this

regard. The short list below presents some of the most relevant

programming paradigms for supply chain.

Vertical integration names an environment that provides

the entire stack—from data storage, through processing and logic,

to presentation—inside a single, coherent medium. The canonical

exemplar is the spreadsheet: a monolithic application where the

grid is at once the datastore, the formula engine, and the user

interface, with live recalculation binding the three. In such an

environment there is no impedance mismatch between “where

386 CHAPTER 9. ENGINEERING

the facts live”, “how they are transformed”, and “how they are

inspected”; one artifact carries them all.

Vertical integration matters because it collapses handoﬀs. It

lets a small number of practitioners who are not software specialists

author, run, and audit numerical recipes end-to-end. The same

person can ingest records, express the decision logic, and visualize

the resulting commitments without waiting for another team to

expose an API or wire a dashboard. Absent vertical integration,

composing the recipe becomes a technical project that cannot be

left to anyone but software experts. The eﬀort metastasizes into

the usual procession—supply-chain experts, consultants, project

managers, database administrators, software engineers, data an-

alysts—whose coordination creates an interface-design problem

larger than the original economic problem. Spreadsheets demon-

strate both the attraction and the limits of this pattern: they make

vertical integration immediately tangible, yet they also reveal why,

beyond a trivial scale, a diﬀerent substrate is required (see the

section “Spreadsheets” below).

Time-travel reproducibility means that the environment

preserves the exact code and data as they existed at any point in

history. Users can step back in time and rerun the computations

as they were. This type of end-to-end versioning (code + data)

ensures that results are not only reproducible in principle but also

automatically veriﬁable at any historical point.

Indeed, in supply-chain decision-making, even small glitches

can prove extremely costly if bad decisions are generated

. Too

often the problematic behavior cannot be reproduced because, by

the time the issue is identiﬁed, the input data has changed. As

a result, glitches remain unaddressed far longer than desirable.

Time-travel reproducibility is critical for eliminating those defects

before supply chain teams lose faith in the viability of the numerical

recipe and revert to their spreadsheets.

In computer programming jargon, a heisenbug is a software bug that seems

to disappear or alter its behavior when one attempts to study it. The presence

of heisenbugs usually reﬂects the lack of time-travel reproducibility.

9.5. ON PROGRAMMABILITY 387

Relational algebra operates over tabular data—typically

stored across multiple tables—through a concise set of composable

operations such as selection, projection, join, and aggregation.

These operations, formalized in the 1970s,

gave rise to the SQL

(Structured Query Language) family of technologies that remains

ubiquitous in modern enterprise systems. The chief virtue of

relational algebra is its declarative nature: rather than spelling out

how to perform operations step by step, developers and analysts

specify what results they want, and let the execution engine handle

the mechanics.

Relational algebra is paramount for supply chain because the

overwhelming majority of the operational data—sales transactions,

purchase orders, stock levels, transportation events—resides in

relational databases. The relational model oﬀers a systematic

way to link these pieces, ensuring that joins on common keys

yield a coherent whole. Without relational algebra to elegantly

unify these disparate sources, practitioners fall back on a tangle of

ad hoc workarounds—typically unwieldy macros in spreadsheets—

which are not only error-prone but also computationally slow. In

practice, a relational algebra is indispensable for any numerical

recipe supporting a modern supply chain, owing to supply chain’s

inherent complexity.

Array programming is a paradigm in which operations apply

uniformly to entire collections of data—known as arrays—rather

than requiring developers to write loops over individual elements

By treating data in aggregate, array languages and libraries

en-

able concise expressions that mirror the mathematical formulation

of the problem. Instead of writing error-prone loops, the practi-

tioner speciﬁes higher-level transformations—such as arithmetic,

logical, or statistical operations—that automatically broadcast

Edgar F. Codd introduced the relational model of data in 1970. His landmark

paper “A Relational Model of Data for Large Shared Data Banks” remains the

foundational reference for relational databases.

Kenneth E. Iverson is credited with developing array-based approaches to

programming, beginning with APL in the 1960s.

APL, MATLAB, NumPy, etc.

388 CHAPTER 9. ENGINEERING

over each element of the array. By eliminating the need for explicit

indexing, array programming signiﬁcantly reduces oﬀ-by-one errors

and other subtle defects. Moreover, because the underlying execu-

tion engines can optimize bulk operations, array code frequently

outperforms equivalent loop-based routines, particularly on large

datasets.

From a supply chain perspective, array programming is critical

because nearly all relevant decisions must be computed for thou-

sands (if not millions) of SKUs, store locations, or time steps at

once. Iterative or loop-based approaches make this process slower

to write, harder to debug, and more prone to silent mistakes,

especially when coping with edge cases or intricate constraints.

In contrast, array programming’s vectorized style yields compact,

expressive code that is not only easier to review but also sim-

pler to extend as new product lines, locations, or supply chain

complexities emerge. This heightened clarity reduces the risk of

subtle logic bugs going undetected, while the performance gains of

vectorized operations enable faster iteration. In practice, these two

attributes—correctness and agility—greatly improve a company’s

ability to prototype, reﬁne, and conﬁdently deploy its decision-

making processes.

Distributed dataﬂow is a programming paradigm that auto-

matically distributes computations across multiple machines, ef-

fectively partitioning and parallelizing workloads to accommodate

vast volumes of data. Rather than forcing developers to orchestrate

concurrency on a step-by-step basis, distributed dataﬂow systems

typically represent data transformations as Directed Acyclic Graphs

(DAGs), which can then be executed in parallel by a cluster of

machines

. This approach promotes resilience to partial failures—

faulty nodes can be bypassed or replaced without halting the entire

job—and naturally leverages modern cloud infrastructures. By

abstracting away most low-level details of network communication

Early industrial uses of distributed data processing can be traced back

to mainframe clustering in the 1980s, but frameworks like MapReduce (2004)

and Apache Spark (2014) popularized more accessible paradigms for large-scale

dataﬂow computations.

9.5. ON PROGRAMMABILITY 389

and process synchronization, distributed dataﬂow enables practi-

tioners to focus on the logical ﬂow of their calculations instead of

wrestling with concurrency primitives.

For supply chain, the signiﬁcance of distributed dataﬂow is

twofold. First, the sheer scale of many operations—spanning

SKUs, warehouses, and transportation routes—requires ingesting

and transforming massive datasets. A centralized or sequential

system would be prohibitively slow, leaving decision-makers with

stale outputs or forcing them to limit the scope of their analyses.

Second, it facilitates pooling of computing resources that are only

intermittently needed. For example, if a forecast need only be

refreshed once per day, the corresponding computing resources can

be acquired and released on demand, vastly lowering infrastruc-

ture costs. More generally, distributed dataﬂows are critical to

maintaining stable performance under highly variable workloads.

Diﬀerential execution is a programming paradigm in which

the system automatically tracks dependencies between calculations

and re-executes only those portions of the workﬂow aﬀected by

a given change. Rather than re-running the entire pipeline from

scratch, the engine inspects a graph of computation—representing

data transformations as nodes and edges—and evaluates a “diﬀ”

between the current graph and the one previously executed. Inter-

mediate outputs that remain valid are preserved, while only the

new or modiﬁed nodes are recalculated. Additionally, the system

can intelligently cache expensive or time-consuming intermediate

results that yield relatively small outputs. By doing so, it acceler-

ates subsequent runs that involve the same partial computations.

This paradigm stands apart from both standard batch processing

and purely incremental approaches, as it combines the breadth of

a full dataﬂow engine with the selectivity of ﬁne-grained updates.

From a supply chain perspective, diﬀerential execution is critical

because numerical recipes are inherently iterative. Practitioners

typically edit a few lines and then rerun the recipe—sanity-checking

the results computed so far. Given that the ﬁnal recipe often spans

thousands of lines, its development involves hundreds of small

iterations. Without diﬀerential execution, each small tweak to

390 CHAPTER 9. ENGINEERING

the script would require reprocessing all datasets in full. This is

prohibitively expensive and discourages rapid iteration, especially

when dealing with gigabyte-sized datasets ﬂowing in from enterprise

systems. By zeroing in on the minimal set of computations that

need refreshing, diﬀerential execution preserves resources, reduces

turnaround time, and fosters fast feedback loops.

Algebra of random variables is a programming paradigm

where uncertain quantities are modeled and manipulated as random

variables—not merely as numerical scalars or point estimates. In

this environment, addition, multiplication, and other arithmetic or

logical operations can be applied directly to random variables, yield-

ing new random variables. Rather than bundling all uncertainty

into error-prone ad hoc simulations or unstructured data tables,

practitioners can deﬁne each uncertain factor—demand, lead time,

buy prices, production yields, or returns—as its own distribution.

When these factors interact, the language seamlessly combines their

distributions through a coherent set of probabilistic operations.

The resulting code closely mirrors the underlying stochastic phe-

nomena, bypassing complex looping or separate Monte Carlo steps.

By treating random variables as ﬁrst-class citizens, this paradigm

enables precise and transparent transformations of uncertainty

throughout the entire decision-making pipeline.

This algebraic treatment of uncertainty proves vital for supply

chain, given the pervasive role of probabilistic forecasts in risk

analysis. In real-world operations, it is never suﬃcient to track

a single best guess: demand often spikes without warning, parts

arrive late for reasons no one fully understands, and exchange rates

ﬂuctuate in ways that mock simple predictions. Without an explicit

“algebra” to combine these distributions, simplistic point-based

analytics are almost invariably used, which then silently discard

information as soon as averages or worst-case assumptions are taken.

By contrast, an algebra of random variables preserves the entire

probability distribution from upstream (e.g., supplier lead time) to

downstream (e.g., inventory placement), ensuring that uncertainties

remain visible, consistent, and mathematically grounded. This

approach not only reduces the likelihood of costly miscalculations—

9.5. ON PROGRAMMABILITY 391

where a small underestimation can lead to stockouts or a slight

overestimation triggers inﬂated holding costs—but also accelerates

experimentation.

Diﬀerentiable programming is a paradigm that fuses the

ﬂexibility of general-purpose programming with the computational

techniques of automatic diﬀerentiation and gradient-based opti-

mization. At its core, this approach treats every step in a data

transformation pipeline as a function amenable to diﬀerentiation,

allowing parameters to be tuned via stochastic gradient descent (or

related optimizers) directly within the code that implements the

logic. Rather than relegating learning to an isolated environment

(e.g., exporting data to a specialized machine-learning toolkit), dif-

ferentiable programming makes “trainable code” a ﬁrst-class citizen.

Each transformation—from input parsing to ﬁnal forecast—can be

designed, annotated with parameters, and automatically updated

to minimize a chosen loss function. This paradigm goes beyond

purely numerical procedures: it weaves domain insights straight

into the parametric structure of the models, ensuring that these

structures remain interpretable and aligned with the underlying

business processes.

In supply chain, diﬀerentiable programming is particularly po-

tent because it bridges several critical gaps simultaneously. First,

supply chain data is notoriously sparse and heterogeneous; many

SKUs, locations, or time segments oﬀer only limited observations,

yet share higher-level patterns (e.g., weekly cycles, promotional

lifts). A diﬀerentiable program can explicitly model these pat-

terns through shared parameters—thereby improving statistical

eﬃciency compared to oﬀ-the-shelf black-box approaches. Second,

the very shape of the supply chain problem—its mix of relational

queries, array transformations, and random variables—can be en-

coded directly in a parametric, learnable script, rather than forcing

practitioners to patch together a “language-in-language” design.

This alignment not only streamlines iteration but also yields more

transparent models whose named parameters (e.g., “seasonal am-

plitude”, “port congestion shift”, “Black-Friday uplift”) resonate

with domain experts. Above all, by integrating learning loops into

392 CHAPTER 9. ENGINEERING

the normal ﬂow of data and decisions, diﬀerentiable programming

oﬀers a uniﬁed environment in which forecasting and optimization

can co-evolve with the actual business constraints—rather than

stand apart as a one-oﬀ or purely academic exercise.

Deterministic Distributed RNG is a programming paradigm

designed to reconcile two seemingly conﬂicting goals: leverag-

ing large-scale Monte Carlo simulations across multiple machines

while ensuring that every run—even across a distributed cluster—

produces bit-for-bit identical results given the same inputs. Put

diﬀerently, the approach guarantees that random draws, once

seeded, unfold in a predictable pattern throughout the dataﬂow

graph, irrespective of shifting workloads or dynamic machine as-

signments. Monte Carlo techniques often surpass a direct algebra

of random variables when the phenomenon in question resists an-

alytical decomposition; however, naive parallelization can easily

render the process non-deterministic, making it impossible to repro-

duce outcomes precisely from one run to the next. Deterministic

distributed RNG averts this pitfall through a carefully orchestrated

mechanism of pseudo-random seed management and partitioning,

thereby combining the ﬂexibility of simulation-based approaches

with the reproducibility vital for rigorous engineering.

This paradigm is indispensable in supply chain for two main

reasons. First, the domain is already rife with genuine uncertainty:

demand shocks, supplier delays, currency swings, and more. If the

software layer itself introduces its own randomness—generating dif-

ferent outputs on each re-run despite unchanged inputs—managers

cannot reliably diagnose issues or trust the decision-making pipeline.

Second, supply chain initiatives live or die by continuous iteration,

as practitioners frequently reﬁne their numerical recipes, replay

historical scenarios, and compare new results against old baselines.

Strict determinism spares them from chasing “heisenbugs” that

vanish when retested and ensures that any anomaly discovered a

week or a year later can be traced to the precise combination of

data and code that caused it. By preserving exact reproducibil-

ity in their simulations and forecasts, supply chain teams gain

auditability and conﬁdence needed to safely automate decisions.

9.5. ON PROGRAMMABILITY 393

9.5.2 Spreadsheets

The enduring success of spreadsheets in supply chain is no coinci-

dence. Spreadsheets oﬀer, within a single grid, a form of functional

reactive programming tightly coupled with vertical integration:

data storage, formula-driven logic, and a visual user interface coex-

ist in a uniﬁed environment. The result is a near-perfect feedback

loop—akin to a rapid REPL

—that fosters a gentle learning curve.

Supply chain practitioners can start with a few cells, tweak a for-

mula, and instantly see the outcome of their adjustments. This

immediacy contrasts sharply with classical software engineering

workﬂows, which require code to be written, packaged, and then

deployed before any results can be observed.

Spreadsheets’ programmatic versatility should not be underes-

timated. Within a matter of minutes, a user can assemble ad hoc

logic for forecasting, inventory consolidation, or what-if analyses.

The barrier to entry is low; supply chain managers do not need to

become database administrators or software engineers to handle

many day-to-day calculations. Even as vendors have repeatedly

attempted to encapsulate forecasting or optimization in “ready-to-

use” analytical modules, spreadsheets have consistently remained

popular. This phenomenon arises because the spreadsheet’s func-

tional reactive grid can accommodate edge cases and “cable-like”

complications

more ﬂexibly than rigid oﬀ-the-shelf solutions.

Despite these strengths, spreadsheets are fundamentally at odds

with the true automation of supply chain decision-making. Practi-

tioners often cite a “lack of scalability” as the reason spreadsheets

fail beyond a certain data volume—especially when a workbook

is shared via local hosting on a single machine. Although hosting

spreadsheets remotely can alleviate some performance burdens,

REPL stands for Read-Eval-Print-Loop, a programming environment in

which code can be typed, immediately executed, and the output returned in

real time.

See earlier discussion of how materials such as cables already defeat most

oﬀ-the-shelf inventory tools, indicating how minor-looking quirks often break

rigid analytical models.

394 CHAPTER 9. ENGINEERING

it does not address the deeper, structural limitations. Two such

limitations, in particular, thwart the prospect of robust supply

chain automation.

First, over time, spreadsheets become riddled with extensively

duplicated logic scattered across dozens—sometimes hundreds—

of tabs and workbooks. Every time a user copies a formula or

re-creates a similar worksheet for a slightly diﬀerent SKU or loca-

tion, the structure diverges a little more. Eventually, no one can

conﬁdently trace which cells drive the ﬁnal outcome. Even minor

modiﬁcations become hazardous, potentially breaking dependen-

cies hidden in cell references. Once multiple people collaborate,

these problems multiply. Worse, spreadsheets typically lack the

robust versioning, modularity, and auditing features needed to

keep track of a fast-evolving codebase.

Second, supply chain intricacies—multi-sourcing, container-

ﬁlling constraints, lead-time variability, partial bills of materials,

multi-echelon hierarchies—rarely ﬂatten into neat two-dimensional

grids. Spreadsheets forcibly “slice” each dimension down to rows

and columns, complicating even moderate joins or cross-references.

For instance, a single SKU that depends on two suppliers with

diﬀerent cost structures and lead times may require contrived cell

expansions, plus numerous “auxiliary” tabs that try to simulate

a relational model. The more a supply chain requires branching

logic or multi-table queries, the more unwieldy the spreadsheet

becomes.

Beyond these immediate barriers, spreadsheets also fare poorly

against the programming paradigms most conducive to modern

supply chain engineering:

•

Time-travel reproducibility becomes nearly impossible with

large spreadsheets: while ﬁle versions can be saved, each

user action modiﬁes the shared document, often without

precise control over which cells changed and why. Debugging

“heisenbugs” after the fact is nearly hopeless.

•

Algebra of random variables, diﬀerentiable programming,

or deterministic distributed RNG cannot be practically em-

9.5. ON PROGRAMMABILITY 395

bedded in typical grid formulas. At best, practitioners tack

on external macros or third-party add-ins, but these hacks

seldom achieve robust, production-grade workﬂows.

•

LLM instrumentation—allowing Large Language Models to

generate or refactor code automatically—falters with the

inherent two-dimensional logic scatter. Most LLM-based

approaches assume a more structured textual environment

where logical relationships are explicit, not buried in cell

references and ephemeral macros.

In principle, spreadsheets could attempt to address some of

these shortcomings—for instance, by layering more powerful com-

putational engines underneath the grid. However, the spreadsheet

paradigm itself remains unaltered: it is designed for human interac-

tivity and ad hoc exploration, not for systematically handling the

ever-shifting complexities of large-scale ﬂows. The moment a com-

pany commits to continuous, unattended decision-making—over

thousands or millions of SKUs, across hundreds of locations—the

cracks show. The more these cracks are patched by short-term

ﬁxes, the more convoluted the workbook becomes, until no one

can trust the fragile ediﬁce it is.

As a result, spreadsheets, while undeniably central to supply

chain practice for decades, represent a technological dead-end.

Their downfall is not merely a matter of “more software” or “faster

hardware”. Rather, their entire design—grid-based formulas, man-

ual duplication of logic, minimal auditing support, and an implicit

focus on local user edits—runs counter to the paradigms needed

for large-scale automation. Precisely because spreadsheets excel

at one-oﬀ or small-scope tasks, they fail to pave the way toward

robust, fully automated supply chain processes.

9.5.3 Generic languages

For all their limits, spreadsheets point to a broader truth: supply

chain requires ad hoc programming that no static, “packaged”

system can satisfy. In principle, any general-purpose (“generic”)

396 CHAPTER 9. ENGINEERING

language could meet the need, especially now that modern lan-

guages are largely multi-paradigm. By stacking libraries, a team

might reproduce the essentials for supply chain: time-travel repro-

ducibility, array programming, diﬀerential execution, and more.

Yet in practice, the path consumes more eﬀort than it returns.

First, generic languages breed accidental complexity. Object-

oriented patterns, for example, become needlessly elaborate when

modeling mostly tabular supply-chain data. Left unrestricted,

network or system calls invite security issues and so-called “supply-

chain attacks”—inﬁltrations not in the company’s code but in a

third-party package it depends on. Even purely mathematical

operations fracture into a thicket of tiny modules. The deeper

a team sinks into this ecosystem, the more time it spends on

intricacies that have little to do with allocating scarce supply-chain

resources.

Second, relying on a generic environment eﬀectively commits

the company to a full-scale software project. The storage layer

must be selected or built and integrated; the compute layer must

scale for large volumes or SKUs; the presentation layer must be

coded or adapted for supply-chain audiences. This sequence of

choices, each anchored to a diﬀerent technology, makes the solution

harder to maintain. Any small shift—a new database version, an

unmaintained library—can cascade into incompatibilities. Over a

few years, “software rot” sets in and siphons resources from actual

supply-chain improvements.

Third, the overhead in such a project almost always fragments

expertise. A small cadre of software specialists—typically with little

supply-chain background—ends up implementing and maintaining

the code. Meanwhile, the actual practitioners—those who grasp the

knotty quirks and countless other domain-speciﬁc complications—

are sidelined. They must propose changes indirectly, submitting

“tickets” and routing requests through layers of mediation. Agility

evaporates as the lag from discovery to deployment widens. In

extremis, the solution grows so specialized that only its original au-

thors can patch or extend it—a brittle arrangement when personnel

changes or reorganizations occur.

9.5. ON PROGRAMMABILITY 397

Fourth, while generic languages can in theory support advanced

paradigms such as diﬀerentiable programming or deterministic

Monte Carlo, they seldom do so in a cohesive fashion. The relevant

libraries eﬀectively become “languages within the language”. One

library manages distributed dataﬂows; another handles random

variables; yet another attempts time-travel data versioning. Each

of these subsystems demands its own conﬁguration, data struc-

tures, and usage patterns, compounding the cognitive overhead.

What was meant as a single decision-making pipeline splinters into

separate toolchains, any of which can split at the seams.

None of these challenges is insurmountable: with enough talent,

money, and time, the right team can craft a workable solution atop

a generic language. Some of the largest retailers and manufactur-

ers do exactly that. Yet this route is so resource-intensive that

only ﬁrms with tremendous scale can sustain it, and even then it

remains fragile. Enhancements in large language models may au-

tomate fragments of these workﬂows—for instance, by generating

scaﬀolding or refactoring older libraries—but they do not resolve

the mismatch between a free-form language and the specialized

needs of supply chain. Paradoxically, better LLMs strengthen

the case for domain-speciﬁc environments: the more adept they

become at writing and reviewing code, the more a streamlined,

supply-chain-oriented language beneﬁts from their assistance.

In short, no absolute technical barrier prevents a generic lan-

guage from powering a large-scale, fully automated supply-chain

process. Yet mounting an initiative this way imposes overhead and

complexity ill-suited to most organizations. Because supply chain

exists to harness optionality rather than drown in it, layering mul-

tiple paradigms and libraries—none of which natively address the

ﬁeld’s pressing needs—usually proves a detour, not a destination.

398 CHAPTER 9. ENGINEERING

Chapter 10

Deployment

Supply-chain deployment is when the previous chapters—economics,

information, intelligence, engineering, and decisions—become a

machine that makes sound choices daily. The machine is a system

of intelligence that converts authoritative records into unattended,

auditable ﬂow commitments. The objective is neither “better

visibility” nor a nicer forecast: it is to issue purchase orders, trans-

fers, schedules, and price moves that are economically sound by

construction, and to improve them in place.

The deliverable is operational: a working deployment ingests

raw extracts from systems of record, carries a money-denominated

objective under probabilistic uncertainty, and writes decisions

back into the ledgers. Its presence is marked by emissions—lines

created in purchase-order tables, stock transfers booked, prices

posted—without a planner shepherding each line through a spread-

sheet. Anything that stops short of unattended emissions consti-

tutes project theater.

Three properties make this system safe and productive in

practice. First, ownership: one small team—ideally a named

individual—holds end-to-end responsibility for the numerical recipe,

the code that frames options, prices trade-oﬀs, and produces daily

commitments. Second, transparency: every emission comes with a

dossier that exposes the main economic drivers, so practitioners

399

400 CHAPTER 10. DEPLOYMENT

can see why the decision is sane and ﬁnance can audit it in coins.

Third, reversibility: the engine is stateless and guarded by halting

heuristics; it can be dual-run, promoted, or rolled back within

hours, and it halts when conﬁdence drops.

The path to that state is not mysterious, but it is unforgiving.

It begins with raw daily extracts—unﬁltered, schema-faithful, and

stable; continues with a ﬁrst recipe that runs daily next to the

manual routine until no insane lines remain; and culminates in a

progressive rollout that retires spreadsheets rather than wrapping

the engine in them. Scope is set to capture ﬂows that compete

for the same scarce resources; roles are trimmed to what the work

requires; governance is economic, not ritual.

Most of what derails deployments is already familiar: pilots

kept in read-only mode that never write even a single ﬂow com-

mitment back into the ledgers, partial datasets that hide scale

eﬀects, cargo-cult parameters copied from legacy tools, committees

that debate forecasts instead of decisions, and procurement rituals

that privilege the vendor best at paperwork over the one best at

decisions. Avoiding these traps requires the same stance advocated

throughout this book: frame the problem in money, surface options

explicitly, carry uncertainty faithfully, and let unattended software

do the clerical work.

This chapter is a manual for that stance in production: it shows

how to pick a partner when one is needed; how to frame scope so

the engine touches real constraints quickly; how to structure the

data pipeline; how to write, instrument, and harden the recipe;

how to dual-run and promote to unattended; and how to keep the

system healthy once live, while roles and incentives adjust. The

aim is not to add another reporting layer, but to replace manual

routines with a capital asset that issues, audits, and improves

decisions at machine pace.

Because many ﬁrms will not build everything in-house, we

begin with the practicalities of selecting a vendor. The method

recommended here—adversarial market research—ﬁts the opacity

and incentives of software better than RFP theater, and it aligns

with the economics-ﬁrst posture defended in earlier chapters. Once

10.1. SELECTING A VENDOR 401

the partner is chosen, we turn to setup and scope, the numerical

recipe, system eﬀects, roles, timeline, and the mechanics of rollout

and maintenance.

For brevity, the following terminology is used consistently. A

system of intelligence denotes the deployable asset that converts

authoritative records (plus a few vetted external feeds) into unat-

tended, auditable ﬂow commitments written back to the ledgers.

Its logical core—the program that frames options, prices trade-oﬀs,

carries uncertainty, and emits lines—is the numerical recipe.

10.1 Selecting a vendor

The complexity of modern supply chains far exceeds what can

be done without resorting to software. Most lack the means or

the inclination to develop those instruments in-house—even if the

open-source landscape greatly diminishes the ﬁnancial burden of

such an undertaking. Thus, supply-chain software vendors abound,

and most companies, past a certain size, regularly face the prospect

of selecting one.

Yet the software industry exhibits extraordinary variety while

remaining relatively opaque. The opacity is multidimensional:

capabilities, performance, licensing fees, operating costs, reliability,

maintainability, evolvability, security, and more.

The open-source landscape should, in principle, be more trans-

parent, since the source code can be freely inspected, but in prac-

tice there are two major caveats. First, the technical expertise

to reliably assess a given open-source technology is seldom found.

Assuming that an open-source technology is sound only because it

originates from a well-known source—e.g., a big software vendor—is

a time-tested recipe for disaster. Vendors abandon open-source

projects all the time because they have little to lose. Second, the

open-source landscape deals chieﬂy in components rather than so-

lutions—especially in enterprise software. This leaves the company,

if it wants to leverage those components, rolling out what amounts

to an in-house software project.

402 CHAPTER 10. DEPLOYMENT

Thus, for better or worse, companies end up picking software

vendors under high uncertainty. Yet software, like most intellectual

undertakings, exhibits a wildly uneven range of contributions,

from abysmally dumb to peak human ingenuity. Combine the

inherent opacity of software with a vendor gamut that includes a

thick “dangerously—if charmingly—incompetent” segment, and

it becomes imperative for companies to pay close attention to the

vendors they pick.

To their credit, most large companies now acknowledge the

problem and spend considerable eﬀort on market research for soft-

ware vendors. No other area attracts as much market-research

spend as software. By way of anecdotal evidence, a casual obser-

vation of market research ﬁrms reveals that software verticals are

literally dwarﬁng all other verticals put together.

Yes, most software market-research eﬀorts borrow methods

from other verticals; those methods are dysfunctional for software.

Case studies and Request For Proposals are the chief oﬀenders.

Software requires a diﬀerent approach: adversarial market research.

The pitfalls of case studies—often presented as evidence in

vendor selection—were examined in Chapter Epistemology. This

section focuses on RFPs and a more reliable alternative: adversarial

market research.

10.1.1 Request For Proposals

Request For Proposals

(RFPs) are a bureaucratic power play

within large organizations. For software, RFPs are neither rational

nor truthful nor informative. RFPs epitomize the bureaucratic

mindset: ﬁxation on process at the expense of outcome. In systems

of intelligence, the defects are fatal: checklists cannot certify unat-

tended decision quality, halting behavior, or rewritability under

pressure. An RFP steers the company toward incompetence. RFPs

Companies frequently distinguish RFIs (Request For Information), RFPs

(Request For Proposal) and RFQs (Request For Quotation). For concision here,

“RFP” refers collectively to RFI, RFP, and RFQ—all three equally dysfunctional

for the same reasons.

10.1. SELECTING A VENDOR 403

are too often treated as an inevitable plague in any organization

past a certain size. Yet nothing about this self-inﬂicted plague is

inevitable. Eliminating RFPs requires only modest fortitude from

top management.

Consider a company whose management faces a software chal-

lenge—or opportunity—beyond internal resources. After a few

informal exchanges with one or two vendors, management decides

a third party will address the challenge.

The RFP mythos purportedly unfolds in ﬁve steps. (1) The

challenge is ﬁrst put down in writing. (2) Employees then craft a

supposedly high-quality list of questions meant to elicit adequate

answers to that challenge. (3) The relevant vendors are identiﬁed

and the compiled RFP document is dispatched to them. (4)

Each vendor dutifully provides truthful, exhaustive answers, which

are gathered and collated. (5) Finally, management performs an

objective, accountable evaluation of the responses in order to pick

the best vendor.

Unfortunately, every step of this mythos is wrong—or delu-

sional.

(1) Writing, with clarity, the precise problem the software

should address is harder than writing the software itself. For the

uninitiated, this may sound puzzling. Yet most successful software

ventures arise not from superior solutions to known problems, but

from identifying a new problem altogether. Framing the problem

is an open-ended task. Formal processes rigidify thinking and

reduce the quality of the ﬁndings. Out of the near-inﬁnite ways

to look at a situation, one must pick the angle that lends itself to

implementation. Hundreds—possibly thousands—of factors must

then be technically integrated.

Corporate incentives further undermine the eﬀort. The growing

complacency of an aging executive may well be the crux of the

company’s problem, but no one will write this in a semi-public

memo to be shared with third parties. Such a memo will carefully

steer clear of anything that could be construed as an insult to

anyone’s professional capabilities within the company. Inevitably,

the memo beats around the bush—protecting egos rather than the

404 CHAPTER 10. DEPLOYMENT

company. Far from mere etiquette, this guarantees the selected

vendor will chase a solution that has little to do with the actual

problem.

(2) Having employees enumerate RFP questions invariably

produces material worthy of sketch comedy. Consultants add extra

hilarity points. While the goal should be questions that elicit

discriminative answers, the undertaking devolves into something

else entirely. Questions are produced to impress peers. Those who

lack smart questions compensate with volume. Questions from

past RFPs are recycled ad inﬁnitum.

Inane questions are bad enough; hundreds of them can derail

a nascent initiative. Worse, they warp vendor oﬀerings. Most

such questions are requirements in disguise. Far from open-minded

inquiry, those questions frame and severely restrict what counts

as a “solution”. Those questions usually read, “Can the vendor

guarantee that end users will be able to do X while interacting

with Y?” Answering no guarantees immediate disqualiﬁcation, even

when “doing X while interacting with Y” is provably a terrible

idea.

More generally, the there are no stupid questions mindset be-

longs in primary schools. Every question steers the initiative.

Inane questions yield inane answers. Moreover, every question

consumes a fraction of the mental bandwidth that the management

can dedicate to the initiative. As executives’ attention is spread

thin, it takes only a few bad questions to have them pick a vendor

on irrelevant trivia.

(3) Shortlisting vendors lets someone—often not even an em-

ployee (e.g., a consultant)—make the most momentous decision

of the RFP with zero accountability. In practice, the person who

shortlists the vendors implicitly picks the winner. The rest is

merely decorum, dictated by corporate etiquette.

While vendors may be numerous, shortlisting feels necessary

only because the question list is unreasonably long. Indeed, pro-

cessing that many answers for more than a handful of vendors

is a daunting task. That burden is a runaway consequence of a

dysfunctional approach.

10.1. SELECTING A VENDOR 405

(4) Vendors answering RFPs have no incentive to be truthful.

The vast majority of questions beg for a speciﬁc answer: Can your

solution do X? Any vendor worth his salt knows exactly what

answers are expected and responds accordingly. Expecting the

vendor to stick to “the truth” is delusional. Software is complex;

enterprise software even more so. Nearly all questions admit a

near-inﬁnite number of interpretations. The vendor need not lie; a

little imagination suﬃces to invent an interpretation that makes

any answer “truthful”.

Even making answers legally binding will not improve their

truthfulness. Enterprise software is obscure enough that almost any

statement can be construed as truthful—especially with capable

lawyers involved.

Such agreements—and other vexations imposed on software

vendors—make the situation worse for the company. Good vendors,

to some extent, pick their clients. Good vendors walk away when

a prospect makes unreasonable requests. Bad vendors—desperate

because their technology is found wanting—gladly embrace what-

ever bureaucratic insanity is pushed onto them; it is their only

competitive edge. Thus, companies that take RFPs “seriously”

practice adverse selection. Good vendors bow out; bad vendors

persevere.

(5) Finally, a software vendor must be picked. By then, a

huge pile—slanted answers meeting irrelevant questions—has been

collected. All this assumes the original problem statement was cor-

rect—a premise of dubious merit. The RFP perspective supposes

upper management can, by sheer intellect, pick the best vendor.

Nonsense. In practice, it is usually worse.

Often, positive selection never happens. Vendors are vetoed

out one by one—until only a single (viable) vendor remains. A

veto requires only a suﬃciently inﬂuential manager to declare

himself “concerned”. Whether the concern is reasoned or merely

a preference matters little. So long as he doesn’t veto multiple

vendors at once (perceived as abusive interference), his objection

grants him de facto veto power. Larger companies are risk-averse.

Backing an initiative ﬂagged as risky by a peer puts one’s career

406 CHAPTER 10. DEPLOYMENT

in jeopardy if it fails.

This ﬁnal selection oﬀers no guarantee of a good vendor. On

the contrary, mediocrity is favored. A good vendor is likely trans-

formative. After all, substantial returns on investment require

departing from the status quo. Such departure invariably antag-

onizes barons whose ﬁefdoms are challenged. Those barons will

vehemently oppose such vendors. A mediocre vendor is anything

but transformative: he embraces the status quo and ruﬄes no feath-

ers. Unfortunately, this guarantees that, at best, such a vendor

delivers little or no return.

Thus, RFPs are deeply dysfunctional. They also generate con-

siderable busywork: hundreds of questions to write and thousands

of answers to survey. Hence consultants are hired to drive RFPs

Invariably, consultants make things worse: the company’s excess

busywork is their source of income. Inﬂating the busywork is sold

as “process excellence”. Obviously irrelevant questions are retained

to ensure “end-to-end coverage.” Shortcuts that would expedite

the process are dismissed as “unsafe” or “untested”. At the end,

consultants almost invariably lean toward the vendor oﬀering the

richest consulting prospects.

10.1.2 Adversarial market research

Adversarial market research directly addresses two fundamental

issues in software projects: adverse incentives in vendor–client

dynamics, and massive information asymmetry favoring vendors.

It is less bureaucratic—and thus leaner—and more rational—and

thus likelier to match the right vendor to the right challenge.

The method works as follows

. As the manager recognizes

a software opportunity beyond the company’s resources, he asks

one vendor—typically the one that triggered the realization—to

produce a short problem statement on his behalf. The problem

As a litmus test, any consultant agreeing to support an RFP for enterprise

software should be dismissed outright for dangerous incompetence.

Loosely inspired by Warren Buﬀett’s “silver bullet” question: If you could

get rid of one competitor, who would it be?

10.1. SELECTING A VENDOR 407

statement should focus exclusively on framing the challenge and

the company’s speciﬁcs—not the vendor’s solution.

If the vendor cannot resist teasing his oﬀering, the manager

should simply edit out those parts; the document is short anyway.

If speciﬁcs—such as ﬁgures characterizing its supply chain—are

too conﬁdential, make them vaguer, e.g., by rounding numbers.

The goal is to preserve the essence of the challenge, not its ﬁne

print. The resulting memo neither names the originating vendor

nor contains overly sensitive information.

With this short document in hand, the manager reaches out to a

few of the vendor’s competitors. Guesswork is acceptable in picking

those peers, provided they are plausible alternatives. For each

competitor, the manager sends the memo, noting that it is a rough

draft, that he may have misframed the challenge, and that he seeks

expert guidance to reformulate the problem statement. He also

requests two further items from the vendors—including the original.

First, an informal back-of-the-envelope proposal aligned with the

vendor’s revised problem statement. Second, a list of at least three

relevant rivals, ranked by the vendor’s own assessment. Each listed

rival must be accompanied by a few paragraphs explaining its

perceived strengths and weaknesses.

As rivals’ shortlists reveal new competitors, those are added

to the survey. Any vendor that fails to deliver these modest items

within two weeks is removed from the survey. The survey ends

when the manager circles back to the same vendors and no stone

seems left unturned.

Once the survey has ended, the manager collates the results

and creates a short ﬁle for every vendor. Each ﬁle includes what

peers have said, together with the vendor’s problem statement and

its associated back-of-the-envelope proposal.

Finally, with at most two colleagues, the manager reviews the

results to pick a vendor. The selection is a matter of judgment and

cannot be rigidly formalized. Naturally, peer opinion should play

a major role in assessing each vendor. Furthermore, the quality

of its problem statement and of its rival descriptions should be

treated as telltale signs of competence—or incompetence if they are

408 CHAPTER 10. DEPLOYMENT

poor. Back-of-the-envelope proposals are used as tiebreakers. For

auditability, the survey result are archived alongside a short memo

explaining the reasoning behind the ﬁnal selection. Afterward, the

company resumes its regular purchasing process, which will likely

entail obtaining a detailed oﬀer and a full-ﬂedged contract from

this one vendor.

As stated previously, adversarial market research addresses the

shortcomings of traditional methods such as RFPs (requests for

proposals). Let us examine this claim, starting with information

asymmetry.

Letting the company compose its own problem statement, as

with RFPs, is a recipe for getting vendors to lie through their teeth

to make their solutions appear to ﬁt the problem statement. As

enterprise software vendors know far more than their clients about

their real capabilities, it is almost impossible to sort truth from

pretense. It is unreasonable to expect any vendor to fundamentally

change its technology to suit a client. Good software technology

takes years to build

. With each client, the vendor may do a little

better than in past initiatives, but the process is tediously slow

and incremental.

This is why it is critical to let the vendor compose the problem

statement. When the vendor holds the pen, it has no incentive

to craft a problem statement that misﬁts its technology. On the

contrary, it will shape the problem statement as close as possible

to the solution it possesses. Furthermore, vendors—except perhaps

the most incompetent—are vastly more knowledgeable than the

client in framing the problem. Indeed, what the client sees as one

problem may be a series of subproblems better addressed separately

with distinct technologies. Conversely, this “one problem” may be

merely the tip of the iceberg, requiring a broader perspective and

an entirely diﬀerent technological approach. A competent vendor

will demonstrate mastery by presenting a convincing rationale for

Joel Spolsky, a famous American Software Entrepreneur, published an

article “Good Software Takes Ten Years. Get Used To It.” (2001). While this

very broad statement is debatable, one must recognize that, a quarter of a

century later, it remains essentially true.

10.1. SELECTING A VENDOR 409

viewing the challenge through the right lenses.

Letting vendors expose their peers addresses the selection bias

that plagues RFPs. The initial vendor selection is one of the most

important decisions in the whole initiative. Early elimination of a

few vendors all but guarantees a privileged vendor will be selected.

So long as the survey includes half a dozen vendors or more, few

will question the adequacy of the selection. This fragility is an

open secret and is abundantly exploited in real-world RFPs.

By contrast, it is much harder to game the aggregate list

produced by the vendors. Each vendor has its own biases and

may intentionally omit a peer deemed too threatening. Yet the

lack of coordination makes it diﬃcult to single out any one vendor.

Moreover, every vendor knows that its projected expertise is at

stake. It will be assessed by the quality of the peers it provides.

Returning to the client with a shortlist of pseudo-rivals—either

incompetent or irrelevant—marks the vendor itself as incompetent.

Some vendors will produce all sorts of excuses for why they

cannot provide a list of peers, let alone qualify them. Yet in a

free market, a company cannot plausibly be “state of the art”

while wholly ignoring what its rivals are doing. This is doubly

true in software, where progress is extremely incremental. Vendors

oblivious to their peers are common; it is a telltale sign of utter

incompetence and should be treated accordingly.

Bypassing that entire question-and-answer game is a feature,

not a bug. Those questionnaires invariably devolve into a bureau-

cratic monster. Irrelevant questions and requirements in disguise

end up dominating the process. They frustrate the good vendors

and empower the bad ones. There is simply no value in playing

this game. Incentives are wrong, and neither the inputs (client’s

questions) nor the outputs (vendor’s answers) can be trusted.

Similarly, the insistence on back-of-the-envelope estimates is

intentional. Enterprise software is not an exact science. A compe-

tent vendor does not need a lengthy audit to draft a reasonable

estimate of the total cost of ownership for a solution of its own

making. Inability to provide such an estimate should be seen as a

telltale sign of incompetence. At a minimum, the estimate should

410 CHAPTER 10. DEPLOYMENT

cover the vendor’s fees, fees to supporting parties, and the cost

of internal resources dedicated to the solution. One of the most

frequent shenanigans in mis-estimating costs is turning a blind

eye to ancillary costs beyond the vendor’s fee. While this process

cannot prevent a vendor from omitting a few lines, the lack of

cooperation between vendors ensures fairly extensive coverage of

expected expenditures.

By contrast, traditional RFPs coerce vendors into mind-boggling

lists of inane requirements. Good vendors know that weeding this

nonsense will take considerable eﬀort and that, more often than

not, they will end up implementing numerous capabilities of dubi-

ous merit. As a result, vendors invariably inﬂate their fees when

confronted with a traditional RFP. They will also gladly turn a

blind eye to every cost not directly attributable to them unless it

is explicitly mentioned in the RFP.

Finally, peer assessment is the one piece of information that

truly separates wheat from chaﬀ. Every piece of software has

strengths and weaknesses, but no vendor—except perhaps the

most incompetent—will willingly expose the worst ﬂaws of its own

solution. When pressed, minor concerns are put forward as a show

of fake humility. Misdirection is straightforward when dealing with

something as complex and opaque as enterprise software. Rivals,

by contrast, will not hesitate to point out the bad and the ugly.

Given the nature of enterprise software, accusations or disparaging

statements are unnecessary. Instead, a vendor can simply nudge

the client to examine a few well-chosen aspects of a rival’s oﬀering.

Moreover, bringing relevant items to the client’s attention is, in

itself, a demonstration of competence.

Adversarial market research empowers good vendors. They can

train the client in the art of assessing vendors. They can frame

the challenge to maximize returns when deploying the solution.

They can oﬀer lower prices, as they do not have to wade through

overbearing bureaucratic insanity. The only downside is that it

often demands more fortitude than complacent managers possess,

as they are unwilling to ruﬄe the feathers of their own bureaucratic

apparatus.

10.2. SCOPING THE DEPLOYMENT 411

Once a vendor passes this adversarial survey and is selected,

work returns to deployment proper: setting up raw extracts, au-

thoring the ﬁrst numerical recipe, and dual-running until no insane

lines remain.

10.2 Scoping the deployment

In practice, “the same scarce resources” means a shared inventory

pool; shared upstream capacity (molds, MOQs, rebate ladders,

credit lines); a shared transport bottleneck (lane, port pair, vehicle

class); and—routinely forgotten—a shared pocket of managerial

attention. When embarking on a new supply chain initiative, the

ﬁrst task is to clarify its ultimate purpose and the boundaries

within which it will operate. A company might, for instance, be

interested in improving production scheduling or reorder quanti-

ties—or it might wish to unify several decision processes that have

historically run on fragmented spreadsheets. Yet in all cases, the

transformation must be made explicit at the outset: which deci-

sions will be altered, who will own them, and how these decisions

will be carried into daily operations?

One critical insight: an initiative aimed only at improving “vis-

ibility” or producing incidental metrics cannot, by itself, capture

lasting returns. All those visibility eﬀorts—even the most sophis-

ticated dashboards—remain hollow if people in the organization

carry on with the same step-by-step, manual decisions that pre-

dated the initiative. Conversely, once the goal is stated as a system

of intelligence—one that regularly outputs and enacts tangible

actions like purchase orders or capacity allocations—every require-

ment becomes simpler to articulate. This focus on producing—and

trusting—a daily or weekly decision in a live environment forces

the company to align roles and resources around a deliverable

anchored from the start in the economic realities of the business.

A workable rule of thumb follows: If multiple stores draw from

one distribution center, include all those stores in one go; otherwise

the engine will arbitrage across spreadsheets rather than across

412 CHAPTER 10. DEPLOYMENT

options. If two factories or two DCs (Distribution Centers) are

truly disjoint—no common inventory pool upstream, no shared

trunk lanes downstream, no shared buying team—stage them in

separate waves. If a supplier’s mold, MOQ, or rebate ladder binds

items together, bring the whole family into scope at once. If a

trunk lane or port pair is the bottleneck, scope every ﬂow that

rides it. If the same planners must arbitrate the same budget or

credit line, keep their domain together: managerial attention is as

ﬁnite as pallets and hours.

Deciding how broad or narrow the scope should be is no less im-

portant. An initiative might succeed in automating replenishment

for a single plant but inadvertently shift problems elsewhere—such

as creating bottlenecks downstream or mismatched pricing up-

stream. When the objective is sound daily decisions, looking at a

single product line or a handful of warehouses often proves mislead-

ing. While it may appear “safer” to start small, deep structural

interdependencies frequently render these localized pilot runs non-

representative. A narrowly deﬁned project can be so constrained

that the company never sees how the initiative performs under the

true complexity of its operations. On the other hand, an overly ex-

pansive scope can sink the project under years of data-integration

delays. The right balance, in practice, captures all signiﬁcant ﬂows

that compete for the same resources—inventory, transport, or pro-

duction facilities—while avoiding optional detours into functions

with little impact on supply chain decisions.

Equally crucial is establishing a minimal set of well-deﬁned

roles. Despite the myriad job titles found in a large supply chain,

only a handful of positions need to be responsible for supporting an

automated decision process. The Data Oﬃcer maintains the steady

extraction of raw transactional data—ideally an unﬁltered dump—

so subsequent preparation can remain ﬂexible and transparent. A

Scientist then takes ownership of the technical and analytical stack

leading from raw data to the ﬁnal recommended decision. A day-to-

day Flow Manager—formerly a planner in pre-automation routines—

contributes contextual knowledge, challenging outputs that seem

suspect and clarifying on-the-ground constraints. Finally, a supply

10.2. SCOPING THE DEPLOYMENT 413

chain executive stands ready to arbitrate when the initiative meets

roadblocks—ensuring that if a new distribution center or software

tool is introduced, the modeling behind the decisions is revised

accordingly rather than left to drift.

All these elements—explicit decision focus, well-chosen scope,

properly deﬁned roles, and a ﬁnite but ﬁrm onboarding schedule—

pave the way for a smooth initiative. They also make it evident

that the object of interest is not a formula or a theoretical optimum,

but a living recipe, updated and rerun in a shifting environment

of suppliers, customers, and operational changes. Once that foun-

dation is set, the next step is to examine its core: the numerical

recipe at the heart of the system of intelligence, translating every

relevant piece of data into the concrete, everyday decisions that

steer the ﬂow of goods.

This section revisits how to frame a supply chain initiative to

produce the daily (or weekly) decisions needed to run a supply

chain while it secures the “soft” elements (roles and leadership)

that ensure alignment with corporate goals.

10.2.1 The numerical recipe

For deployment—and only for scope—the numerical recipe is best

seen as a contract at the boundary of the initiative. It states in

code three items scoping must settle upfront: the decisions the

engine may emit (and the ledgers they write), the options it is

allowed to consider (and those kept exogenous), and the shared

resources it prices internally versus reads as external shadow prices.

Everything else—forecasting stance, decision theory, debugging

“insane” lines—has been treated in earlier chapters and is not

repeated here.

First, the decisions: a scoped recipe names its emissions pre-

cisely. For replenishment: lines inserted into the purchase-order or

transfer-order tables; for production: start times and quantities

on a ﬁnite set of work orders; for pricing: posted price changes on

an agreed cadence. Emissions must be idempotent and stateless:

the same inputs produce the same outputs. The write-back can be

414 CHAPTER 10. DEPLOYMENT

reconciled unambiguously by a decision identiﬁer and a timestamp.

This narrow declaration anchors scope in reality: if a decision

cannot be written back to the system of record, it is out of scope.

Second, the options: scope ﬁxes what the engine may vary and

what it must take as given. If the initiative covers DC-to-store

dispatch while pricing remains out of scope, the price is read,

not chosen; its eﬀects enter the objective via the ﬁrm’s money

ledger. If overseas ordering is in scope but carrier selection is

not, transport modes arrive as costs and lead-time distributions,

not as choices. Stating admissible moves prevents a relapse into

spreadsheet improvisations that expand scope informally and erase

accountability.

Third, the shared resources: scope is sound only if every bind-

ing resource inside the perimeter is either decided here or priced

here. Inventory pools, mold families under an MOQ ladder, dock

throughput, port-pair capacity, buyer credit lines, and even man-

agerial attention are typical binders. When a binder straddles the

boundary, the perimeter must ingest and honor a shadow price

supplied by the neighboring domain. Absent that price, the engine

arbitrages across spreadsheets rather than across options. Con-

versely, once a binder is brought inside, the external shadow price

is retired; the coupling is internalized.

A scoped recipe also carries attribution. Its window of respon-

sibility—from the instant a commitment immobilizes a resource

until the next–next opportunity to reassign or liquidate it—is ex-

plicit, with terminal valuations and ratchets stated. Within that

window, the engine bears coin-denominated consequences (lost-

sales penalties, overage costs, congestion, markdowns) for its own

emissions; beyond it, costs are priced as terminal values rather

than chased indeﬁnitely. This boundary prevents double counting

across initiatives and the familiar blame game between neighboring

teams.

Scoping requires halting and reversibility at the perimeter. The

engine must stop itself when inputs are stale, when exogenous

shadow prices go missing, or when dossier-level sanity checks fail.

Promotion to unattended mode depends on these halts being rare

10.2. SCOPING THE DEPLOYMENT 415

and diagnosable. Reversibility is achieved through statelessness

and deterministic replays: yesterday’s run can be reconstructed

from versioned code and versioned extracts without pleading “data

drift” or “user overrides”.

The data diet follows from scope. Tables are mirrored, in full

and schema-faithful, on a daily cadence (or faster if the decision

cadence demands it). No “helpful” upstream massaging is per-

mitted: ﬁlters, imputations, and joins that matter to the decision

live inside the recipe, where they can be audited alongside the

economics. Minimal whiteboxing—columns that expose the main

drivers of each emission in coins and constraints—also belongs

to the contract. This dossier lets ﬁnance and operations see at a

glance why a line is sane.

Two short examples make the boundary concrete. A DC-to-

store replenishment recipe scoped to a single DC emits transfer

orders for all stores fed by that DC, varies quantities and release

times, reads prices and planograms as exogenous, and internalizes

the DC’s labor and dock capacity. If another DC draws from the

same upstream pool, the recipe must either ingest a shadow price

for that pool or expand the perimeter to include the sibling DC;

anything in between invites cross-DC arbitrage through spread-

sheets. An overseas buying recipe scoped to a supplier family emits

purchase orders with lot construction, internalizes MOQ/rebate

ladders and the shared mold, reads carrier choices as priced in-

puts, and attributes downstream costs through a terminal value at

handoﬀ to the import DC. Pricing or store allocation remains out

of scope until a subsequent wave.

Finally, scope is expected to evolve. The contract provides

the seam: as neighboring domains are brought inside, exogenous

shadow prices are replaced by endogenous decisions without rewrit-

ing the core. In deployment practice, this is how scope “clicks

together”: the border is not a rhetorical slide but executable code

that names emissions, admissible moves, binders, prices, and halts.

The next subsection examines how these borders interact with the

rest of the system.

416 CHAPTER 10. DEPLOYMENT

10.2.2 The data pipeline

Scope becomes real only where it meets the applicative landscape.

Modern ﬁrms run on silos—ERP for transactions, WMS for lo-

cations, TMS for carriage, CRM for orders—each with its own

schema, cadence, and folklore. Scoping an initiative is therefore

not an abstract drawing of boundaries; it is the decision to cut a

clean, durable seam through those silos and to uphold it with a

data pipeline that is thin, honest, and reproducible.

The numerical recipe deﬁned in the previous subsection sup-

plies the logical perimeter (emissions, admissible options, shared

binders). The pipeline is its informational counterpart. It should

mirror the tables required by that perimeter on the cadence implied

by the decisions. In practice, a daily closing bell—an explicit cutoﬀ

(in UTC) after which extracts are taken—beats vague “near-real-

time” ambitions that blur provenance. Freshness is an economic

choice: accelerate only when the expected uplift in coins justiﬁes

the added friction; otherwise, favor a clean, once-a-day snapshot

that everyone can replay identically. Partial extracts and “pilot

slices” are counterproductive: they mask scale eﬀects, conceal se-

mantic quirks, and create a false sense of correctness on toy subsets.

The perimeter you intend to optimize is the perimeter you mirror.

Each extract must be immutable and self-describing. Package

tables with a small manifest that records the as-of watermark,

per-table row counts, min/max business timestamps, and a schema

ﬁngerprint. Stores are append-only: yesterday’s ﬁles are never

edited; retroactive corrections appear as new facts in today’s dump.

This discipline—together with time-travel—guarantees bit-for-bit

reproducibility: a run can be replayed months later without plead-

ing “the data moved”.

Deletions and late corrections demand explicit treatment. Pre-

fer full snapshots over clever deltas; when that is infeasible, normal-

ize any upstream change-data-capture into an append-only ledger

inside the perimeter (tombstones for hard deletes; versioned rows

for updates). The goal is uniformity: the recipe reasons over a sin-

gle, growing history of events, not over a moving target of in-place

10.2. SCOPING THE DEPLOYMENT 417

edits.

Access follows least privilege: a read-only service account pulls

extracts; the initiative never writes back into systems of record

through the pipeline. Minimize personally identiﬁable data; most

supply-chain decisions carry no legitimate need for it. Keep the

seam thin: extracts are facts, not opinions; transformations that

change decisions belong inside the recipe, where they are versioned

with the economics.

Historical depth should span at least one full replenishment

cycle and, whenever seasonality matters, two seasonal peaks. More

is better, but “enough to learn” suﬃces: an honest three to six

quarters is enough to let the recipe converge; backﬁlls can extend

depth later without derailing deployment. External feeds—public

holidays, price indices, weather for short-haul perishables—enter as

ﬁrst-class tables with their own cadence and provenance. Exploit

internal records ﬁrst; externalities pay only when their signal is

demonstrably stronger than the noise they introduce.

“Helpful” upstream massaging—bespoke ﬁlters, silent imputa-

tions, pre-joined views—turns the seam into fog. Transformations

that matter to decisions must live inside the recipe, where they are

versioned alongside economics and can be audited with the result-

ing emissions. Records remain hypotheses about reality; keeping

them raw preserves provenance and lets defects be traced back

to their source system rather than to an undocumented cleansing

script.

Because applicative silos will not be re

platformed to suit the

initiative, scoping must respect their couplings without attempting

to dissolve them in advance. When a binding resource sits inside

the perimeter, its constraints are internalized; when it straddles

the boundary, its inﬂuence arrives as a shadow price from the

neighboring domain. Attempting to “simplify” by deleting incon-

venient data—or by isolating a sliver that no longer competes for

shared inventory, capacity, lanes, or attention—reintroduces the

very arbitrage across spreadsheets the initiative is meant to retire.

Technologically, the pipeline should be boring: append-only

snapshots, immutable ﬁles, manifest-guarded extracts, and time-

418 CHAPTER 10. DEPLOYMENT

travel to replay any past run bit-for-bit. This modest discipline

makes halting and reversibility—required by the recipe’s con-

tract—eﬀective in practice. Storage is cheap; premature sum-

marization is not. The only worthwhile complexity sits where coins

change hands: inside the recipe, not in the plumbing that feeds it.

Once this seam is cut—faithful mirroring on one side, auditable

transformations inside the recipe on the other—scope can expand

without upheaval. New domains are brought inside by replacing

external shadow prices with endogenous decisions; nothing else in

the pipeline needs to be reinvented. The next subsection assigns

the few roles needed to keep this seam alive day after day.

10.2.3 Roles in the initiative

Once scope is clariﬁed and focused on a daily production of de-

cisions, the next critical step is to deﬁne responsibility for each

part of the process. Although companies often maintain many

specialized roles across supply chain, only a few are necessary to

support an automated decision-making workﬂow. Too many roles

simply dilute the ownership of the ﬁnal outcome. By contrast, a

small, well-deﬁned set of roles ensures accountability and keeps

the ﬂow from raw data to ﬁnal decisions clear and traceable.

The Data Oﬃcer sets up and maintains the data pipeline.

This role sits at the intersection of IT and supply chain but re-

mains ﬁrmly rooted in IT’s discipline. The Data Oﬃcer’s main

objective is to deliver raw, unﬁltered extractions from the systems

of record, such as the ERP, WMS, CRM, or other legacy applica-

tions. Delivering these datasets daily or weekly in reliable form is

harder than it looks: minor schema changes, partial upgrades, or

usage quirks routinely break extraction jobs. Once stabilized, the

pipeline requires minimal ongoing eﬀort. Critically, this role should

refrain from attempting to “ﬁx” or “interpret” the data. Whenever

a serious anomaly appears—like negative on-hand inventory or

missing transactions—the Oﬃcer should notify the relevant teams

but not overwrite or massage the underlying records. Thus the

initiative confronts the real data, not an unknown transformation.

10.2. SCOPING THE DEPLOYMENT 419

The Supply Chain Scientist owns the entire recipe from

raw data to daily decisions. This role is the logical core of the

initiative. While the Data Oﬃcer concentrates on extracting data,

the Scientist focuses on translating every cost driver, operational

constraint, or strategic preference into the recipe itself. Unlike a

typical data analyst or “data scientist”, the Supply Chain Scientist

is personally committed to making the process production-grade.

If a recommendation is blatantly incorrect, it is the Scientist’s

responsibility to track down the source of the error—be it a mis-

taken modeling assumption, an overlooked lead-time constraint,

or a mismatch with the company’s current ﬁnancial objectives.

Over time, the Scientist codes new insights directly into the recipe,

keeping it aligned with shifting operational realities. Although

the Scientist uses statistical tools, optimization engines, or sim-

ulation techniques, his guiding principle is to deliver workable

daily decisions. The diﬀerence in mindset is crucial: dashboards or

performance metrics may drift out of relevance; daily, automated

decisions cannot.

Next comes the Executive Sponsor, who provides top-level

guidance and arbitration. Supply chain decisions inevitably reﬂect

the company’s broader economic calculus, and the Scientist will

sometimes need to make modeling assumptions—about costs, mar-

gins, trade-oﬀs, or vendor terms—that only senior management

can validate. A single warehouse might request premium freight for

faster restock, for instance, but doing so may conﬂict with the goal

of reducing air shipments company-wide. The Sponsor clariﬁes

how trade-oﬀs should be prioritized if there is disagreement. He

also ensures the project has the necessary political and ﬁnancial

support. If the Scientist uncovers a new factor (like leftover prod-

uct warranties or legal constraints on cross-border shipments) that

calls for a deeper process change, the Sponsor’s direct involvement

helps clear organizational hurdles promptly.

Lastly, a Flow Manager (formerly a planner or inventory

manager in older processes) remains essential. Although the daily

decisions eventually become automated, real-world complexities

never vanish. The Flow Manager provides the day-to-day opera-

420 CHAPTER 10. DEPLOYMENT

tional insights that data tables cannot capture—for example, which

supplier tends to deliver partial shipments, or which packaging

lines show subtle quality issues during peak months. In the ﬁrst

months of dual-running the old spreadsheet and the new recipe side

by side, the Flow Manager spots decisions that look “insane” and

relays these observations. Over time, as automation proves stable,

the Flow Manager’s job transforms. Instead of micro-checking each

stock level, he focuses on broader relationships with customers

and suppliers, handles unexpected disruptions such as a ﬂooded

warehouse or an overnight regulatory shift, and channels that

knowledge back to the Scientist. Free from endless spreadsheet

updates, he acts as a network-wide steward of the ﬂow, heightening

resilience and responsiveness across supply chain.

In brief, ownership reduces to four verbs: the Data Oﬃcer

sources facts; the Supply Chain Scientist emits the daily commit-

ments—and halts the engine when conﬁdence dips; the Executive

Sponsor arbitrates trade-oﬀs and boundary disputes; the Flow

Manager monitors ﬁeld ﬂow and escalates anomalies.

These four roles ﬁt within a surprisingly compact structure,

even in large corporations. One person may serve as Data Oﬃcer

across multiple projects, and a single Scientist can manage hun-

dreds of thousands of SKUs. Clarifying at the outset that each

responsibility maps to a single, readily identiﬁable domain reduces

cross-department friction and avoids the endless hand-oﬀs and

partial accountabilities that often paralyze more ambitious (and

less focused) projects.

Once these roles are assigned, the path is set for a tangible,

roughly six-month onboarding cycle—from data extraction to a

stable, production-grade process.

10.3 Onboarding

Onboarding is the passage from scoped intent to unattended emis-

sions. Once the perimeter, the roles, and the seam of daily extracts

are in place, what remains is execution with explicit gates: give

10.3. ONBOARDING 421

the numerical recipe a daily rendezvous with reality; proceed only

when readiness criteria are met; and retire manual routines without

interrupting the ﬂow.

This section does not revisit scope, roles, or economics; those

have been settled above. Its concern is cadence and control: what

must become true, in what order, for the system of intelligence to

take charge safely. Dates follow from criteria—the calendar is a

consequence, not a plan.

The pages that follow take this path: a brief timeline naming

the gates; the daily practice that gets us there; the mechanics of

progressive rollout and reversibility; and the stance for long-term

upkeep. With these pieces, the transition from spreadsheets to

unattended decisions becomes routine rather than heroic.

10.3.1 Timeline overview

Once scope and roles are in place, onboarding proceeds through

three stages that, in most ﬁrms, ﬁt within roughly six months. This

horizon is an empirical expectation, not a promise: the calendar

reﬂects organizational cadence more than compute, and a slip of a

few weeks typically signals governance or scope issues rather than

“hard” technical limits.

The ﬁrst stage ends only when daily data extraction is complete

and reliable. The perimeter of tables required by the numerical

recipe is mirrored schema-faithfully at a clear daily cutoﬀ; extracts

are immutable and self-describing; past runs can be replayed bit-for-

bit; and upstream quirks are handled without “helpful” massaging

that would blur provenance. Halting heuristics are wired so that the

engine stops itself when inputs are stale or missing. In practice, this

stage is complete once the seam has run cleanly and reproducibly

for a sustained period, with defects traced and ﬁxed at the seam

rather than patched downstream.

The second stage ends when the recipe ceases to produce insane

decisions. With the pipeline stable, the numerical recipe runs daily

next to the incumbent routine. Each emission is accompanied

by a dossier that whiteboxes its main economic drivers in coins.

422 CHAPTER 10. DEPLOYMENT

Day after day, surprises are fed back into semantics, constraints,

or prices until glaring missteps vanish. The criterion here is not

perfection but the absence of insanity: the dual-run must sustain

zero insane lines, and any remaining diﬀerences from the manual

routine are matters of marginal economics, not logic errors.

The third stage ends when trust suﬃces for production use.

The very same recipe now writes back within a bounded perimeter,

guarded by halting heuristics and reversible by design: emissions

are stateless and idempotent; rollback and replay are routine; and

documentation enables another Scientist to diagnose and revert

within hours. Instrumentation is convincing enough for ﬁnance

and operations to audit decisions without meetings: dossiers are

legible, attributions are clear, and the window of responsibility is

explicit. After a short, clean unattended rollout over part of the

perimeter, promotion ceases to be a leap of faith and becomes a

formality.

These gates exist to prevent pilot purgatory. If a stage lingers,

escalate the cause—unﬁnished extracts, scope creep, or unowned

prices—instead of stretching the calendar. Conversely, rushing

past a gate without meeting its conditions merely displaces risk

into production. The pages that follow unpack the day-to-day

work inside each stage without rehearsing scope or roles already

settled.

10.3.2 Iterative deployment

Deployment operationalizes the experimental optimization method

introduced in Chapter Engineering: expose the numerical recipe

to the real seam every day; let falsiﬁcation drive the edits; and

progress only once the resulting decisions are sane. The aim here is

practical: how this stance is conducted in production, not a reprise

of its theory (cf. Chapter Engineering, Experimental optimization,

and Whiteboxing).

10.3. ONBOARDING 423

Daily dual-run

Each day at the closing bell, run the recipe end-to-end on the

full perimeter, with the same cutoﬀ and the same admissible

moves it will have in production. Emit decisions but do not write

them; archive both emissions and their dossiers; compare them

to the incumbent routine. The run is stateless and reproducible:

given the extract and the code version, the outputs are identical.

Halting heuristics remain active; any halt is treated as a seam

defect (missing tables, stale shadow prices, semantic drift) to be

removed before the next run. Dual

run is not a demo; it is the daily

laboratory where the recipe meets reality until it stops producing

surprises.

Zero insane decisions

“Insane” names any emission that violates a hard physical or legal

constraint; contradicts an elementary business fact; or carries a

manifestly negative expected return under the stated prices. The

absence of insanity—sustained over the whole perimeter—is the

gate out of dual-run and matches the second stage in the timeline

above. Marginal disagreements with the manual routine do not

block promotion; they are adjudicated by the ledger and settled in

the next section’s rollout.

Evolving instrumentation

To make falsiﬁcation fast, every emission carries a short dossier

that exposes its main economic drivers—in coins—together with

the few constraints that bind it. The dossier schema belongs to the

recipe and evolves with it; it is versioned, replayable, and written

by the same code that produces the lines. In dual-run, dossiers

let practitioners pinpoint the cause of a surprise in minutes; in

production, the same dossiers let ﬁnance and operations audit

decisions without meetings. The mechanics of whiteboxing were

developed earlier; here we keep only what accelerates deployment.

424 CHAPTER 10. DEPLOYMENT

When dual-run has held for a while with no insane lines and halt-

ing is rare and explained, the same recipe can begin to write back

within a bounded perimeter. The mechanics of that cutover—scope

selection, safety valves, and reversibility—are addressed next under

Progressive rollout.

10.3.3 Progressive rollout

Moving from daily parallel checks to active reliance on the system

of intelligence is an exercise in risk management. The transforma-

tion must be decisive enough that the company at large no longer

falls back on older manual methods at the ﬁrst sign of trouble, yet

cautious enough to avoid large-scale disruptions. Many organiza-

tions hesitate at this juncture—maintaining the new system as a

perpetual pilot—but an orderly, ﬁrm transition keeps the initiative

out of limbo.

The ﬁrst scope

A common way to begin is to select a tightly knit segment of the

supply chain and let automated decisions operate for that subset

alone. For instance, a single distribution center might follow the

daily recommendations for a speciﬁc category, or a manufacturing

facility might run the automated production schedule for a single

product line. Ideally, these products or facilities already have

stable data and well-understood constraints. By adopting the

recipe’s outputs in live operations (e.g., sending the suggested

purchase orders directly to suppliers), the organization can verify

that no “insane lines” appear under genuine operating conditions.

This initial transition typically lasts a week or two, long enough

to conﬁrm that basic cost, inventory, and lead-time assumptions

translate accurately into daily actions.

Once performance stabilizes, a broader portion of the network

can be brought in. For instance, if the ﬁrst wave covered one dis-

tribution center, the next may encompass all distribution centers

of the same region. With every extension, the Scientist and the

10.3. ONBOARDING 425

Data Oﬃcer must ensure that the same data-pipeline architecture

scales to the added locations or product lines without new com-

plexities. In parallel, the Flow Manager conﬁrms that no unique

local rule (such as a vendor’s strict full-pallet requirement) has

been overlooked. This pattern—select, conﬁrm stability, then ex-

pand—repeats until all relevant corners of the original scope are

covered.

Manual fallback phase-out

The harder task is deciding how—and when—to retire the old

manual steps. Many companies have attempted “semi-automation”,

in which planners review every system-generated recommendation

and adjust parameters by hand at the ﬁrst hint of a shortfall.

Prudent in appearance, indeﬁnite double-checks quietly erode the

gains. Nominal automation devolves into a gloriﬁed suggestion

engine, ignored the moment numbers contradict intuition. Every

hour spent second-guessing or retyping recommended orders is

an hour not invested in tasks a machine cannot handle—such as

forging supplier relationships or analyzing fresh market signals.

Truly capitalizing on automation requires retiring the older

process from active use. This decision may seem to require a

leap of faith. But by then, the daily comparisons should have

shown that the new method rarely, if ever, produces glaring errors.

When anomalies occur—supplier lead times suddenly spiking, an

unannounced change to a packaging standard—correcting them in

the recipe proves more systematic and faster than the spreadsheet

patchwork. Because the logic is centralized in code, a single ﬁx

corrects potential errors across SKUs and locations. This clarity

ultimately convinces planners and middle managers that adoption

saves time without ceding control.

Handling exceptions

A dynamic supply chain inevitably encounters cases the recipe did

not fully anticipate. Natural disasters might ﬂood a warehouse,

426 CHAPTER 10. DEPLOYMENT

or a supplier might default. The same automation that works

under normal conditions now needs a quick adjustment—either a

short-term override or a modeled constraint. Immediately after

the old manual process is switched oﬀ, everything that breaks feels

urgent. Surges in recommended purchase orders or an inability to

meet large batch sizes have to be tackled without the comfort of

the old spreadsheets.

The Scientist bears primary responsibility here. Because the

recipe is stateless—recomputing decisions fresh each day from

the entire dataset—changes can be introduced as soon as the

assumptions are clear. If a water-damaged facility must run at half

capacity, the Scientist can encode that limit the next morning. The

key is to integrate those exceptions into the uniﬁed code, rather

than let them reappear as local overrides in scattered spreadsheets.

Even after the crisis passes, the recipe retains that constraint,

ready if it recurs.

Measuring outcomes

As mechanical decisions roll out across the network, instrumenta-

tion expands in scope. Previously, instrumentation helped catch

erratic or incorrect lines; now it also assesses whether the predicted

gains in service, cost, or reactivity are materializing. The Scien-

tist—typically with ﬁnance and operations leads—compares actual

demand to forecasts and tallies realized margins and lead times

against modeled counterparts. That 360-degree view shows how

well the daily decisions hold up to real-world variability. Crucially,

these performance artifacts—such as accuracy measures or cost

comparisons—are intended primarily for the Scientist’s iterative

improvements, rather than fueling new layers of oversight. A new

gap between forecast and demand might signal a subtle seasonality

shift or a change in competitor pricing—an insight best folded back

into the recipe’s code. If those artifacts are shared widely across

departments, the organization risks returning to forecast-tweaking

by committee, diluting accountability.

The mechanical rollout rests on two concurrent forms of mea-

10.3. ONBOARDING 427

surement. First, the zero-insanity threshold: does the recom-

mended order or schedule remain obviously correct for the day?

Second, the longer-term actual-versus-projected checks: does the

approach reﬂect the business’s cost realities and revenue objectives?

Keeping both forms of instrumentation in the code base—visible

and revisable by the Scientist—lets the Scientist locate faulty

assumptions swiftly. The company retains a stable end-to-end

perspective, free of the confusion arising from partial trackers or

conﬂicting spreadsheets in separate departments.

Solidifying autonomy

Over several months of phased rollout and reﬁned instrumentation,

the initiative coalesces into a daily routine. Staﬀ who once scram-

bled through spreadsheets redirect attention to more nuanced tasks

or to immediate exceptions that inevitably arise in ﬂuid markets.

Conﬁdence in the automated system grows steadily. By the time

the entire scope runs under the new recipe, the manual approach

is not missed. Employees see that re-litigating every reorder deci-

sion is unnecessary, while managers note that changes in demand,

supply constraints, or even cost structures can be adjusted at the

recipe level swiftly and consistently.

Successful automation ignites a cultural shift. Once staﬀ real-

ize that one piece of supply chain has truly become mechanical,

they begin identifying new areas where a similar approach applies.

Even if those expansions fall outside the original scope, they can

piggyback on the pipeline, instrumentation, and iterative process

that have already proven themselves. Such momentum spares

the organization from the stagnation common after conventional

software rollouts, where the system is “frozen” as soon as it goes

live. Here the system’s daily outputs remain open to reﬁnement,

precisely because the capacity for quick iteration was baked into

the initiative’s structure from the beginning.

Transitioning to full automation is not the ﬁnal word. The next

question is how to sustain and improve this new normal, ensuring

the recipe keeps pace with shifts in corporate strategy or market

428 CHAPTER 10. DEPLOYMENT

conditions. While no one wishes to revert to spreadsheets once a

well-tested mechanical approach is in place, ongoing vigilance is

needed to prevent complacency. The following section explains how

to keep the initiative relevant amid continuous evolution, turning

each event—from everyday changes to black-swan disruptions—into

an opportunity to strengthen the automated process.

10.3.4 Long-term maintenance

A deployed system of intelligence is never static; however polished

its numerical recipe may appear, business conditions and opera-

tional realities keep evolving. New regulations may arise, pricing

structures may change, and lead times or service constraints may

shift in ways the automated recipe did not anticipate. An initiative

that stops adapting soon becomes as obsolete as the system it

replaced.

Maintenance, in this context, is not limited to software patches

or data extraction ﬁxes. Above all, it is about preserving the

correctness and relevance of the economic logic encoded in the

recipe. If the company opens a new distribution center or negotiates

a new supplier contract, the costs and constraints underpinning

daily decisions must be revised to reﬂect these developments. In

practice, failing to maintain the recipe means either reverting to a

litany of manual overrides that undermine automation or letting

it produce decisions increasingly divorced from the company’s

objectives.

Relentless evolution

One critical reason for systematic upkeep is that every link is

constantly evolving—new SKUs are introduced, old SKUs retired,

marketing campaigns appear, and unexpected disruptions force

alternate routes. If the recipe stays locked in last year’s assump-

tions, it will miss fresh opportunities and fail to mitigate newly

introduced risks.

10.3. ONBOARDING 429

This variability is not a ﬂaw of automation but a feature of real-

world markets. A robust initiative embraces change by scheduling

regular reviews of the recipe’s assumptions and surfacing anomalies

whenever the data pipeline detects large deviations from normal

conditions. A surge in freight costs might call for deeper modeling

of container capacities or a reevaluation of the favored transport

mode. Similarly, adding advanced manufacturing lines can alter

trade-oﬀs: batch sizes once optimized can become wasteful if new

lines oﬀer cheaper, faster setups.

Finance and operations

Coordinating updates to the recipe demands ongoing dialogue

between the Scientist and senior leaders in ﬁnance and operations.

Day-to-day maintenance can address minor issues—a new vendor

code or a recalibrated reorder policy—whereas signiﬁcant changes,

such as a new discount structure or a diﬀerent approach to working

capital, are inherently strategic.

For the recipe to remain aligned with the company’s current

ﬁnancial realities, it must reﬂect any shifting cost allocations or

budgetary constraints. Because ﬁnance controls how capital is

valued, the Scientist looks there for clarity on how aggressively or

conservatively to scale inventory, how to treat intangible overhead

(such as brand damage from a missed shipment), and how to

weigh shipping delays against the expense of urgent overtime. An

annual or semiannual joint review ensures the code continues to

reﬂect the company’s strategic direction rather than drift into stale

patterns. If upper management wants to prioritize expansion into

new markets or slash air-freight usage by a given percentage, the

Scientist cannot guess these priorities and must be informed early

and precisely.

Stateless implementation

An enduring design principle for eﬀective maintenance is to keep

the recipe stateless. Each day or week it recomputes every rec-

430 CHAPTER 10. DEPLOYMENT

ommendation from raw data, carrying no hidden state forward.

This approach prevents errors from compounding over time. If an

upstream glitch inﬂates today’s stock levels, tomorrow’s run—once

the data is corrected—naturally reverts to normal decisions. The

system can “snap back” as soon as the data pipeline recovers,

without lingering ghosts of old calculations.

Stateless designs also make long-term maintenance easier. When-

ever the Scientist updates a cost function or introduces new con-

straints, the change takes eﬀect immediately on the next run,

touching every SKU or site that needs adjustment. There is no

need to weed out older stored states or to worry that a previously

trained model might have internalized outdated signals. Recomput-

ing each decision may look demanding, but modern infrastructures

handle these volumes for supply chains of any size, especially once

data extraction is under control.

This stateless pattern shows its worth when the system must

handle surges in demand or abrupt policy shifts. If an entire region

experiences an unusual spike in orders, the next daily run can

incorporate it without being anchored to the prior day’s misjudg-

ment. The recipe is ﬂexible: it adapts as soon as conditions evolve,

eliminating the temptation to rely on stale manual buﬀer rules

once used to shore up a fragile process.

Black-swan events

Beyond everyday variability lies the risk of severe, fringe disrup-

tions—warehouse ﬁres, ﬂoods, pandemics, or sudden regulatory

embargoes. Such black-swan events upend normal cost structures,

capacity constraints, and distribution paths. While no daily recipe

can anticipate events far outside typical parameters, the organiza-

tion can rely on the iterative approach used during the initiative’s

creation. When disaster strikes, the Scientist encodes the new or

temporary constraints directly into the recipe: a site might run at

half capacity or be bypassed entirely; an emergency logistics route

might be assigned ten times the usual shipping cost. Once these

short-term adjustments are made, the system runs again, giving

10.3. ONBOARDING 431

an immediate view of how the new constraints reshape allocations

or reorder lines.

A black-swan event might also require deeper changes—building

alternative ﬂows or drastically revising lead times for an entire

region. The principle remains: the recipe is the single point of

integration, so the newly discovered constraints or extreme costs

are introduced there. This approach is more resilient than ad hoc

ﬁreﬁghting through manual overrides scattered across spreadsheets

and departments. Each ﬁx, once validated, becomes part of the

code base and ceases to rely on ephemeral human vigilance.

Crucially, an extraordinary crisis can become an opportunity to

deepen the company’s risk awareness. If a supplier default reveals

that 90% of a key component was single-sourced, the recipe can

be permanently updated with rules that prevent overexposure to a

single vendor. The same instrumentation used for daily checks can

surface such vulnerabilities. In time, lessons from turmoil elevate

the baseline, ensuring the system emerges more robust.

Long-term maintenance, therefore, is not only vigilance against

normal drift but also a structured response to sudden shocks. Both

daily updates and urgent ﬁxes tie back to a single stateless recipe

that remains the core of an automated supply chain. Once a

company becomes accustomed to this cycle—small increments,

periodic recalibrations, and crisis-driven overhauls—it ﬁnds that

the automated ﬂow stays current. The initiative proves more stable

and ﬂexible than any patchwork of spreadsheets because it has a

built-in mechanism for continual renewal.

At this juncture, the initiative has a stable architecture, daily

operations run under automated decisions, and the company is

ready for both routine changes and big surprises. What remains is

to assess how this redeﬁnes roles, organizational structures, and

job descriptions: a well-maintained recipe does not merely replace

old processes; it transforms the supply chain workforce.

432 CHAPTER 10. DEPLOYMENT

10.4 Organizational changes

Organizational shifts are unavoidable once automated decisions ﬂow

daily through the company. Though the mechanics of a well-deﬁned

scope and an iterative rollout may feel purely technical, people

across the business soon realize that many tasks—once handled

by planners or middle managers—now proceed with little human

intervention. Employees accustomed to gathering data, reconciling

conﬂicting indicators, and juggling last-minute overrides discover

that the new process leaves fewer gaps to patch. Some welcome

the relief and focus on thornier supply chain problems that resist

automation. Others sense a loss of relevance and resist the change,

citing alleged “exceptions” or insisting the system “cannot capture

every detail”.

Yet once laid out explicitly, these details can be encoded into

the recipe. If, for instance, a planner tweaked reorder quantities

for a vendor that consistently shipped partial pallets, that nuance

belongs in the code. Once added, the recipe no longer relies on

last-minute ﬁxes. Freed from perpetual ﬁreﬁghting, employees

can extend the scope of the initiative or strengthen relationships

with partners. Over time, the company must acknowledge that

many day-to-day routines have changed. Traditional planners,

accustomed to scanning spreadsheets for hundreds of SKUs each

morning, ﬁnd such manual checks redundant. Finance, which once

saw forecasts as loose “best guesses” that demanded caution in

budgeting, is now confronted with a system that commits capital

daily unless told otherwise. The IT department, historically over-

loaded with backlog requests for reports or mini-applications, ﬁnds

that a single well-engineered pipeline supplants many scattered

customizations.

Overcoming these frictions requires a clear articulation of why

the initiative demands not only a shift in software but also a

transformation in who does what. If the automated decisions are

trusted, the company no longer beneﬁts from employing multiple

teams to replicate or override them. Rather than stewarding endless

spreadsheets, planning staﬀ should enhance how each product is

10.4. ORGANIZATIONAL CHANGES 433

sourced or stored, reﬁne cost assumptions, and capture new signals

that would otherwise remain invisible to the algorithm. Similarly,

ﬁnance executives need not revisit the same line-by-line details if

the economic drivers in the recipe faithfully reﬂect the intended

constraints. Instead, their role is to adjust those drivers when

strategy changes or new resource limits apply.

This shift also calls for new forms of cooperation. A mechanical

system is both transparent and inﬂexible: it cannot quietly paper

over a mismatch between two departments. If a warehouse con-

trol rule contradicts a recent marketing push, the conﬂict appears

quickly in the daily outputs. Ultimately, resolving such contra-

dictions requires more than a polite meeting or an ad hoc ﬁx. It

requires the Scientist—or whichever role now owns the recipe—to

incorporate the updated guidelines and then rerun the logic. For

that to happen without chaos, the organization must unite behind

a single automated process rather than let each team revive a

personal spreadsheet whenever it dislikes the result. Those who

cling to local workarounds will dilute the beneﬁts of automation

for everyone.

The most visible change unfolds in the day-to-day planning

workforce. Previously, they rotated across SKUs or sites, adjusting

inventory levels, double-checking lead times, and manually inter-

vening when supply or demand changed abruptly. Now they can

engage more strategically. Instead of verifying hundreds of reorder

lines, they might conﬁrm that new supplier contracts are coded

correctly or investigate an unexpected surge that the recipe ﬂags

as an outlier. Their daily work becomes less about mechanical

data entry and more about relationship-building, discovering new

cost drivers, and anticipating shifts in the market. That evolution

can feel unsettling at ﬁrst. Some employees may assume their

original role—manually handling each step—remains irreplaceable.

They may try to keep partial manual processes alive. But once the

system runs smoothly for a small subset and expands to a broader

scope, the recipe’s consistency soon outperforms scattered human

vigilance.

Although organizational changes can trigger discomfort, they

434 CHAPTER 10. DEPLOYMENT

pave the way for the company to become more agile and more

aligned with genuine priorities. If a strategic initiative aims to lower

transport costs or reduce overall lead times, it can be implemented

directly in the recipe, then tested and measured on real orders—not

just debated in a meeting. Planners gain opportunities to shape

how constraints are modeled, rather than playing catch-up each

time demand shifts. Executives see a clearer cause-and-eﬀect

between constraints they approve and the subsequent outcomes in

inventory or fulﬁllment metrics. As unproductive routines dissolve,

staﬀ once absorbed in repetitive tasks can focus on forging stronger

ties with suppliers, gleaning meaningful signals from the market,

and ensuring that new product introductions or transitions align

with the daily mechanical approach.

These organizational adjustments form the backbone of a sup-

ply chain that thrives on rigorous, automated decisions. In the

sections that follow, we turn to the most direct impact on plan-

ners themselves, who evolve into “Flow Managers” within this

new framework. We then examine how the Scientist role becomes

central to sustaining and improving these decisions long after the

initial deployment.

10.4.1 Planner vs. Flow Manager

When daily decisions become fully automated, the traditional

role of the planner—poring over spreadsheets, adjusting reorder

quantities, and reconciling conﬂicting priorities—gradually loses

much of its daily purpose. In many ways, this shift is liberating.

Freed from the burden of chasing small data inconsistencies or

applying repetitive heuristics, the same person can step into the

higher-value role of Flow Manager. Rather than micromanaging

every SKU, they now survey the entire supply network, identify real-

world exceptions, and cement collaborative ties among suppliers,

carriers, and internal stakeholders.

10.4. ORGANIZATIONAL CHANGES 435

Repetitive adjustments

Planners were typically tasked with aligning moving pieces—forecast

data, supplier lead times, and local constraints—by manually rec-

onciling spreadsheets and system-generated ﬁgures. They were the

“glue” holding processes together whenever the formal software

or the oﬃcial forecast fell short. In an automated setting, how-

ever, the logic that once ﬁlled these spreadsheets is embedded in

the recipe that produces each day’s decisions. This recipe does

not merely generate theoretical numbers: it proposes or enacts

reorder lines, allocations, or capacity bookings, each grounded in

the company’s ﬁnancial costs and constraints. Whereas before

a planner might spend hours each week scouring for suspicious

stock levels or last-minute shipping mistakes, the process now ﬂags

outliers automatically, prompting targeted interventions rather

than universal manual checks.

As a result, the focus of the role rises to a higher level. Instead

of scanning hundreds or thousands of SKUs for reorder miscalcu-

lations, the ex-planner can invest his energy in more strategic or

more relational tasks. He can track subtle signals that numeric

models might miss, such as a supplier’s newly acquired certiﬁcation

or early warnings that a carrier is struggling in certain regions. He

can also assess areas where the recipe might beneﬁt from deeper in-

sights—such as incorporating real-time capacity data for a critical

production line or capturing new discount schedules from a primary

vendor. By supplying these discoveries back to the Scientist, the

Flow Manager ensures that the automated recipe evolves without

daily guesswork.

The state of consensus

This transformation often does away with the repetitive alignment

sessions—commonly known as Sales and Operations Planning

(S&OP)—in which teams formerly debated how to adjust forecasts

or handle uncertain capacity. In many companies, these sessions

devolve into monthly or quarterly rounds of nudging numbers, pro-

436 CHAPTER 10. DEPLOYMENT

ducing a single consensus ﬁgure still disconnected from operational

realities. Such a process, at best, repeats known constraints and

departmental viewpoints; at worst, it masks conﬂicts and leads to

feeble compromises.

With a numerical recipe driving daily allocations or production

schedules, there is no protracted debate about which forecast is

“right”. All relevant constraints—lead times, cost structures, stock-

out penalties—are either in the system or not. When a department

raises a concern (for instance, a marketing plan that will push

demand spikes on short notice), the response is concrete: the recipe

must be amended to reﬂect the true economic stakes. One might

encode higher penalty costs for stockouts of certain promotional

items, or factor in premium freight for sudden surges. Rather

than negotiating a middle-ground forecast in weekly meetings, the

Flow Manager and the Scientist ensure that operational decisions

align automatically with updated models of costs and constraints.

This renders the old forecasting debates largely moot—every “ﬁ-

nal ﬁgure” that shapes daily orders is anchored to the company’s

economic logic, not a compromise hammered out in a conference

room.

Real exceptions

Because the numerical recipe self-corrects in response to new data,

daily overrides for individual SKUs become unnecessary. The

Flow Manager’s vigilance instead focuses on genuinely abnormal

events—cases so far beyond the recipe’s rules that they would

produce decisions at odds with reality. For example, if a key

supplier closes a plant for two weeks after an unforeseen technical

failure, the manager identiﬁes a major disruption. Instead of

overriding hundreds of reorder lines, he elevates the matter: “Our

lead time from Supplier A is eﬀectively doubling this month, with

partial shipments only.” The Scientist codes the change, the recipe

reruns, and the entire system readjusts accordingly—from revised

reorder quantities to diﬀerent carriers, if needed. In the former

manual approach, such an event would trigger days of frantic email

10.4. ORGANIZATIONAL CHANGES 437

threads and one-oﬀ spreadsheet changes in multiple regions. Now

it is handled as a single change in the logic.

This shift in focus reduces friction across the organization.

The Flow Manager no longer needs to defend, line by line, how

he reached certain reorder amounts. Instead, he can examine

the broader shape of demand spikes or supply constraints. If

certain items systematically underperform service expectations,

the question is not “why did you reorder too little last week?”

but rather “how can we reframe the cost drivers or lead-time

assumptions so that the recipe captures this item’s volatility?”

True issues get resolved swiftly; old patterns of “we’ll just push up

the forecast” fade away.

Strategist, liaison, and auditor

Over time, the rebranded role of Flow Manager stands at the inter-

section of three core missions. First, he becomes a strategist—no

longer stuck in day-to-day reorder monotony, he reﬁnes the under-

lying ﬁnancial logic, highlights future expansions (new warehouses

or product lines), and proposes broadening the automated scope.

Second, he serves as a liaison—communicating with sales, mar-

keting, and procurement to gather developments the automated

system has not yet captured. Finally, he acts as an auditor—spot-

checking the system’s outputs for outliers, investigating anomalies,

and verifying that the cost or lead-time assumptions still match

real-world conditions.

None of these missions resembles the classic routine of adjusting

safety stock in a spreadsheet every morning. That old pattern is

replaced with continuous improvement cycles. Each cycle expands

either the coverage of the automation (taking on new product

families, channels, or constraints) or the depth of the model (in-

troducing better cost approximations for shipping, new rules for

batch production, or more nuanced penalty structures for delayed

deliveries). The ex-planner, freed from clerical tasks, is key to

unearthing which expansions matter most.

438 CHAPTER 10. DEPLOYMENT

The wider team

When the Flow Manager devotes energy to strategic liaison work,

the former roles of “senior planners” or “production coordinators”

must likewise adapt. In organizations that cling to older structures,

multiple staﬀ may still attempt to replicate daily decisions oﬄine.

This duplication only reintroduces confusion—no two spreadsheets

mirror each other perfectly. But in organizations that embrace

the shift, employees who once specialized in manual reordering

can become domain experts. They may concentrate on speciﬁc

complexities, such as ensuring hazardous-materials shipping follows

precise regulations, or forging stronger lead-time improvement deals

with logistics partners.

Moreover, while S&OP or forecast-collaboration meetings might

vanish in their traditional form, some communication channels re-

main. The diﬀerence is that these channels no longer revolve

around “tweaking” forecasts. Instead, they focus on bridging the

recipe’s logic with the front lines of the business—discussing new

store expansions, pending product line retirements, unexpected

raw material surcharges, or brand-protection strategies that, if

not coded, lead to suboptimal daily outputs. Each conversation

is grounded in the same integrated logic that drives every deci-

sion, and participants talk about adjusting real drivers instead of

haggling over whose forecast is correct.

In sum, as automation takes hold, the planner’s role rises from

daily micromanagement to that of an empowered Flow Manager.

By assuming accountability for capturing operational truths—both

subtle and dramatic—the ex-planner-turned-manager ensures that

the mechanical decision process remains accurate, timely, and ever-

improving. Freed from spreadsheet chores, his insights expand

to serve the entire network, from supply partners to in-house

manufacturing, forging deeper synergies than the old routines ever

permitted.

10.4. ORGANIZATIONAL CHANGES 439

10.4.2 The Supply Chain Scientist

The Supply Chain Scientist carries a unique responsibility: translat-

ing the organization’s evolving economic realities into a numerical

recipe that steers the daily ﬂow of goods. Once the company

commits to replacing large swaths of manual planning with auto-

mated decisions, the Scientist becomes the process’s linchpin. This

role stands apart from traditional business analytics or forecast-

ing positions because it mandates both end-to-end ownership of

the recipe and direct accountability for proﬁtability. Instead of

merely improving visibility or producing numerical artifacts, the

Scientist aims to deliver correct, self-adjusting decisions, trusted

in production day after day.

Bridging with IT

From a technical standpoint, the Scientist sits at the frontier of the

company’s systems and data. Yet it would be a mistake to treat the

Scientist as part of the IT function. The Scientist must be free to

focus on modeling supply chain decisions, not on wrestling with a

litany of data-integration chores that IT already specializes in. The

Scientist does rely on a stable pipeline of raw records—unﬁltered

extracts of transactions, inventories, supplier terms, and the like.

However, once that pipeline is set up, the Scientist’s mission pivots

to coding new insights and constraints that reﬂect supply chain

conditions. The IT group, for its part, remains the caretaker of

data extractions and infrastructure.

This division of responsibilities beneﬁts both sides. The Sci-

entist does not worsen the IT backlog with endless demands for

specialized transformations; the data oﬃcer in IT delivers neutral,

daily snapshots of the source systems, letting the Scientist handle

all reﬁnements in the recipe. Meanwhile, the IT department can

concentrate on its broader operational mandate—maintaining the

stability of key applications and ensuring that every upstream

database and data feed remains healthy. Once a problem surfaces,

such as a table rename in the ERP or a failed overnight export, the

440 CHAPTER 10. DEPLOYMENT

two sides cooperate quickly: IT resolves the pipeline glitch, and the

Scientist resumes normal operations with the updated data. There

is no friction or confusion about who “owns” the transformation

logic or which changes were introduced midstream.

On ﬁnance

A corollary of the Scientist’s mandate is the need to integrate ﬁnan-

cial constraints deeply into daily decisions. Cost drivers—whether

freight rates, working-capital limits, or margin targets—cannot

remain abstract ideas ﬂoating in slide decks. They require explicit

numerical treatment so that every allocation roots the trade-oﬀs

in genuine monetary realities. The Scientist thus cooperates with

ﬁnance not out of formality but out of necessity: if upcoming

promotional campaigns alter expected margins, or if new capital

constraints preclude particular inventory levels, the recipe must

be updated accordingly.

These exchanges with ﬁnance take place more regularly than

might be assumed. During the initiative’s rollout, the Scientist

typically holds a series of short meetings where he presents how

current cost assumptions—such as storage overhead or penalties

for expedited freight—translate into daily decisions. Finance might

spot hidden inaccuracies, such as an overlooked handling fee for

bulk deliveries or a missed opportunity to factor discount tiers

more ﬁnely. Correcting these details can shift the solution from a

naive local optimum to decisions that better reﬂect the company’s

real P&L impact. This is how the automation remains ﬁnancially

“sharp”—no budget line or proﬁt center is left to guesswork.

Over time, even after the recipe matures, major operational or

strategic shifts still prompt fresh rounds of modeling. A high-level

directive—such as lowering the cost of capital or diminishing expo-

sure to a class of risks—ﬂows through ﬁnance to the Scientist, who

translates it into revised penalty functions, discounts, or sourcing

rules. The eﬀect appears in the daily outputs soon after, keeping

the company’s ﬁnances and day-to-day supply chain harmonized.

10.4. ORGANIZATIONAL CHANGES 441

On leadership

Although the Scientist is neither the CEO nor the Executive Spon-

sor, he does hold a pivotal seat at the table whenever top leadership

alters strategic priorities. As soon as the new direction crystal-

lizes—entering a new country, switching to nearshoring, favoring

premium service for certain product lines—the numerical recipe

must encode those priorities. Without this prompt alignment, the

mechanical decisions remain stuck in outdated logic.

A direct, brief conversation with top leadership once or twice

a year can suﬃce. Its purpose is not to pore over micro-level

code changes. Rather, it conﬁrms that the Scientist understands

the larger picture. If the company aims for faster expansions yet

stricter capital usage, the Scientist might reﬂect this by tightening

inventory budgets on slower-moving items or by introducing steeper

penalties for using emergency freight. The recipe’s daily outputs

thus shift where they will inﬂuence the bottom line. Because

the code is stateless, no states linger to hamper the transition.

Within a week of leadership’s new directive, the supply chain might

already be producing purchase orders aligned with those strategic

imperatives.

This pattern of swift adaptation demonstrates how strategic

coherence emerges from the supply chain’s mechanical foundation:

once the code’s assumptions change, every relevant decision shifts as

well. Where a manual process once required months of incremental

retraining for dozens of planners, a single update here instantly

scales across the entire product range or site network.

Beyond two Scientists

In many midsize companies, a single Supply Chain Scientist—plus

a second for redundancy—can cover entire decision streams. Yet as

the initiative grows—adding more product lines or diving deeper

into advanced production or distribution problems—additional

Scientists may be needed. Managing a larger team requires a more

rigorous approach to the recipe’s structure.

442 CHAPTER 10. DEPLOYMENT

The ﬁrst principle is to keep as much shared logic as possi-

ble in a uniﬁed codebase. Each Scientist might handle distinct

sub-areas—one focuses on procurement constraints, another on

production scheduling—yet all collaborate on a single blueprint.

Just as software engineers rely on version control and code reviews,

Scientists adopt similar practices to coordinate daily changes and

ensure an improvement in one domain does not inadvertently break

constraints in another.

Crucially, Scientists remain supply chain experts ﬁrst and fore-

most. They are not hired to pursue abstract machine-learning

innovations. If they set up code reviews, the goal is not to create a

specialized IT pipeline but to keep daily decisions consistent and

error-free. Each Scientist learns the rationale behind major lines

of code—the “why” as well as the “what”. A master manual

can

prove invaluable, tracking the strategic motivations for each rule.

When one Scientist departs or a new hire arrives, knowledge trans-

fer is smoother and spares the group from rediscovering intricacies

of cost assumptions or corner-case constraints.

By handling expansions in this manner, the Scientist’s team

can scale gracefully while preserving the daily iteration pace. No

matter how large the business becomes, every aspect—from new

discount structures to reﬁned lead-time predictions—can be tackled

by the relevant Scientist and then merged, in an orderly way, into

the master recipe.

Strict focus

Although they code extensively, Scientists are not subordinate

software developers. Their success is measured by the real impact

of daily decisions—lower inventory misalignments, higher margins,

or faster adaptation to abnormal conditions. They do not vanish

into IT’s backlog, and they are not a generic “data science” team

chasing ephemeral accuracy benchmarks. Instead, they retain a

sharp focus on actionable changes that reorder the ﬂow of goods

Called the Joint Procedure Manual internally at Lokad.

10.4. ORGANIZATIONAL CHANGES 443

to better serve strategic and ﬁnancial goals.

This grounding in the tangible separates them from depart-

ments that traﬃc in dashboards or once-a-quarter analyses. The

Scientist must repeatedly take small, purposeful steps with code

whenever the environment shifts, verifying each day’s results in

production. The synergy with IT is essential but bounded at

the data-extraction boundary. The synergy with ﬁnance persists,

ensuring that cost drivers retain credibility even as the market

moves. And the synergy with senior leadership tightens, allowing

the automated process to reﬂect any new direction the company

adopts swiftly.

By internalizing these realities, companies realize that the Scien-

tist’s role is neither ephemeral nor disposable after the “automation

project” ends. Just as the Flow Manager replaces the manual plan-

ner in day-to-day tasks, the Scientist replaces a tangled web of

partial solutions with an evolving, codiﬁed approach. The necessity

of strategic updates never ceases, and the Scientist stands ready

to embed each shift into the mechanical logic. This continuous

interplay is how a supply chain remains perpetually relevant and

linked to real economic drivers.

The Scientist, in short, is a new breed of contributor to corpo-

rate operations—one whose orientation is neither purely technical

nor purely managerial, but a binding of the two. Their authority

is drawn not from any formal managerial hierarchy but from the

everyday accuracy and adaptability of their code. In the next

section, we examine in greater depth the common pitfalls that be-

fall “data science” in corporate contexts, and why genuine Supply

Chain Scientists must go beyond superﬁcial number-crunching to

attain lasting success.

10.4.3 The greater company

Every initiative that reshapes supply chain decisions, no matter

how precisely it is scoped at the outset, inevitably presses on the

corporate fabric as a whole. The numerical recipe cannot operate in

isolation—its daily outputs must reﬂect the constraints, objectives,

444 CHAPTER 10. DEPLOYMENT

and cost structures of multiple departments, many of which have

traditionally maintained local processes disconnected from broader

concerns. While the earlier sections showed how to structure the

initiative around a Supply Chain Scientist, the Data Oﬃcer, the

Flow Manager, and the Executive Sponsor, the rest of the company

must also learn to interact with—and ultimately trust—this new

decision-making process. In the absence of a unifying framework,

local, short-term pressures from sales, marketing, procurement, or

other areas risk overriding or undermining the mechanical output.

Yet the real strength of the recipe lies precisely in gathering

every relevant driver and expressing it daily, without hidden bias

or fragmented oversight. For that to happen, each department

must relinquish the temptation to maintain its own conﬁdential

spreadsheets and shadow processes. It must, instead, state its

constraints and cost structures in the open so the Scientist can

encode them directly. This is where upper management, and in

particular the top executive or CEO, must intervene decisively. The

CEO is not, and cannot be, the ﬁnal arbiter of economic drivers;

such details are too ﬁne-grained for the top oﬃce. Rather, the

CEO is the ultimate arbiter of which department owns each driver,

and of ensuring that these owners cooperate with the Scientist

rather than attempt to circumvent the system. If ﬁnance asserts

that certain capital costs must be set at a speciﬁc rate, those costs

become the recognized standard. If procurement stipulates that

a particular strategic vendor is to be spared from cuts in order

volumes, that choice is modeled explicitly. The policy must come

from the relevant department, but once conﬁrmed, it ceases to

exist as an unwritten rule and is anchored in the daily decision

logic.

This arrangement can feel jarring to departments that previ-

ously adjusted their numbers on a whim. When a brand manager

ﬁnds that a popular product does not appear in the upcoming

order plan, he may fear losing revenue or market share. In the

old approach, he could scramble to override the reorder quantity

in a spreadsheet, whether or not it truly beneﬁted the company’s

bottom line. In the new system, if the brand manager believes

10.4. ORGANIZATIONAL CHANGES 445

that omitting the product causes reputational damage that far

outweighs any short-term proﬁt concerns, the correct path is to

escalate this factor so its ﬁnancial cost or penalty is coded into the

recipe. The goal is not to “blame” the mechanical process for ignor-

ing a brand imperative; it is to embed that imperative—expressed

as a penalty for stockouts or a premium for brand-building avail-

ability—in the economic drivers. From then on, the daily decisions

that once led to friction will naturally align with the brand man-

ager’s priority, so long as that priority remains truly important to

the company’s overall strategy.

Similar tensions arise with marketing when a promotion is

omitted from the ﬁnal plan, or when a featured product proves

marginally proﬁtable and is assigned a low reorder priority. If the

marketing team has evidence that a promotion will raise cross-

selling of other items, it must show how the additional revenue or

brand equity translates into an economic payoﬀ. Once the added

payoﬀ is stated explicitly, the Scientist incorporates it into the

code. Those marketing concerns no longer get handled through last-

minute overrides or “ﬁnal forecast adjustments”; they become part

of the daily mechanical process—an open, documented variable

that can be revised if market feedback shows the promotion did

not generate the expected cross-selling. This open articulation of

what were once tribal or intangible factors forces each department

to decide whether a local desire is a passing hunch or a vital

economic truth. It is, in eﬀect, a game-theoretic dynamic: every

department sees that partial cooperation (such as whispering an

inﬂated demand to secure inventory) no longer yields an advantage.

The ﬁnal recipe checks all constraints daily; if a cost or beneﬁt

is not declared, it is not recognized. Top management’s role is

to ensure each domain states its costs and gains honestly, rather

than manipulating demand ﬁgures to secure resources at others’

expense.

Procurement, likewise, may initially feel threatened when the

daily recipe dictates exact reorder lines and timing, eﬀectively

replacing the manual negotiations some buyers believed were their

primary value. Yet the procurement function remains indispensable

446 CHAPTER 10. DEPLOYMENT

in revealing which vendors have reliable quality, which suppliers

impose hidden conditions, and which may merit sacriﬁce in favor

of new sourcing strategies. The procurement team does not need

to overshadow the system or revert to side deals; it gains more

inﬂuence by stating vendor constraints clearly. Are there mini-

mum batch sizes, special inbound tariﬀs for partial shipments, or

vendor loyalty discounts? Each nuance is a piece of the economic

puzzle. Once coded, it shapes the recipe’s outputs, so procurement

inﬂuences daily decisions through stable, rational rules rather than

repeated manual rework.

The same logic applies to engineering teams and product de-

signers who want to incorporate new components or adjust bills

of materials. In the past, a design modiﬁcation could blindside

planning, causing last-minute shortages when the newly speciﬁed

part was overlooked. Now the mechanical daily process immedi-

ately ﬂags any unknown part, revealing that the new item lacks

cost references or lead times. The design team can no longer slip

changes through unnoticed; it must collaborate with the Scientist

to deﬁne how the new component alters production constraints or

overhead. This enforced collaboration spares the company from

silent supply mismatches that once appeared “inevitable”.

Even ﬁnance—long accustomed to viewing supply chain as a

cost center apart from brand or marketing priorities—is pressed

to clarify how liquidity constraints and working-capital targets

feed into daily reorder levels. If ﬁnance wants certain lines to run

with minimal buﬀer stock, or signals that inventory valuations are

changing, those updates translate into immediate code changes.

The daily outputs no longer merely reﬂect a forecast and a stan-

dard margin; they express ﬁnance’s up-to-date stance on capital

usage. When a top executive wonders why certain SKUs have

been cut back, there is a direct line from ﬁnance’s constraints to

the automated decisions—and from the Scientist’s code back to

ﬁnance’s constraints. No arbitrary overrides are needed and no

endless forecast meetings are held; everyone sees the cause and

eﬀect.

In such an environment, the question of blame rarely arises.

10.4. ORGANIZATIONAL CHANGES 447

If an unexpected shortfall in a promoted SKU hits the shelves,

marketing will challenge the numerical recipe—not to undermine

it, but to check whether the promotion’s impact was correctly

included. The Scientist, in turn, checks whether marketing truly

disclosed all relevant information—such as incremental brand risk

or cross-selling gains. If not, the remedy is a new or revised cost

assumption. Over time, these cross-department interactions evolve

from friction-laden skirmishes into constructive updates to the

recipe’s parameters. This model of collaboration thrives only if

top management, from the CEO downward, endorses the process

and stands ﬁrmly against reintroducing manual manipulations

whenever a department is dissatisﬁed with an outcome.

Once each department sees that alignment comes from adjusting

shared assumptions—not from ignoring or overriding mechanical

outputs—it realizes the mechanical decisions have become the

single source of truth for daily operations. That single source is

not rigid or unwieldy; it adapts as soon as a cost, constraint, or

margin driver proves relevant. It refuses to incorporate half-baked

ephemeral guesses. This dynamic can be disconcerting in the

initial months, as employees discover they can no longer quietly

ﬁx an inconvenient number in their spreadsheets. Yet after a

full cycle—often a quarter or two—people recognize that fewer

emergencies arise, fewer turf wars erupt, and the supply chain feels

more coherent.

The initiative becomes more than a supply chain improvement;

it is a mechanism for continuous cross-department synergy. Every

domain within the company—marketing, procurement, engineering,

ﬁnance, and operations—sees that the best way to secure its

priorities in the daily ﬂows is to articulate them openly and then

to trust the mechanical process to execute them consistently. The

CEO’s role is to ensure each domain truly owns the costs or beneﬁts

it claims and to endorse the Scientist’s ﬁnal code as the channel

through which those claims reshape decisions. Anything less breeds

duplication or pushback that dilutes the new system’s gains.

Over time, a culture emerges in which no one ﬁghts the recipe’s

daily numbers so long as they reﬂect properly stated drivers. If

448 CHAPTER 10. DEPLOYMENT

marketing ﬁnds brand costs understated, it raises the issue for-

mally. If procurement sees a vendor’s reliability has improved, it

updates either the penalty for stockouts or the expected lead times.

Everyone gradually grasps that game-like maneuvers—such as in-

ﬂating forecasts—no longer yield an advantage, because the recipe

cross-checks everything with actual data. The shared impetus is

to reﬁne numerical assumptions, not sabotage them.

This shift toward a single, daily, mechanical orchestration may

appear dramatic in companies long reliant on departmental auton-

omy. The beneﬁts include sharp reductions in ﬁreﬁghting, deeper

synergy across brand, marketing, and operations, and a stead-

ier link between strategic ambitions and on-the-ground execution.

The moment each department accepts that it must shape the in-

puts—rather than override the outputs—the initiative achieves the

company-wide collaboration essential to long-term success.

Chapter 11

Stagnation

Supply chain, as treated in this book, is an intent executed through

decisions; the narrow claim of this chapter is that, as a discipline,

supply chain has largely stagnated. Not because goods fail to

move—they do—but because ﬁrms’ choices of what to move, when,

where, and how much have not kept pace with four decades of

computation. The dominant posture remains clerical: people

reconcile exceptions produced by software that records facts and

narrates the past but rarely places bets.

By stagnation we mean operational stasis masked by motion.

Ledgers are digital and ubiquitous; reports refresh by the minute;

data warehouses swell; acronyms and dashboards proliferate. Yet

the core mechanics of allocation are much as they were in the mid-

1980s: point forecasts treated as certainty, buﬀers tied to arbitrary

service targets, reorder surrogates wired into weekly cadences, and

a daily swirl of approvals. Computers serve as systems of records

and reports, and where “automation” appears, it is usually alert

theater—lists that hand the hard cases back to humans—rather

than a system of intelligence that issues unattended, auditable

commitments.

This stasis exacts a cost. It systematically suppresses what

moves money: fat-tailed variability, couplings across time and

space, and the option value of waiting. Averages stand in for

449

450 CHAPTER 11. STAGNATION

distributions; local performance indicators stand in for economics;

Goodhart’s law quietly turns measures into targets. The result is

a clerical equilibrium that delivers continuity through buﬀers and

overtime while letting coins leak in ordinary times but faltering

under stress.

None of this is inevitable. Earlier chapters have assembled the

missing instruments: probabilistic views of the future instead of

point surrogates; coin-denominated valuations at ﬁrm scope; and

systems of intelligence that recompute within frames, price trade-

oﬀs, and write decisions back unattended—guarded by halting

heuristics, whiteboxed dossiers, and experimental optimization

under dual-run. When these tools are adopted, the same assets

produce better ﬂows because the decisions change.

What follows is both synthesis and indictment. The ﬁeld did not

merely fail to accelerate with modern computation; it settled into

a stable, mediocre equilibrium sustained by incentives that reward

narratives over emissions: academic puzzles, vendor rebranding

of records and reports as “planning”, consulting ceremonies, and

internal “data science” that stops at insights. The organizational

trap—supply chain’s second

class status and subordination to

central IT—anchors the arrangement.

This double image—a ﬂow that keeps delivering and a discipline

that stands still—sets up the paradox that follows.

11.1 The paradox of success

Supply chains are celebrated as dependable. Goods cross borders

daily; store shelves are rarely bare for long; companies post solid

results while expanding their networks. Judged by visible outcomes,

the ﬁeld appears vigorous and resilient.

Yet this outward success masks a deeper stalemate. Over

decades of extraordinary gains in computing, most organizations

have treated computers as ledgers and dashboards rather than

decision engines. What passes for “automation” is usually a cascade

of exceptions for humans to reconcile; what passes for “planning”

11.1. THE PARADOX OF SUCCESS 451

remains a brittle blend of deterministic forecasts, reorder surrogates,

and manual overrides—merely reskinned with modern interfaces.

Continuity is bought less with engineered intelligence than with

buﬀers, redundancy, and clerical vigilance.

This is the paradox: reliable delivery at scale coexists with

intellectual stasis. Other domains have converted cheap compu-

tation into unattended decision making; supply chain has largely

repackaged yesterday’s routines. The much-touted innovations

are cosmetic—new acronyms for old artifacts, new screens for old

habits—while the core remains a daily, human-mediated reconcili-

ation of numbers.

The cost of this stasis seldom appears in a day’s snapshot

because the ﬂow does “deliver”. It becomes conspicuous dur-

ing shocks—abrupt demand shifts, transport disruptions—when

processes designed for compliance rather than adaptation to un-

certainty falter. More damaging is the quiet, compounding bleed

in ordinary times: overgrown safety stocks, underused options,

and thousands of hours spent curating spreadsheets instead of

improving decisions. Expensive software is acquired, but it rarely

elevates decision quality; its “automation” devolves into alert the-

ater that assigns the hard parts back to people. More insidious

than any visible miss is the mounting opportunity cost: every day

that variability is treated as a nuisance rather than priced and

exploited is a day when surplus that modern computation could

harvest simply evaporates.

Earlier chapters outlined remedies that already exist: proba-

bilistic views of the future, economics

anchored valuations, and

systems of intelligence that emit unattended, auditable decisions

daily—continuously improved through experimental optimization.

The aim here is not to re-argue those instruments but to explain

why a ﬁeld that could adopt them largely has not. The next section

traces the roots of this equilibrium—academic fashions, vendor

economics, consulting theater, and corporate governance—that

keep a demonstrably central function stable and mediocre.

452 CHAPTER 11. STAGNATION

11.2 Real-world complexity

Invoking “complexity” has become ritual apology for mediocre prac-

tice. The problem is not that supply chains are complicated—our

ﬁrst chapter already distinguished the essential from the acciden-

tal—but that most organizations ﬂatten what matters economically

into a handful of surrogates. Point forecasts stand in for uncer-

tainty; a single “service level” impersonates proﬁt; average lead

times erase the tails that do the damage. The result is not a model

of the ﬂow but a tranquilized caricature of it.

Simpliﬁcation is not a vice when it preserves the asymmetries

that move money; it is a fault when it erases them. Variability and

optionality are the raw materials of proﬁt. When a representation

suppresses fat tails, correlations, batching, or ratchets that bind

tomorrow’s options, it no longer guides decisions—it conceals their

stakes.

What follows is not another taxonomy of failure but a con-

crete vignette. A small, operational episode makes it sharp. It

also bridges to the method that consistently breaks stalemates in

the wild: experimental optimization (see the chapters Decisions,

Engineering, and Deployment).

11.2.1 Operational vignette

Consider a mundane episode. A national retailer operates two

regional DCs, each with a nominal receiving capacity of forty

containers per day. Purchasing works on a weekly cadence keyed to

supplier MOQs and rebates; the planning system computes reorder

points from point forecasts and average lead times; warehouse KPIs

track dock utilization and turns; stores are judged on “on-shelf

availability”. No villain, only sensible local goals.

Every Monday, sixty to seventy containers arrive at DC West.

The gates saturate by noon; trucks idle; demurrage mounts; a frac-

tion of the freight rolls to Tuesday; the tail spills into Wednesday.

Meanwhile, store shelves run patchy mid-week on long-tail SKUs

that were “in transit” in bulk on Monday yet physically receivable

11.2. REAL-WORLD COMPLEXITY 453

only on Wednesday. No dashboard lies: service looks ﬁne on a

weekly average; the rebate ladder was captured; dock utilization

peaked; inventory turns hold. Yet coins leak everywhere—idle

trucks, overtime, transient stockouts—while the organization con-

gratulates itself for “hitting targets”.

Nothing subtle is required to ﬁx the picture; what is required

is to admit the picture. In dual-run, a thin numerical recipe was

introduced: lead times as distributions with fat right tails; a priced

shadow for dock saturation that rises nonlinearly beyond forty

containers per day; supplier MOQs as penalties rather than edicts;

and, crucially, a daily assessment of orders without an obligation

to order daily. The engine could emit “do nothing yet” whenever

the option value of waiting exceeded the expected, risk-adjusted

return.

On day one, the halt tripped on insanity—a planned Monday

inbound of forty-eight containers for a forty-container dock; dossiers

exposed the drivers—priced in coins—and the defect was removed:

weekly batches were replaced by staggered releases that preserved

rebates while ﬂattening the inbound. Within four weeks of dual-run,

demurrage and receiving overtime fell markedly; mid-week shelf

gaps on the long tail diminished without raising working capital;

rebate capture persisted. No heroics, no grand reorganization:

the same assets, the same suppliers, the same stores—only the

decisions changed.

The lesson is not that “Mondays are bad”. It is that the real

complexity was economic and temporal, not algebraic. Averages

hid tails; a single service metric hid heterogeneous payoﬀs; hard

“constraints” became elastic once priced. Computers did nothing

human planners could not, in principle, do—they simply did it for

thousands of micro-options every day, without fatigue, surfacing

the right frictions in money.

11.2.2 Local maximum

The episode shows why mediocre equilibria persist. Each function

perched on a defensible hill: purchasing on rebates and MOQs;

454 CHAPTER 11. STAGNATION

warehouses on utilization; stores on availability; ﬁnance on turns.

The hills were real; the mountain lay elsewhere. Attempts to

“optimize” any one hill in isolation merely displaced pain. This

is the wickedness described in the ﬁrst chapter: objectives couple

across time and space, and measures become targets the moment

livelihoods depend on them.

Escaping a local maximum requires no manifesto—only a safe,

mechanical way to try better decisions and keep them when they

pay. Experimental optimization provides exactly that: run side-

by-side against end-of-day extracts; halt fast on insanity; expose

drivers in coins; promote only on measured uplift; revert cheaply.

Because the method prices trade-oﬀs at ﬁrm scope, it dissolves

many made-up “constraints” into penalties and turns departmental

vetoes into tunable, auditable parameters.

In practice, the barrier is seldom physics; it is narrative. As

long as averages and single KPIs narrate success, the equilibrium

looks acceptable. Replace the narration with daily, auditable

commitments priced in coins, and the supposed “constraints” reveal

themselves for what they are: choices. The next section examines

why the institutions meant to sharpen those choices—academia,

vendors, consultants—so often teach averages and KPIs instead.

11.3 Why the equilibrium holds

Bad ideas need no shocks to survive. A supply chain can stay

serviceable while its methods lag. The equilibrium persists because

incentives are muted and misaligned: each actor involved in shaping

the discipline earns a quiet rent from keeping decisions human-

mediated and deniable, while no one is paid in coins for unattended

decision quality.

Such equilibria endure because they clear a low bar. Trucks

still move, rebates are captured, dashboards stay green, and no

one must stake a career on a bolder alternative. In the absence

of an existential shock, the local gains of inaction often outweigh

the uncertain gains of rewiring decisions. As long as the apparatus

11.3. WHY THE EQUILIBRIUM HOLDS 455

works well enough, pressure to change remains muted.

The mechanics are banal. Long feedback loops dilute respon-

sibility; dashboards substitute targets for economics; pilots are

staged where failure cannot bite; “exceptions” divert the hardest

cases back to humans, thereby insulating brittle logic from falsi-

ﬁcation. In such a setting, mediocrity compounds quietly. What

passes for prudence—alerts before action, manual “validation”,

consensus around a single forecast—merely preserves a clerical

loop that neither prices uncertainty nor learns from it.

Around this loop sits a network of interests that damps pres-

sure to change. Academia is rewarded for puzzles and publication,

not for decisions that survive contact with docks and cut

oﬀs;

it polishes stylized models that are easy to grade and hard to

operationalize. Software vendors make their margins on seats,

storage, and upgrades of systems of records and reports; genuine

automation would shrink seats and erase screens. Consultants sell

choreography—workshops, matrices, “alignment”—whose deliver-

ables leave code and economics untouched; the engagement ends,

the rituals remain, and the spreadsheet ﬂow resumes. Corporate

data-science teams, measured by projects and dashboards, stop

at insights; ownership of the coin-denominated commitment sits

elsewhere, along with the incentive to rework assumptions when

reality disagrees. Inside the ﬁrm, supply chain remains second-class

and subordinate to central IT: budgets favor ledgers that never fail

over engines that must decide; risk is deﬁned as platform deviation

rather than economic loss.

Prices signal these incentives as clearly as any market does.

Vendors bill for access, not uplift; consultants for eﬀort, not gains;

academics for novelty, not resilience; internal teams for artifacts,

not emissions. Goodhart’s law then does the rest: once a met-

ric promises career safety, the organization optimizes the metric.

“Forecast accuracy” improves by shortening horizons and padding

buﬀers; “service level” rises with inventory that nobody prices;

“adherence to plan” is celebrated while trucks idle at a saturated

dock.

The vignette above is typical, not exceptional. Nothing in

456 CHAPTER 11. STAGNATION

physics prevented the weekly inbound pile-up; it persisted because

the measures governing each silo—rebates, utilization, availabil-

ity—were local, while the costs they displaced—demurrage, over-

time, mid-week gaps—were borne elsewhere. Where no one is

explicitly accountable for the ﬁrm

wide economics of the decision,

clerical equilibria harden into custom. The silence of proﬁts and

losses at the level of the micro

choice leaves narratives—“best

practice,” “industry standard,” “one set of numbers”—to ﬁll the

void.

The sections that follow examine the main sustainers of this

equilibrium. Academia’s attachment to publishable puzzles, ven-

dors’ rebranding of records and reports as “intelligence” while

outsourcing intelligence to users through alert theater, consultants’

buzzworded ceremonies that stop short of code, and corporate data

science’s insulation from ownership all contribute to a stable but

mediocre state. The organizational trap—supply chain’s second-

class status and subordination to central IT—locks the arrangement

in place. None of this requires malice. It suﬃces that incentives are

decoupled from unattended, auditable, coin

denominated decisions.

Until that coupling is restored, the equilibrium holds.

11.4 Who sustains it

The stalemate endures not through malice but through dispersed

incentives. Four constituencies, each pursuing reasonable aims on

its own terms, collectively sustain a stable but mediocre equilib-

rium: academia, enterprise software vendors, consultants, and

corporate data

science programs. Each constituency supplies

something the others demand—publishable puzzles, marketable

platforms, conﬁdence-building ceremonies, and dashboards of “in-

sights”—while leaving the daily act of allocation where it has long

resided: with human clerks reconciling exceptions.

The lens adopted here is adversarial, not hostile. Earlier chap-

ters have already set out what workable progress looks like: unat-

tended, auditable emissions of decisions priced in coins. Against

11.4. WHO SUSTAINS IT 457

that yardstick, we can examine how each constituency, by following

its own incentives, perpetuates practices that never clear the bar.

We begin with academia, whose stated mission—advancing

knowledge—should have made it the natural antidote to stagnation.

Instead, through a persistent misframing of supply chain, it has

too often furnished the rhetoric of science without the substance

of unattended decisions.

Academia

Universities and journals promise science; the factory ﬂoor receives

spreadsheets. The gap is not a matter of taste but of framing. As

argued in Epistemology, supply chain is applied economics: it prices

options under variability to allocate scarce resources proﬁtably.

Recasting it as “applied mathematics”—a catalog of tidy puzzles

with conveniently ﬁxed assumptions—yields papers that read well

and travel poorly. What matters operationally is not a theorem

about an idealized reorder point; it is whether unattended software

can turn records into auditable, coin-denominated commitments

that survive contact with docks, cut-oﬀs, rebates, and fat-tailed

lead times.

This misframing explains why mainstream academic outputs

so rarely register in practice. The canonical artifacts—point fore-

casts, Gaussian safety stocks tied to service targets, deterministic

plans—suppress exactly what moves money: tails, correlations,

batching, and ratchets that bind tomorrow’s options. When those

artifacts are installed in software, they reappear as alert theater:

the brittle logic delegates the diﬃcult cases back to humans, who

become the de facto intelligence. The organization then congrat-

ulates itself for “following best practice” while planners continue

the clerical reconciliation computers were meant to end.

The puzzle ﬁlter compounds the problem. A publishable paper

is easiest to produce when the world is simpliﬁed until a closed

form or a small solver can be shown to “optimize” a toy. One

more constraint is added here, one more index there; results are

reported on synthetic instances or sanitized extracts. Yet nothing

458 CHAPTER 11. STAGNATION

in this workﬂow compels the authors to expose their premises to

falsiﬁcation in the wild. In business, knowledge earns trust only

when it places bets—emits decisions unattended—and improves

proﬁt. Absent emissions, the bar is not met, no matter how elegant

the mathematics looks in isolation.

The neglect of data semantics and software engineering is

equally telling. As detailed in Information, modern ﬂows are

mediated by overlapping systems of records and reports, where

ﬁeld meanings drift and where “derived data” (forecasts, ABC

tags, buﬀers) contain no new information. A workable system of

intelligence—the engine that issues unattended decisions—must

therefore (i) read authoritative but messy records, (ii) carry un-

certainty without ﬂattening it, (iii) price trade

oﬀs in coins at

ﬁrm scope, and (iv) write commitments back, with dossiers ﬁt

for audit. Most academic treatments airbrush this junction away.

They assume clean, stationary inputs and stop where the work

becomes decisive: semantics, economics, and code that runs every

day at scale.

The right test bench has long been available. Intelligence,

Decisions, and Deployment laid out a minimal discipline: dual-run

against full-scope end-of-day extracts; halting heuristics that stop

the engine when conﬁdence drops; whiteboxed dossiers showing the

monetary drivers of each line; promotion only on measured uplift;

reversibility through statelessness and time-travel. A scientiﬁc

contribution that claims operational merit should instantiate itself

within that harness—so that failures surface as “insane lines”

and are removed by revising costs, constraints, or semantics, not

by exception lists and meetings. Papers that do not cross this

threshold are chronicles or conjectures, not supply chain knowledge.

Curricula mirror the literature. Master’s programs continue

to center on time

series gadgets from the 1980s, Gaussian safety

stocks tethered to arbitrary service targets, and classroom ABCs.

Programming and data engineering are treated as accessories;

enterprise semantics and probabilistic economics scarcely appear.

Graduates learn to supervise spreadsheets and packaged “planning”

modules, not to author numerical recipes that convert records into

11.4. WHO SUSTAINS IT 459

unattended emissions. The best student, sensing the ceiling, drifts

toward other STEM ﬁelds where code, data, and falsiﬁcation govern

status. The remainder staﬀ the clerical loop that the literature

presupposes.

None of this condemns mathematics. Durable mathematical

contributions do exist in our trade—but they present themselves

as subroutines inside an economic and engineering frame: queueing

results that calibrate congestion costs; linear

programming compo-

nents that assemble a truck; concentration bounds that regularize

demand tails. The error is to mistake the subroutine for the system.

An optimizer with the wrong objective, wrong semantics, or no

unattended pathway is not an advance; it is a polished detour.

A workable academic agenda is close at hand. Treat supply

chain as applied economics ﬁrst; let mathematics, statistics, and

computer science serve that end. Deliver artifacts that run: code

that ingests semantically faithful extracts; price schedules in coins

rather than “service targets”; probabilistic forecasts evaluated by

proper scoring rules and uplift, not by retrospective ﬁt; numerical

recipes shipped whiteboxed so practitioners can audit each commit-

ment. Above all, accept the discipline of experimental optimization:

iterate in the ﬁeld until insanity disappears, then publish what

survived. Citations may follow; if not, the coins will.

11.4.1 Software vendors

Enterprise vendors sustain the equilibrium not by conspiracy but

by economics and engineering ﬁt. As argued in Information, there

are three distinct software families: systems of records (authori-

tative ledgers and workﬂows), systems of reports (narrations of

the past), and systems of intelligence (engines that issue unat-

tended, auditable decisions). Most vendors sell records or reports,

then repackage add-ons as “planning” or “optimization” to chase

growth. The category confusion is not semantic; it is operational.

A CRUD-ﬁrst ledger cannot host heavy analytics without harming

latency; a reporting stack built to reshape aggregates cannot carry

uncertainty or price trade-oﬀs in coins. When records or reports

460 CHAPTER 11. STAGNATION

masquerade as intelligence, the ﬂow reverts to clericalism.

The dominant pattern is alert theater. “Automation” takes the

form of an exception list that delegates the hard cases back to

humans. Users—recast as human coprocessors—manually reconcile

thin rules against fat-tailed realities. Nothing in this posture

improves the software’s intelligence; it merely hides the absence

behind modern interfaces. The incentives align: vendors bill by

seats, storage, and modules, not by uplift; genuine unattended

decisions would reduce seats and screens. In Intelligence we noted

the culture clash: records vendors optimize CRUD productivity

and uptime; systems of intelligence optimize economic decision

quality under uncertainty. The former has no incentive to build

the latter.

Hype cycles amplify the misﬁt. Every few years, a wave of

“AI

driven planning”, “digital twins”, or “control towers” promises

a clean break from spreadsheets. Startups are acquired and folded

into suites; the add-on inherits the host’s record/report DNA and

reappears as a conﬁgurable ruleset with alerts. The combinatorics

remain unaddressed; variability is ﬂattened into averages; costs

are proxied by arbitrary service targets. The result is continuity

under new acronyms.

Analyst quadrants stabilize the story. Because the research

economy is funded by the vendors it rates, superﬁcial features

and fashionable labels are rewarded. The list of “leaders” ro-

tates slowly; the criteria privilege breadth of catalog and refer-

enceability over unattended decision quality. On the client side,

RFP theater—critiqued in Deployment—produces adverse selec-

tion: questionnaires canonize yesterday’s artifacts, demos reward

choreography, and the winner is the supplier best at answering

questions that never should have been asked. When the stack goes

live, the exception list returns.

Operationally, the failure modes are consistent. Category confu-

sion creates resource contention (optimizers inside the ledger starve

workﬂows) and information loss (derived artifacts are ingested as

if they contained new information). Semantic drift—tolerable in

a ledger when humans supervise—becomes lethal when fed to a

11.4. WHO SUSTAINS IT 461

decision engine that nobody lets decide. Where a system of in-

telligence would recompute from raw, authoritative records and

write back priced, auditable commitments, the packaged “planning”

tier consumes pre

digested numbers and emits suggestions that

planners must edit to stay safe.

None of this requires bad faith; it is the equilibrium implied

by the business model. Vendors price access, not coins; buyers ask

for screens, not unattended emissions. Thus, a stable mediocrity

persists. The remedy is not a new quadrant but a diﬀerent contract

and a diﬀerent architecture: keep records, reports, and intelligence

strictly disjoint; run the decision engine as a stateless, replayable

asset that ingests raw records, carries probabilistic inputs, prices

trade

oﬀs in money, and writes commitments back with dossiers

that expose their drivers. Select suppliers through adversarial

market research rather than RFPs; pay in proportion to unattended

decision scope and measured uplift. Where these conditions are met,

alert theater dies of disuse; where they are rejected, spreadsheets

resurface—an outcome that should be read as the diagnostic it is.

11.4.2 Consultants

Consultancies thrive by promising change that preserves what their

clients are least willing to disturb: the applicative landscape, the

org chart, and the daily rituals that assign accountability without

owning outcomes. In the equilibrium described earlier, vendors sell

records and reports under “planning” labels; consultants complete

the loop by supplying the rhetoric and choreography that let brittle

tools pass for progress. The deliverable is seldom a decision but a

narrative: maturity models, quadrants, roadmaps, and ceremonies

that rebrand yesterday’s routines.

The literature that underwrites these engagements is volumi-

nous and shallow at once. It catalogs horizons, capabilities, and

archetypes; it partitions the world into neat tiers and dial settings;

it multiplies 2×2 matrices whose corners invite an inﬁnity of pages.

Such segmentations are not knowledge; they are staging. They

evade the economic question—what allocation of scarce resources

462 CHAPTER 11. STAGNATION

improves proﬁt under variability—and substitute a taxonomy that

can be taught and sold. As argued across this book, classiﬁcations

earn their keep only when they predict; most consulting taxonomies

do not.

Inside the engagement, buzzwords supply momentum. “S&OP

alignment”, “integrated business planning”, “demand-driven”, “dig-

ital twin”, “control tower”, “resilience by design”—none is intrinsi-

cally absurd, but all are routinely deployed as covers for unchanged

decision loops. The work product is workshops, cadence charts, and

“one-number” committees. The same pattern reappears: exception

lists grow, spreadsheets metastasize, and the hardest cases—fat

tails, binders, ratchets, incoherent semantics—are politely routed

back to the very clerks the software failed to relieve.

This cycle repeats because it is safe. Reorganizing meetings

does not threaten the ERP; renaming phases does not expose

premises to falsiﬁcation; publishing a “target operating model”

does not force a single coin-denominated commitment to be placed

without human arbitration. The engagement pleases every con-

stituency: executives receive a story of progress; middle man-

agement retains vetoes through ritual checkpoints; vendors keep

modules in place; the consultant departs with references. The

change in the ﬂow of goods is negligible.

A workable standard exists to separate choreography from

substance. In Information, Intelligence, Decisions, and Deployment

we set the bar: daily, full

scope dual

run on authoritative extracts;

promotion only on measured uplift in coins. By that yardstick, most

consulting interventions are non

events: they emit no unattended

decisions, carry no probabilistic futures, price no trade

oﬀs, and

accept no halting responsibility. They narrate; they do not allocate.

The economic test is simple. If a proposition cannot be run

tomorrow night against the client’s end-of-day extracts, audited

in coins, and—if it survives—paid for in proportion to uplift, it

belongs to theater, not practice. When consultants are willing to

sign for unattended emissions and accept reversibility and uplift as

the basis for fees, they cease to be consultants in the conventional

sense; they become co

authors of the numerical recipe. This is

11.4. WHO SUSTAINS IT 463

rare, not because the work is mystical, but because the prevailing

business model rewards ceremonies over code.

The “consultant cycle”—periodic transformations that rename

the same routines—will persist until buyers invert the incentives.

The remedy, already outlined in Deployment, is adversarial market

research: have vendors and would

be advisors draft and critique

each other’s problem statements, demand back

the

envelope

TCOs and peer lists, and pay only for unattended scope and

measured uplift. Where the engagement produces emissions, keep

it; where it produces quadrants and cadences, let it lapse. The next

section shows how a similar equilibrium reproduces itself inside

the ﬁrm under the banner of “data science”—insights without

ownership standing in for decisions.

11.4.3 Corporate data science

Inside ﬁrms, “data science” has largely reproduced the same equilib-

rium sustained by academia, vendors, and consultants: it produces

insights without taking ownership of emissions. Since the early

2010s, teams have multiplied dashboards, pilots, and forecasts; yet

few organizations can point to daily purchase orders, allocations,

or prices issued unattended by code and written back to ledgers.

The work looks modern, but it stops short of placing bets in the

world.

Three traits recur. First, deliverables remain forecast

centric

and KPI

centric. Error bars, accuracy scores, and tidy graphs

abound, while the ﬁrm

wide money ledger—the only common

yardstick—is absent. Uncertainty is described, not priced. Second,

the engineering junction is elided. Models are built atop derived

artifacts (ABCs, buﬀers, “cleansed” tables) instead of authoritative

records; ﬁeld semantics drift; halting conditions are undeﬁned.

When a recommendation is brittle, an “exception” hands the

hard case back to a planner. Third, incentives reward slides and

proofs

concept rather than unattended commitments. “Decision

support” becomes a convenient euphemism for preserving human

arbitration over the very decisions software should make.

464 CHAPTER 11. STAGNATION

The result is insight theater that complements vendors’ alert

theater. Planners end up reconciling brittle rules against fat-

tailed realities by hand. The combinatorics that justify com-

puters—millions of micro

options each day—are pushed back to

people. Clericalism, now animated by dashboards, persists.

The alternative is well within reach and has been sketched

across this book. Replace the notion of an insight factory with a

Supply Chain Scientist who owns a numerical recipe: a compact

program that ingests authoritative records, carries probabilistic

views of demand, lead times, and reliabilities, prices trade

oﬀs in

coins at ﬁrm scope, and emits unattended decisions. The Scientist

is accountable for emissions, not for decks. This posture forces

tacit assumptions into the open and binds modeling to economics.

The operational vignette above (ﬂattening the Monday pile

by pricing dock saturation and admitting “do nothing yet” as

an option) is typical. No reorgs, no new warehouses: the same

assets deliver better because decisions changed. This is what

day

day ownership accomplishes. As soon as dock capacity

carries a shadow price, MOQs become penalties rather than edicts,

waiting competes economically with buying, and the weekly rhythm

stops dictating losses. A system of intelligence turns such reasoning

into unattended practice; a data-science dashboard does not.

Accountability also imposes proportion. Once costs, constraints,

and ratchets are encoded end

end, marginal gains in a sub

model

are pursued only when they move coins at the emission boundary.

Fashionable metrics (forecast accuracy, service level) lose their

talismanic status; what matters is uplift in risk

adjusted operating

proﬁt after write-back. Collaboration then shifts naturally: ﬁnance

publishes capital charges and valuations; procurement publishes

rebates, MOQs, and reliabilities; operations publishes capacities

and cutoﬀs. The recipe binds these inputs nightly under dual

run,

so learning compounds where it pays.

This is how the internal equilibrium breaks. Data science stops

narrating the ﬂow and starts running it—under safeguards that

make automation safe to trust: daily replayable extracts, state-

less computation, whiteboxed dossiers, and reversible emissions.

11.5. THE ORGANIZATIONAL TRAP 465

The chapters Information, Intelligence, Decisions, and Deployment

have already set the harness; here the point is institutional: until

someone is paid for unattended decision quality, “data science”

will continue to feed clerical loops. The next section arms read-

ers with rejection ﬁlters for the literature that nourishes those

loops—materials that look busy yet fail the tests of prediction,

pricing, and falsiﬁcation.

11.5 The organizational trap

External constituencies—academia, vendors, consultants—help ex-

plain why the equilibrium holds, but the decisive brake is internal

to ﬁrms. Supply chain retains second-class status, de facto subordi-

nated to corporate IT. Reputation, bureaucracy, and ticket latency

interlock until the organization accepts clerical vigilance in lieu of

engineered intelligence. The remedy is not another committee: a

structural reallocation of ownership and a thin seam of data that

frees ﬂow from platform vetoes.

11.5.1 Second-class status

Supply chain is praised as “mission

critical”, yet treated as pe-

ripheral. Because trucks keep moving and shelves are stocked,

leadership infers that only incremental polishing is warranted. The

appearance of continuity becomes an alibi for underinvestment in

code and the people who write it. A self-fulﬁlling loop follows:

prestigious recruits aim elsewhere; the department inherits routines

rather than authors decisions; shadow spreadsheets proliferate to

compensate for fragile “planning” add-ons; the resulting clericalism

conﬁrms the original prejudice that nothing better is possible.

The reputation gap is old—logistics as the dull chore after

the “real” work of product and brand—and it still shapes staﬃng

and budgets. What makes the loop particularly tenacious is its

intellectual veneer: singular, noneconomic KPIs — a “98 % service

level,” “forecast accuracy” — masquerade as objective. When

466 CHAPTER 11. STAGNATION

livelihoods depend on such surrogates, buﬀers inﬂate, exceptions

are deferred to humans, and the ledger glows even as coins leak. The

department looks busy and safe, even as it refuses the probabilistic

economics that would measure trade

oﬀs in money and automate

the mundane.

11.5.2 Bureaucracy and ticket latency

Where intelligence is manual, bureaucracy thrives. Alerts and

exceptions are routed through rituals—monthly “one

number” con-

claves, pre

allocation councils, approval cascades—whose true func-

tion is to distribute accountability rather than to improve decisions.

Each ritual adds a layer of process without reducing uncertainty;

the combinatorics remain unchanged, only now ﬁltered through

more hands.

Ticket latency completes the trap. Any substantive change—an

added constraint, a revised cost schedule, a subtle semantic cor-

rection—requires ﬁling tickets against central systems. Weeks

turn to months as priorities are triaged by platform risk, not by

expected economic uplift. By the time a ticket clears, the business

has moved on and the patch is already stale. What should be a

daily, reversible code edit inside a decision engine is recast as a

cross-department project; the status quo prevails by default.

Compromise therefore drifts toward the smallest step all parties

can tolerate. “Machine

learning” modules are grafted on as dash-

boards that defer hard cases to an exception list; ERP upgrades

unify ﬁelds but leave the decision rules untouched. The facade

changes; the clerical loop persists.

11.5.3 Subordination to IT

Corporate IT’s charter is uptime, standardization, and compliance.

This is proper for systems of records and reports. It is ill-suited to

systems of intelligence. When “planning” is hosted inside the ledger,

heavy analytics contend with workﬂows; when it sits atop reporting

views, uncertainty is ﬂattened and decisions devolve into alert

11.5. THE ORGANIZATIONAL TRAP 467

theater—users as human coprocessors. Budgets follow the charter:

capital ﬂows to CRUD platforms; decision automation is purchased

as a module and delivered as conﬁguration plus exceptions.

Risk is inverted: deviation from the platform is treated as

danger; economic loss from inferior decisions is treated as business

as usual. Gatekeeping then hardens the boundary conditions:

derived artifacts are treated as authoritative, schema changes are

anathematized, and any tool that cannot be absorbed by the suite

is rejected on principle. Under such governance, even sincere

attempts at “advanced analytics” resolve into dashboards feeding

the clerical loop.

11.5.4 Patch accretion

Half-measures accumulate. A temporary workbook to smooth a

rebate ladder, a bespoke report that reconciles two masters, a

per

vendor exception in a buyer’s tool—each acquires dependents

who rely on its outputs. Over time the patchwork gains defenders,

and removing any one part appears riskier than leaving all in place.

What began as a stopgap hardens into custom.

Patch accretion scatters accountability. No one owns the eco-

nomic quality of the whole; each team owns a sliver and a story.

This is wickedness in miniature: the problem mutates as each

patch adapts to its neighbors, and survival is mistaken for success.

The cure is architectural rather than exhortatory. A seam

that makes facts portable and a stateless recipe that turns those

facts into priced, auditable commitments dissolve patches into

parameters. With daily dual-run, veto power retreats; patches are

either encoded properly or retired.

11.5.5 Seam and ownership

Two structural moves break the trap.

First, establish a seam: a faithful daily extract covering the

systems of records. It is boring on purpose: append-only snapshots

with manifests; enough history to cross a full cycle; no “helpful”

468 CHAPTER 11. STAGNATION

upstream massaging; least-privilege reads. With it, the decision

engine can be stateless and replayable. Tickets that once sought to

alter ledgers become code changes against a private copy; semantics

are audited empirically rather than negotiated by email. Latency

shrinks from months to hours, and reversibility becomes routine.

Second, assign ownership of the numerical recipe to supply

chain. Roles follow naturally (see Deployment): a Data Oﬃcer runs

the seam; a Supply Chain Scientist authors the recipe; an Executive

Sponsor arbitrates prices and boundaries; a Flow Manager brings

ﬁeld sense and escalates anomalies. Vendors and consultants are

judged by emissions and measured uplift, not by screens.

Absent such a seam and such ownership, nobody is even posi-

tioned to ask—let alone answer—the only question that matters

operationally: why are we not using modern computation to handle

uncertainty and allocate scarce resources more proﬁtably?

These two moves demote bureaucracy and platform vetoes

from governors to parameters. Once a seam exists and ownership

is clear, the department is no longer a second

class custodian of

other people’s software; it becomes the author of its economics in

code. The rest of this chapter arms the reader with ﬁlters to reject

materials that cannot survive such a harness. If a proposition

cannot run nightly against the seam, and defend its emissions in

coins, it belongs to theater, not practice.

11.6 Rejection ﬁlters

The bottleneck today is not a shortage of material but its sur-

plus. More than a million papers and tens of thousands of books

parade under the “supply chain” banner; the pile grows faster

every year while day

day practice remains spreadsheet

centric.

The simplest explanation is that most of this production lacks the

properties that make knowledge operational—properties that let

unattended software place proﬁtable, auditable bets every night.

This section oﬀers three ﬁlters to discard what will not help: trivia,

authorities, and puzzles. They are not a taxonomy of the literature;

11.6. REJECTION FILTERS 469

they are a reader’s toolkit for protecting attention.

11.6.1 Trivia

Trivia catalogs the world—lists, matrices, horizons—without show-

ing why the classiﬁcation is essential, not accidental. Ockham’s

razor applies: do not multiply categories beyond necessity. Here,

necessity means power to predict and economize in coins. A seg-

mentation earns a place only if learning it reduces uncertainty about

future outcomes and improves the engine’s unattended allocation

decisions.

The periodic table clariﬁes the standard: chemistry’s grid is

not a decoration; it compresses causality. Atomic number orders

the elements so behavior in reactions becomes predictable; the

table is a compact, falsiﬁable theory of matter. By contrast, much

planning jargon is mapmaking without geology. Splitting “plan-

ning” into strategic, tactical, and operational horizons says little

unless the split predicts diﬀerent money-denominated choices un-

der uncertainty; otherwise the trichotomy is mere scenery. Likewise

for product ABCs, “fast, slow, non

moving”, “local, regional, na-

tional” sites, or the immortal 2×2 quadrants

where “autocratic

vs. consensus

seeking” crosses “think

then

act vs. act

then

think”

These devices are easy to teach and inﬁnitely recombinable; their

predictive content is typically nil.

The test is blunt: if adopting a classiﬁcation does not change

emissions—purchase orders, transfers, prices—it is trivia. In the

earlier operational vignette, nothing about the weekly inbound

pile-up required a new quadrant or horizon. The ﬁx came from

pricing dock saturation, replacing averages with distributions, and

admitting “do nothing yet” whenever the option value of waiting

dominated. A handful of prices and distributions moved coins; a

shelf of segmentations would not.

It’s always 2×2 matrices.

See “Leadership styles”, page 149, Dynamic Supply Chains (2010), John

Gattorna.

470 CHAPTER 11. STAGNATION

11.6.2 Authorities

You’re in no position to lecture the public about anything.

Opening monologue of the Golden Globes (2020), Ricky

Gervais.

Prominence is not proof. Executive quotes, celebrity forewords,

and brand

name anecdotes entertain; they do not decide. Treat

the appeal to authority as a red ﬂag unless it crosses three bridges:

falsiﬁability, counterfactuals, and emissions. Falsiﬁability means

the claim could turn out wrong once encoded; counterfactuals

require the speaker to state what would likely have happened

otherwise; emissions require the claim to be precise enough to alter

unattended commitments tomorrow night.

Large ﬁrms complicate the matter. Success attaches to many

causes—product, brand, capital structure—while supply chain

remains secretive by design. Even where the chain truly underpins

success, the public spokesman may not be its author; outsized

contributions are unevenly distributed, while many linger doing

very little

. And those who are responsible have incentives to

keep eﬀective methods tacit or to recast them as palatable rituals.

Counting quotes is not the road to knowledge.

The operational standard is narrow and fair. A proposition

from a famed operator or vendor earns attention only once it can

be written as code that ingests authoritative extracts at day’s close,

emits, and defends each line in coins. Until then, ﬁle the story as a

hypothesis and resist building processes around it. Authority may

inspire; it may not substitute for falsiﬁcation.

11.6.3 Puzzles

Academic supply

chain literature tends to optimize what is tidy to

write, grade, and publish—stylized puzzles with ﬁxed assumptions.

Puzzles can be delightful and, at times, useful as subroutines. They

Any CEO knows that half of his employees are doing nothing of value, but

he doesn’t know which half.

11.6. REJECTION FILTERS 471

become harmful when presented as supply

chain knowledge absent

the junction where knowledge must live: semantics, economics,

and code that runs every day. The issue is not mathematics; it is

misframing. Supply chain is applied economics under variability; a

paper that never places bets—never emits unattended decisions—is

not yet in the arena.

Two questions separate a subroutine from a system. First, can

the proposal ingest the original records without laundering meaning

through derived artifacts? Second, does it carry uncertainty and

price trade-oﬀs all the way to the emission boundary? If the answer

to either is “no”, the contribution remains a puzzle. The correct

harness has been described throughout this book: daily dual

run

on full-scope extracts; halting heuristics; dossiers that expose the

top monetary drivers of each line; promotion only on measured

uplift; reversibility through stateless, replayable computation. A

method that cannot survive this harness is not “early stage”—it is

untested.

This ﬁlter is not hostile to theory; it is hospitable to reality.

Queueing results that calibrate congestion costs, programming

blocks that assemble a truck, concentration bounds that regularize

fat tails—these are welcome once embedded in a numerical recipe

that moves coins. Mathematical elegance without unattended

emissions is mere storytelling in symbols. The literature will

modernize when papers ship code that passes the same economic

test we require of production: under the same constraints and

audit trail, does it raise risk-adjusted operating proﬁt?

11.6.4 Using the ﬁlters

Applied consistently, these ﬁlters compress a reading list to mate-

rials that change emissions. Trivia falls to Ockham’s razor unless

it demonstrates predictive, money-denominated power; authorities

are accepted only once their claims can be falsiﬁed under dual-run;

puzzles advance only when they attach to unattended, auditable

decisions. This is why so much activity coexists with little progress:

most of what circulates fails these tests while looking busy. The

472 CHAPTER 11. STAGNATION

remedy is not cynicism but discipline: price uncertainty, demand

emissions, and let coins—not credentials—arbitrate.

11.7 Looking onward

Looking onward, the point is not another manifesto but a change of

posture. The yardstick proposed throughout this book is practical

and narrow: a supply chain advances when, every night at the

closing bell, software emits unattended, auditable commitments

priced in coins—or halts itself and explains why. Everything else is

staging. By this measure, progress is neither a quarterly ceremony

nor a new screen; it is the steady replacement of clerical reconcilia-

tion with a mechanical ﬂow that carries uncertainty explicitly and

prices trade-oﬀs at the scale of the ﬁrm.

The path requires no revelation—only two durable moves that

place responsibility where it can compound. First, make facts

portable through a seam: schema

faithful, immutable extracts

that let the decision engine be stateless and replayable. Second,

condense the ﬁrm’s economics into a numerical recipe that ingests

those extracts, carries probabilistic views of the future, assigns

penalties and rewards in money, and emits unattended decisions.

Whiteboxed dossiers that accompany each emission expose the

few drivers that mattered in coins. With this discipline, improve-

ment becomes routine: when reality falsiﬁes an assumption, the

recipe changes, replays, and resumes. Errors do not accumulate;

knowledge does.

People do not vanish; their work acquires leverage. Planners

become stewards of frames and prices rather than line

editors of

suggestions. Finance publishes capital charges and valuations that

the engine must honor; procurement publishes rebates, MOQs,

and reliabilities as prices rather than vetoes; operations publishes

capacities and cut-oﬀs as auditable constraints. Leadership ar-

bitrates trade-oﬀs in money, not in slogans, and owns the rare

occasions when the engine halts. The test is the same for everyone:

did the emissions raise the ﬁrm’s risk-adjusted rate of return?

11.7. LOOKING ONWARD 473

Alternatives—alert theater, plan theater, insight theater—can

be kept alive indeﬁnitely because trucks still move and averages

still comfort. Its cost is largely invisible in a day’s snapshot yet

crushing over years: options neglected, tails mispriced, attention

spent grooming artifacts that carry no information. The choice

facing mature ﬁrms is therefore not between stability and risk, but

between a clerical equilibrium that quietly taxes every ﬂow and a

mechanical discipline that makes uncertainty pay.

Before the constructive program, we must be blunt about

what stands in the way. The next subsection names the habits

that guarantee clerical equilibria no matter the software or the

rhetoric. Only after discarding them does the better path—already

sketched in earlier chapters and entirely compatible with existing

ledgers—become straightforward to adopt.

11.7.1 What must stop

If progress is to be more than a slogan, three entrenched perfor-

mances must cease. Each has a pedigree; each ﬂatters professional-

ism; and each reliably preserves clerical equilibria while computers

idle.

First, alert theater must stop. Software that styles itself “au-

tomated” yet emits lists of exceptions for humans to reconcile is

not automation; it is an interface for postponing decisions. A

system that cannot write back unattended and expose its drivers

in coins reduces planners to reconciling exceptions by hand—and

calls it progress. The criterion is simple and non

negotiable: either

the program issues commitments to the ledgers—orders, alloca-

tions, prices—or it is a reporting tool and should be judged (and

purchased) as such. Anything that requires daily line

editing to

be “safe” belongs to the past. Seats, screens, and alerts are no

substitute for emissions.

Second, plan theater must stop. Monthly consensus rituals

around a “one set of numbers” ﬂatten uncertainty, with ignorance

masquerading as precision. Forecast accuracies, service

level tar-

gets, and other plan

adherence indicators are not economics; they

474 CHAPTER 11. STAGNATION

are numerically phrased etiquette. They reward buﬀers, not judg-

ment; compliance, not proﬁt. Where edicts are declared “hard con-

straints”, they typically mask unpriced preferences; where averages

stand in for futures, tails are ignored until they bite. The remedy is

not decorum but prices: treat most limits as penalized constraints

in coins; let “do nothing yet” compete with “commit now” through

an explicit option value of waiting; replace plan adherence with

the only test that matters—under the same constraints and audit

trail, did the emissions raise the ﬁrm’s risk

adjusted rate of return?

Absent this discipline, plans will continue to look impeccable while

trucks idle at saturated docks.

Third, insight theater must stop. Dashboards, control towers,

and “digital twins” that never place a bet are ornaments. So are

pilots that live forever in sandboxes, “visibility

only” programs that

move no levers, and procurement rites (RFPs) that select vendors

on choreography rather than unattended decision scope. Inside

the ﬁrm, the mirror image is a data-science function that produces

slides and prototypes yet refuses ownership of write-backs. The

standard is again mechanical: nightly dual

run on faithful extracts;

stateless, replayable computation; halting on insanity; promotion

only on measured uplift; reversibility by design. Anything less

narrates the ﬂow; it does not contribute to it.

These three performances—alerts in lieu of decisions, plans in

lieu of prices, insights in lieu of emissions—persist because they

are comfortable and deniable. They end the moment leadership

demands coin-denominated unattended decisions rather than seats

and ceremonies. Only then does the constructive program that

follows have room to work at all.

11.7.2 Towards a better way

The better way is prosaic in routine, radical in consequence. It

treats tomorrow’s uncertainty as priced raw material and turns the

daily close into a disciplined reckoning. From authoritative records,

a compact numerical recipe recomputes the ﬁrm’s admissible moves,

carries probabilistic views of the future, attaches coin-denominated

11.7. LOOKING ONWARD 475

payoﬀs and penalties, and either writes back unattended commit-

ments or halts itself when conﬁdence drops. Nothing else qualiﬁes

as progress. Everything that matters—options, tails, couplings,

ratchets—is expressed in code that can be audited, replayed, and

revised without ceremony.

Once this discipline is in place, the day takes on a diﬀerent

texture. Insanity disappears ﬁrst: dock saturation is no longer a

surprise averaged away in weekly dashboards; “MOQs as edicts”

dissolve into priced trade-oﬀs; an explicit option value lets “do

nothing yet” compete fairly with “commit now”. Trust accumulates

not in meetings but through whiteboxed dossiers that explain, line

by line, in coins, why an order was placed, a transfer withheld, or

a price nudged. When reality falsiﬁes an assumption, the engine

halts; the premise is corrected; the run replays; the ﬂow resumes.

Wrongness becomes cheap and reversible; rightness scales.

Two brief vignettes show this in practice. A seasonal fashion

label opens with generous bets but replaces push plans with policy:

assortments are evaluated as options; early sell-through updates

a basket-level probabilistic view; markdowns become a reiﬁed

function rather than after-the-fact remorse. The engine prices

stockout pain against obsolescence in one ledger, lets late binding

prevail where half-life is long, and writes unattended allocations

and price moves each night. The visible eﬀects are not theatrical:

fewer mid-season transfers, smaller end-of-season ﬁre sales, and

steadier gross margin. Beneath the surface, the gains come from

admitting tails, pricing them, and letting postponement win when

it should.

In civil aviation MRO, a rotable pool is governed as a portfolio

of options. Unscheduled removals are modeled as fat-tailed arrivals;

AOG exposure carries a shadow price; cannibalization, lease, repair,

and buy are admissible moves ranked by expected risk

adjusted

return. Many nights the most proﬁtable action is to wait; on others,

the engine pre-positions stock against a rising risk shadow, not a

calendar. What used to be a chain of emails becomes unattended

dispatch and purchase orders. Each carries a dossier that exposes,

in coins, why a part ﬂew to this tail number, why another stayed

476 CHAPTER 11. STAGNATION

on the shelf, and why a lease beat a repair given turnaround

dispersion. Fewer crises are “resolved” by heroics because fewer

are manufactured by earlier edicts.

Institutional consequences follow naturally. Roles tilt from

arbitration to authorship: planners become stewards of frames and

prices; ﬁnance owns capital charges and valuations the recipe must

honor; procurement publishes rebates, MOQs, and reliabilities

as schedules rather than vetoes; operations states capacities and

cut-oﬀs as auditable constraints. Vendors and advisors are paid for

unattended scope and measured uplift, not for seats, screens, or

ceremonies. Budgets move toward the only asset that compounds

this century: a small codebase that writes the ﬁrm’s economics

down and improves under experimental optimization.

None of this demands revelation. The instruments have been

laid out already to achieve unattended decisions. Demand a change

of posture: accept that every commitment is a bet whose quality

can be stated in coins ex ante and audited ex post, and organize

work so that software places these bets at the cadence reality

allows.

The book opened by insisting that supply chain is an intent,

not a thing, and closes by making that intent executable. When

a ﬁrm can explain each nightly commitment in coins and stop

itself when it cannot, it has crossed the line that separates theater

from mastery. The rest is slow compounding of knowledge—seeing

a little farther each evening and foreseeing enough to deserve

tomorrow’s return.

Annex A

Technical notes

A.1 Aperiodic rate of return

This annex formalizes the aperiodic rate of return referenced

in Chapter Economics. The intent is practical: each option (a

micro

decision that commits scarce resources) should be ranked

by the speed at which its tied

up capital is repaid and begins to

compound. Because supply

chain cash ﬂows seldom align with

calendar periods, the option itself selects its most informative

horizon.

We consider a discrete time grid

= 0

, . . . , T

max

(days, weeks,

or months). Let

s(t) ∈ R

and r(t) ∈ R

denote, respectively, the present

valued spending and revenue at

time

associated with the option. (If discounting is desired, convert

future amounts to present value ﬁrst; see the next section.)

Deﬁne the total capital the option requires—valued at

0—and the cumulative revenue up to any cutoﬀ T :

S ≜

max

t=0

s(t), R(T ) ≜

t=0

r(t).

477

478 ANNEX A. TECHNICAL NOTES

The aperiodic rate of return is then

RoR

aper

= max

0<T ≤T

max



R(T )

− 1



Interpretation. Slide a cursor along the option’s cash

ﬂow timeline.

At each date

, compute the per

unit

time return you would have

realized if you had closed the position then: cumulative inﬂow

(

), divided by the total capital

the option demands, minus one,

scaled by 1

. The metric keeps the peak value; the corresponding

⋆

is the option’s implicit horizon. Two options with very diﬀerent

calendars become commensurable because each is judged at its

own best repayment point.

Units. If

is measured in days,

RoR

aper

is a daily rate. Annual-

ization—when desired—follows compounding on the chosen grid

(e.g., (1 +

)

365

−

1 for a daily rate

). For small

, multiplying by

the number of periods is a coarse approximation; compounding is

safer once rates are large.

Worked example. A buy today of 100 coins (

= 100) yields

two sell

throughs: 60 coins at day 15 and 60 coins at day 30. The

per-day speeds are

ρ(15) =



100

−1



= −0.026

6, ρ(30) =



120

100

−1



= 0.006

The maximum occurs at

⋆

= 30 days with

⋆

≈

006

per day;

compounded over a year this is roughly (1 + 0

006

)

365

−

≈

(i.e., about 1

030% per annum). Such magnitudes are commonplace

at the item level because they reﬂect fast inventory rotations rather

than portfolio-wide returns.

Two caveats. First, a positive bias: the metric assumes that the

released capital at

⋆

can be redeployed immediately into an equally

attractive opportunity. In practice, matching opportunities may

arrive with a delay. Second, a negative bias: revenues beyond

⋆

are ignored for the sake of measuring speed. In reality, unless goods

are scrapped, later inﬂows will still materialize and lift the ultimate

proﬁt. Both eﬀects are acceptable—indeed useful—because the

purpose here is not to settle the ﬁnal P&L of the option, but to

prioritize scarce capital toward the fastest compounding uses.

A.2. DISCOUNTED CASH FLOWS 479

A.2 Discounted cash ﬂows

When we discount cash ﬂows, we merely restate the earlier dis-

cussion of time preference in coin and on a discrete time grid. A

coin received later is worth less today because it cannot be used,

buﬀered, or reinvested in the meantime. Discounted cash ﬂow

(DCF) converts every future amount into “today

coins” and, once

this conversion is done, the aperiodic rate of return of the previous

section can be applied without any further change.

Fix a time grid

= 0

, . . . , T

max

(days, weeks, or months). Let

denote the ﬁrm’s cost of capital per grid step; if the cost of funds

varies with time (credit line tiers, temporary squeezes), write

The discount factor that brings an amount at date

back to

= 0

D(t) =

τ=1

1 + k

(for constant k, D(t) = (1 + k)

−t

Let

∗

(

) and

∗

(

) be the undiscounted revenue and spending

at time

(with immediate amounts recorded at

= 0). Their

present-valued counterparts are

r(t) = D(t) r

∗

(t), s(t) = D(t) s

∗

(t).

The discounted cash ﬂow of the whole option (the sequence it

entails) is then

DCF =

max

t=0



r(t) − s(t)



max

t=0

D(t)



∗

(t) − s

∗

(t)



This sum is an absolute valuation in today

coins (when positive, it

creates wealth; when negative: it destroys value).

Connection to the aperiodic rate of return. In the previous

section,

(

) and

(

) were explicitly deﬁned as present

valued

ﬂows. Thus, to rank options by speed, ﬁrst discount as above,

then plug the present

valued series into the aperiodic rate formula

480 ANNEX A. TECHNICAL NOTES

unchanged. In symbols, keep

max

t=0

(

) and

(

) =

t=0

(

and evaluate

RoR

aper

= max

0<T ≤T

max



R(T )

− 1



Discounting enforces time preference; the aperiodic rate then re-

ports how fast the present

valued outlay is repaid and begins to

compound.

Choosing

. Use the ﬁrm’s marginal cost of liquidity on the grid

you compute with. If you work with an annual rate

on a daily

grid, convert it to a per

day step by

= (1 +

)

1/365

−

1 (mutatis

mutandis for weeks or months). When liquidity is piecewise or

time

varying, encode it as a path

}

; the deﬁnition of

(

)

already accommodates this. In ﬁnance it is common to “pad”

to reﬂect uncertainty; in supply chain we avoid this double

counting—uncertainty is handled explicitly via the probabilistic

treatment of returns later in the annex.

Worked micro

example (daily grid). Spend 100 coins at

= 0;

receive 60 coins at day 15 and 60 coins at day 30. With an

annual cost of capital of 10%, the daily step is

k ≈

10)

1/365

−

The discount factors are

(15)

≈

10)

−15/365

and

(30)

≈

(1.10)

−30/365

. The DCF is

−100 + 60 D(15) + 60 D(30) ≈ 12.6 coins.

Feeding the present

valued series into the aperiodic metric gives

S = 100 and R(30) ≈ 60 D(15) + 60 D(30) ≈ 112.6, hence

RoR

aper

≈



112.6

100

− 1



≈ 0.0042 per day,

which compounds to roughly (1 + 0

0042)

365

−

≈

6 (about

260% per annum). The precise ﬁgure is not the point; the method

is. Discount ﬁrst to express everything in today

coins; then com-

pare options by how fast those present

valued inﬂows repay the

present-valued outlay.

A.3. INFORMATIONAL ENTROPY 481

Practical notes. Units must match:

is measured in the same

step as

. If payments are staged (e.g., a deposit at

= 0 and a

balance at shipment), treat each as its own

∗

(

). While interest

rates matter, inventory rotations typically dominate in supply

chain; discounting’s role here is to restore comparability across

options with diﬀerent calendars so the aperiodic rate can do its

ranking job cleanly.

A.3 Informational entropy

This annex distills the few pieces of Shannon’s theory used in Chap-

ter Information, in a form directly usable for inventory examples.

We stay with discrete variables and base

2 logarithms so units are

bits (changing the log base merely rescales the unit).

Consider a discrete random variable

that takes states

with

probabilities

(

). The information revealed when we learn that

the state is x—the self-information—is

I(x) = − log

p(x) bits.

A certain outcome (

= 1) carries 0 bit; rarer outcomes carry more

bits.

The entropy of X is the average self-information:

H(X) = E[I(X)] = −

p(x) log

p(x) bits.

Entropy depends only on the probabilities, not on how we name

or store the states. It can be read as the average number of fair

yes/no questions needed to identify the state. Two immediate

corollaries suﬃce for our purposes:

• If X has n equally likely states, then H(X) = log

• If X

, . . . , X

are independent, the joint uncertainty adds:

H(X

, . . . , X

) =

j=1

H(X

482 ANNEX A. TECHNICAL NOTES

(When variables are correlated the joint entropy is below that sum;

we will assume independence here to keep the illustration simple.)

We will model each product’s stock level as a discrete variable

and, as a ﬁrst pass, treat diﬀerent products as independent. This

lets us compute a store’s entropy as “100 times” the per

product

value. The next subsection applies these formulas to two contrast-

ing stores—a luxury watch shop and a suburban depot—and makes

the contrast quantitative. For a compact, accessible primer on the

wider theory, see James V. Stone, Information Theory: A Tutorial

Introduction (2019).

Entropy of two stores

We now make precise the contrast used in the original chapter.

Model each SKU’s on

hand as a discrete random variable and, as

a ﬁrst pass, treat SKUs as independent.

The store

level entropy

is then the sum of the per-SKU entropies.

Luxury watch shop. Each SKU has at most one unit on hand,

with a 95% probability of being available and a 5% probability of

being out of stock. Thus a single SKU is binary with

p(1) = 0.95, p(0) = 0.05.

Using the binary entropy,

(1)

watch

= − 0.95 log

(0.95) − 0.05 log

(0.05) ≈ 0.2864 bits.

For 100 independent SKUs,

watch

= 100 H

(1)

watch

≈ 28.64 bits.

Suburban depot. For each SKU, assume the on

hand can be

any integer from 0 to 9,999 with equal probability. A uniform law

on n states has entropy log

n, hence per SKU

(1)

depot

= log

(10,000) ≈ 13.2877 bits,

Independence is a simplifying assumption. Correlations—e.g., shared capac-

ity shocks—lower the joint entropy relative to the sum of per

SKU entropies;

they do not overturn the order-of-magnitude gap shown here.

A.4. TOPOLOGICAL SORT 483

and for 100 SKUs

depot

= 100 H

(1)

depot

≈ 1328.77 bits.

Numerically, the depot carries about H

depot

watch

≈

times more inventory information than the watch shop.

Read in “twenty

questions” terms, fully specifying the watch

shop’s entire 100

SKU stock state takes on average about 29 yes/no

answers; the depot takes about 1,329. The uniform assumption

slightly overstates entropy relative to realistic, peaked stock distri-

butions, but the gap remains of the same order. This calculation un-

derwrites the chapter’s claim that two assortments of identical size

(100 SKUs) can have radically diﬀerent informational weight—one

measured in a few dozen bits, the other in kilobits—hence the

necessity of software for anything beyond the simplest ﬂows.

A.4 Topological sort

A topological sort arranges a collection of items so that every

prerequisite appears before the item that depends on it. The

input is a directed graph whose nodes are items (tasks, tables,

components) and whose arrows encode “must come before”. If the

graph has no directed cycle, such an order exists; if there is a cycle,

no ordering can honor all prerequisites.

Supply

chain practice is full of such precedence structures. A

bill of materials requires sub

assemblies before the assembly; a

daily numerical recipe must ingest records before deriving features,

forecast before valuing options, and value options before emitting

purchase orders or transfers. In a system of intelligence, this

structure is made explicit as a directed acyclic graph (DAG) and

executed in that order at each run.

Intuition. Start with everything that has no prerequisite; place

it in a worklist

. Repeatedly pick one item

from

, output

it, and relax its outgoing arrows: each successor

has one fewer

prerequisite; when that count reaches zero,

joins

. If, in the

484 ANNEX A. TECHNICAL NOTES

Algorithm 1 Kahn’s topological sort

Require:

Directed graph

= (

V, E

) where an edge (

u→v

) means

“u before v”.

Ensure:

Either an order

that respects all edges, or error if a

cycle exists.

1: Compute in[v] ← |{ u | (u→v) ∈ E }| for every v ∈ V ▷

number of prerequisites

2: S ← queue of all v with in[v] = 0 ▷ no prerequisites

3: L ← empty list

4: while S is not empty do

5: remove a node n from S ▷ use a stable key for

reproducibility if desired

6: append n to L

7: for all edges (n→m) ∈ E do

8: in[m] ← in[m] − 1

9: if in[m] = 0 then

10: insert m into S

11: end if

12: end for

13: end while

14: if |L| < |V | then

15:

return error

▷

the remaining nodes participate in at least

one cycle

16: else

17: return L

18: end if

A.4. TOPOLOGICAL SORT 485

end, some items were never output, they mutually depend on one

another—a cycle.

Worked supply

chain example. Consider a tiny bill of materials:

Screw and Panel have no prerequisites; Frame requires Panel;

Assembly requires Screw and Frame. One valid order is:

Screw, Panel, Frame, Assembly.

Running the algorithm,

initially holds Screw and Panel; after

outputting Panel, the in

degree of Frame drops to zero and it joins

; after Screw and Frame have been output, Assembly can be

produced. The exact order among simultaneously eligible items is

irrelevant to correctness but matters for reproducibility.

Correctness and complexity. Every edge (

u → v

) is honored

because

can only be output after its last prerequisite is removed,

which happens only after

has been output. Conversely, if an

item is output it had no unmet prerequisite at that moment. The

method runs in linear time

(

|V |

|E|

) with adjacency lists and

a single pass over edges, and uses linear space. In production

systems, make outputs deterministic by taking items from

a ﬁxed key order (e.g., (

site, sku, step

)); the same inputs then

reproduce bit

for

bit the same order, which supports auditing and

time-travel replays.

Operational use in a system of intelligence. The daily numerical

recipe is a DAG: ingest raw extracts, normalize, derive features, ﬁt

or refresh probabilistic models, compute valuations in coins, then

emit idempotent write

backs (POs, transfers, prices). The runtime

performs a topological sort of that computation graph and executes

nodes accordingly. A detected cycle is not a corner case to be

“overridden”; it is a breach of semantics that must halt the engine

(as argued in Chapter Intelligence) and surface a small witness

cycle to ﬁx the logic or the data contract. Producing such a witness

is straightforward: after the algorithm stops with

|L| < |V |

, follow

predecessor links within the residual subgraph until a node repeats;

the loop thus revealed is the minimal explanation the operator

needs.

486 ANNEX A. TECHNICAL NOTES

A.5 Four-layer perceptron

A four

layer multilayer perceptron (MLP) is the smallest useful

specimen of what the “deep learning” section described in words:

a ﬁxed circuit made of linear maps separated by simple non

linear

gates. It is a good vehicle for demystifying the mechanics because

every step reduces to ordinary linear algebra.

We revisit the handwritten

digit toy task (MNIST) used earlier;

see Figure 6.2 in Chapter Intelligence. Each gray

scale image is

a 28

28 array rescaled to numbers in [0

1] and ﬂattened into a

vector x

∈ R

784

. The network computes scores for the ten digits

0, . . . , 9, then turns those scores into probabilities. The predicted

digit is the one with the highest probability.

Forward pass (inference)

The network stacks three aﬃne transforms separated by rectiﬁers.

Using column

vectors and applying non

linearities element

wise,

the computation is:

= W

x + b

, a

= R(z

= W

+ b

, a

= R(z

y = W

+ b

Here W

∈ R

256×784

, W

∈ R

128×256

, W

∈ R

10×128

are weight

matrices; b

∈ R

256

, b

∈ R

128

, b

∈ R

are bias vectors. The

non-linearity is the rectiﬁer

R(u) = max(0, u),

applied coordinate

wise. Rectiﬁers are not only suﬃcient for ex-

pressiveness; they are also cheap to evaluate on silicon, which is

why they displaced older sigmoid-shaped gates.

The output y

∈ R

collects one score per digit. To obtain

class probabilities we apply the soft-max map:

j=0

for k = 0, . . . , 9.

A.5. FOUR-LAYER PERCEPTRON 487

The classiﬁer reports

arg max

; the full probability vector p

is useful during training because it quantiﬁes uncertainty and

supports a smooth loss.

Training by stochastic gradient descent

Training chooses all weights and biases so that the network assigns

high probability to the correct digit on images it has not seen. Let

(i)

, t

(i)

) denote the

th labeled example, with

(i)

∈ {

, . . . ,

}

the true class. The standard loss for a mini

batch

is the average

cross-entropy

= −

|B|

i∈B

log p

(i)

optionally augmented by a small quadratic penalty on the weights

(weight decay) to curb overﬁtting. Modern frameworks compute

the gradient of

with respect to every parameter by automatic

diﬀerentiation. One step of stochastic gradient descent (SGD) then

nudges parameters in the direction that reduces the loss:

θ ← θ − η ∇

where

denotes the collection of all W

ℓ

and

η >

0 is the

learning

rate. Repeating this simple loop—sample a mini

batch,

run the forward pass, evaluate the loss, apply the gradient up-

date—gradually lowers the loss on held

out images until improve-

ments stall. Variants (momentum, adaptive learning rates) reﬁne

the same idea; none alter the basic picture.

Two practical choices matter for stability and speed but do not

complicate the story. First, initialize weights so that activations

neither blow up nor vanish as they propagate: with rectiﬁers,

a zero

mean Gaussian scaled like

) works well,

being the fan

in of the layer. Second, process examples in small

contiguous groups (“mini

batches”) to exploit hardware parallelism;

inference and training then reduce to a handful of dense matrix

products (GEMMs) plus cheap rectiﬁers.

488 ANNEX A. TECHNICAL NOTES

Sizes, costs, and what is really computed

Counting only the weights, the 784

→

256

→

128

→

10 MLP carries

784 × 256 + 256 × 128 + 128 × 10 = 234 752

trainable numbers; the three bias vectors add a negligible 256 +

128 + 10 on top. One forward pass is “three matrix products and

two rectiﬁers.” This mechanical view—linear algebra punctuated

by a simple gate—is the correct mental model. There is no hidden

magic; the network is a programmable circuit whose parameters

are tuned by gradient descent.

A.6 Mathematical optimization

This annex complements the discussion The optimization paradigm

in Chapter Decisions. Its purpose is practical: to restate the

vocabulary of that section—marginal commitments, feasibility

tests, priced penalties, probabilistic forecasts, and economic objec-

tives—in a minimal mathematical form that admits unattended,

repeated resolution.

From decisions to a model

A decision is a ﬁnite set of marginal commitments. Let

, . . . , a

}

denote the admissible micro

moves for the present

cycle (buy one more unit, allocate one more unit to store

, schedule

one more stop, and so on). Selecting a move is recorded by

∈

{

}

; the vector

x ∈ {

}

encodes the whole commitment. The

frame makes

explicit and attaches to each

the data needed

to test feasibility and to value consequences.

Feasibility is expressed as tests that a candidate

must pass.

Some limits are absolute—legal prohibitions, physical impossi-

bilities—and appear as hard tests that reject

outright. Most

operational limits tolerate relaxation at a price; rather than forbid-

ding those violations we price them. Let

(

x, ω

) be a measurable

A.6. MATHEMATICAL OPTIMIZATION 489

quantity the ﬁrm wishes to restrain (dock load on a day, missed

window minutes, overtime hours), computed under a realization

of the uncertain future. A priced penalty

(

), stated in coins,

turns that preference into the objective. Keeping only truly hard

limits as tests keeps the engine ﬂexible and honest; priced penalties

make unlike nuisances commensurable on one ledger.

The objective is money

denominated. Within the window of re-

sponsibility attached to the present decision class, each micro

move

earns coin ﬂows (margins, rebates, salvage) and incurs ﬂows

(procurement, handling, carrying, obsolescence, congestion, late

fees). Under a probabilistic view of futures Ω, the engine evaluates

the expected value of x,

J(x) = E

Ω





i=1

(Ω) −



(x, Ω)







where

(Ω) is the net coin contribution of

over the window once

all ordinary costs are charged. Risk adjustments—e.g., heavier

pricing of tail losses—live in the

terms rather than as abstract

percentages. When ranking small alternatives that tie up capital

for diﬀerent durations, Chapter Economics recommends comparing

by aperiodic rate of return; in practice, this means computing both

the expected coin delta and the tied

up capital and then reporting

speed with the metric deﬁned earlier in this annex. The solver

can maximize

and, for emitted marginals, report the associated

aperiodic speeds.

Uncertainty is carried in the inputs, not hidden in the objective.

The random element Ω collects the distributions the ﬁrm main-

tains for demand, lead times, reliabilities, returns, and any priced

exogenous factor. The objective

is deterministic given those

distributions. This separation keeps causality and blame legible

when outcomes disappoint: either the frame omitted options, or

prices were misguided, or forecasts were miscalibrated.

490 ANNEX A. TECHNICAL NOTES

Penalties versus chance bounds

Two formalisms encode “keep this under control”. A chance con-

straint requires



(

Ω)

⩽ τ



⩾

− α

for some threshold

and tolerance

. A priced constraint assigns a coin cost

(

)

to all values of

. The former hides economics inside a dimension-

less

that must be tuned elsewhere; the latter states trade

oﬀs

directly in money and aggregates naturally with other terms. In

supply

chain operations—where most limits can be overrun at a

cost—the priced form is the workhorse; pure chance bounds are

best kept as guardrails for rare, safety-critical fronts.

A convenient choice is a convex, rising penalty, often piecewise

linear, that approximates the ﬁrm’s nuisance curve. For dock load

beyond a soft capacity

with sharp pain after

+∆, one writes

(

) =

(

q − K

)

(

q − K −

∆)

with

≫ c

. The exact

numbers are economics; once stated, the solver ﬁnds least

bad

compromises across heterogeneous frictions.

Worked micro-model: ﬁlling a container

An importer must raise a full

container order. Each candidate

unit

carries a volume

and an expected, window

bounded net

coin m

(Ω) that already includes procurement, handling, holding,

obsolescence, salvage, and shortage eﬀects. The container capacity

in the same unit as

. Overﬁlling is possible but painful at

rate λ per excess unit.

A faithful objective is

max

x∈{0,1}

Ω

(Ω) − λ



− W



subject to hard tests such as legal incompatibilities or forbidden

loadings. This is a penalized knapsack under uncertainty. A sim-

ple, eﬀective resolution aligns with the chapter’s marginal stance:

at each step compute a shadow price for capacity, pick the ad-

missible unit with the highest expected marginal

above that

shadow price, recompute the shadows, and stop when the next

A.6. MATHEMATICAL OPTIMIZATION 491

unit fails the economics of waiting cut

oﬀ (expected, risk

adjusted

aperiodic return below capital plus option value of delay). Because

valuation depends on the set in the box and not on the ceremony

of its selection, this greedy accumulation targets what matters

economically; the window and terminal valuations ensure clean

attribution.

Worked micro-model: smoothing inbound

A warehouse faces random supplier drifts and a ﬁnite number of

receiving doors. Let

(

Ω) be the number of arrivals on day

implied by

under Ω. Overtime, driver detention, demurrage, and

disruption are captured by a convex penalty

dock

(

). A daily,

coin-denominated objective over horizon T is

max

Ω





(Ω) −

t=1

dock



(x, Ω)







again with only truly hard limits enforced as tests. The optimizer

advances or defers orders until the expected return of the next

admissible batch exceeds the shadow cost of capital plus the option

value of waiting, while the convex

dock

spreads load without

brittle edicts.