When Supply Chains Fight Back Against Their Own Playbook
When supply chains fail in the real world, the post‑mortem almost never looks like the neat diagrams in textbooks. What you see instead is a trail of decisions: targets that made no sense once they met reality, incentives that pushed people to say one thing and do another, vendors and consultants that sold compelling stories that quietly under‑delivered. Very little of this looks like a neutral, stochastic process that merely needs more data and a better forecast.
In Introduction to Supply Chain, I argued that we should look at supply chains through an adversarial lens. In this essay, I want to clarify what I mean by that, and confront it directly with the mainstream view that dominates textbooks, software brochures, and most consulting slides.
The comfortable fiction of the neutral supply chain
If you open a standard textbook, you will find a reassuringly clean definition: a supply chain is all the parties and functions involved in fulfilling a customer request, from suppliers and factories through warehouses and retailers to the end customer. The stated goal is to coordinate these flows so that the “supply chain surplus” is maximized and the customer is served at the lowest possible total cost.
The world that follows from this definition is largely cooperative. Manufacturers, distributors, retailers and service providers are seen as partners who need to share information and align decisions. Uncertainty is there, of course, but mostly as an external disturbance: demand varies, lead times fluctuate, machines break down. The job of the supply chain professional is to buffer and anticipate these variations with better forecasts, more sophisticated inventory models, and improved planning processes.
Even when mainstream literature looks at “bad behavior,” it usually treats it as a localized pathology. The classic illustration is the bullwhip effect: demand variability is amplified as you move upstream because of forecast updates, order batching, price promotions, and what is typically labeled “rationing and shortage gaming.” Customers and downstream partners place exaggerated orders to grab scarce supply, which then cascades upstream into wild swings in production.
Notice how this is framed. Shortage gaming is a problem to be eliminated so that the underlying cooperative structure of the supply chain can be restored. The proposed remedies are familiar: better contracts, more information sharing, vendor‑managed inventory, and collaborative planning arrangements.
Inside the firm, the same spirit prevails. Sales & Operations Planning (S&OP) and Integrated Business Planning (IBP) are presented as the natural evolution of supply chain management: get sales, marketing, operations, finance and supply chain around the same table; converge on a single demand plan; align capacity and financial projections; monitor performance against that plan. Major vendors describe IBP as a way to bring all functions together under “one plan and one set of numbers” to support better, more collaborative decisions.
This is, in many ways, an attractive picture. It is also deeply incomplete.
Supply chains are made of other people’s decisions
Volatility in a supply chain is not primarily meteorological. It is not weather. It is the aggregated result of other people’s decisions: competitors changing prices, regulators changing rules, customers changing preferences, marketing teams changing promotions, procurement changing suppliers.
Once you recognize that, the environment stops looking like neutral “uncertainty” and starts looking like a field of opponents. Not opponents in the moral sense – they are not necessarily dishonest or malicious – but opponents in the game‑theoretic sense. Their actions affect your payoffs, and your actions affect theirs.
When a competitor launches an aggressive promotion, they are not adding random noise to your demand; they are making a calculated move to capture your customers. When a large retailer pushes you to hold inventory closer to their distribution centers, they are improving their own risk profile at the expense of yours. When a regulatory change suddenly invalidates half your catalog in a country, that is not a shock of nature; it is the outcome of decisions taken by political actors with their own incentives.
Modeling these forces as “exogenous variability” that can be ironed out by ever more refined stochastic models misses the point. The world is not just uncertain; it is contested.
Adversaries inside the firm
The adversarial landscape does not stop at the boundary of the company.
Inside large organizations, people are paid, promoted, and fired based on how they appear to perform under a particular set of metrics and processes. It is entirely rational for them to optimize for those metrics and those processes, even when this goes against the economic interests of the firm as a whole.
Forecasting is a good example. In theory, a demand forecast is an honest probabilistic statement about the future. In practice, it is often a social artifact. Sales may sandbag to protect their quotas. Finance may push for optimistic numbers to support a strategic narrative. Operations may prefer conservative figures to avoid being blamed for capacity shortfalls. Once that forecast becomes “the plan,” it evolves into a shield: if reality turns out badly, people can always say they followed the plan.
The more a company elevates plan compliance as a virtue, the more attractive this shield becomes. Accuracy metrics, frozen demand plans, and consensus meetings create a structure where deviating from the plan is riskier for a manager than silently watching a bad plan unfold. The process is collaborative on the surface, but adversarial underneath: each function tries to shape the numbers in a way that protects its own scoreboard.
Traditional S&OP and IBP frameworks acknowledge misalignment, but mostly as a coordination issue: silos that need to be bridged, incentives that need to be aligned, communication that needs to be improved. The adversarial pattern is deeper. Even with perfect information and excellent tools, as long as careers are tied to local metrics, people will rationally game the system.
When the playbook itself is biased
So far, I have spoken about adversaries in markets and organizations. There is another layer, which, in my view, is even more neglected: the adversarial nature of the knowledge we use to run supply chains.
The philosopher Sergio Sismondo introduced the term “epistemic corruption” to describe what happens when a knowledge system is co‑opted by interests that are at odds with its stated purpose. In his work on the pharmaceutical industry, he documents how drug companies systematically shape medical research, publications, and expert opinion in ways that serve commercial objectives while preserving an appearance of scientific legitimacy.
We should be naïve to believe that supply chain knowledge is immune to similar forces.
A large share of what practitioners read about supply chain – outside a small number of academic journals – comes from vendors and consultants: white papers, case studies, conference presentations, “thought leadership” pieces, benchmark reports. Even textbooks and business school cases often lean heavily on success stories supplied or co‑written by technology and consulting firms.
Everyone in this ecosystem is playing a reasonable game from their own perspective. Software vendors want to sell licenses or subscriptions. Consultants want to sell projects and ongoing advisory work. Academics want publications, citations, and access to data. None of this is scandalous; it is simply how careers are built.
But the cumulative effect is exactly what Sismondo describes in medicine: a body of “knowledge” that looks authoritative, is widely repeated, and yet is systematically biased toward solutions that can be sold, that look good in slides, and that generate repeatable revenue for the providers. Failures, cost overruns, and quietly shelved implementations almost never make it into the public narrative.
Consider the humble case study. Successful projects are written up with glowing numbers and polished stories. Unsuccessful projects vanish into confidentiality clauses and broken relationships. Even if every line in every published case study were technically true, the selection mechanism alone would make the whole corpus deeply misleading. We mostly see the winners, occasionally the acceptable draws, almost never the losses.
If you ignore this, you are not just assuming a cooperative environment in your operations; you are assuming a cooperative environment in your knowledge supply. You are treating the playbook as neutral when it is not.
A more adversarial way to think
What does it mean, in practice, to adopt an adversarial lens without lapsing into paranoia?
First, it means remembering that every important decision is a bet, and asking who else is at the table. When you commit to inventory, capacity, sourcing, prices, or service levels, you are writing a contract with the future. That contract can and will be exploited – by competitors, by partners, by your own organization – whenever it creates an asymmetry that benefits them. Instead of asking “What is the most likely forecast?”, it becomes more natural to ask “If we place this bet, who gains if we are wrong, and how quickly can we adjust?”
Second, it suggests giving more weight to what I like to call negative knowledge: a deliberate catalogue of approaches, patterns, and vendor stories that have repeatedly failed in your context or in comparable ones. Most companies informally remember their disasters; very few maintain a structured memory of them. The result is that the same types of projects are launched again and again, with new acronyms and new logos, but with the same structural weaknesses.
Negative knowledge is not cynicism. It is a way to narrow the search space before you fall in love with a shiny idea. Certain incentive structures almost always produce gaming. Certain metrics almost always turn into targets. Certain project templates almost always produce theatrical change instead of real improvement. Capturing these patterns explicitly makes it harder for them to reenter the organization with new packaging.
Third, when buying technology or consulting, an adversarial stance means using the vendors’ own rivalry as a source of information. Classic RFP processes tend to produce sanitized problem statements and carefully stage‑managed demos. An alternative is to let one provider propose a way of framing your problem, then strip out the sales language and circulate this framing to several competitors, asking them to refine it and to comment, explicitly, on each other’s strengths and weaknesses. No vendor is neutral about their competition; this is precisely why their mutual assessments are valuable, if you collect and compare them with care.
Finally, inside the firm, it means treating metrics and processes as objects of design in a contested system, not as neutral arbiters of truth. A forecast accuracy KPI is not just a way to measure performance; it is an incentive that pushes people to behave in specific ways. A monthly IBP cycle is not just a way to align plans; it is a ritual that allocates voice, status, and blame. If you ignore those dimensions, others will exploit them.
Beyond good intentions
The mainstream supply chain view is not wrong so much as it is incomplete.
We do need rigorous network models, inventory theory, demand analysis, and planning processes. The standard definition of a supply chain as a network of parties and flows remains useful, as do many of the tools built on top of it. It is genuinely valuable to have cross‑functional reviews where finance and operations talk to each other, and to think in terms of integrated business planning rather than siloed budgets.
However, good intentions and elegant models do not neutralize incentives. The fact that a process is designed to foster collaboration does not prevent people from using it to protect their own position. The fact that a piece of software claims to enable end‑to‑end visibility does not guarantee that the visibility will be used to make better bets rather than to enforce compliance with yesterday’s plans. The fact that a white paper carries respected logos does not mean that the story it tells is representative of what actually happens in most implementations.
An adversarial lens simply takes these realities seriously. It assumes that markets, organizations, and knowledge systems are all filled with actors who pursue their own goals, sometimes aligned with yours, often not. It suggests that robustness in supply chain management comes less from assuming away these conflicts and more from designing decisions, metrics, and learning processes that remain useful in their presence.
Closing thought
If you adopt this perspective, you will still need forecasts, planning cycles, software, and partners. But you will approach them differently.
You will see variability as the surface of other people’s decisions. You will treat internal misalignment as a structural fact to be managed, not a misunderstanding to be wished away. You will look at glossy case studies and immaculate maturity models with a friendly, but disciplined, skepticism. And, over time, you will build your own body of knowledge – including its scars – instead of relying on a body of knowledge that others have every reason to curate on your behalf.
Supply chains are not neutral systems waiting to be optimized. They are contested spaces shaped by incentives, power, and information. As practitioners, our responsibility is not only to navigate this terrain, but also to recognize when the very maps we are given have been drawn with someone else’s destination in mind.
