Probabilities, Not Scenarios
Supply chain is often described as the art of balancing demand and supply. I see it more starkly: every purchase order, every production batch, every price change is a bet on a future we do not control. We commit inventory, capacity, cash, and attention today, hoping that tomorrow’s world will reward those commitments rather than punish them.
Because these decisions are bets, the way we think about the future is not a side issue. It is the core of the discipline. In my book Introduction to Supply Chain, I argue that the point is not to preserve harmony around a plan, but to commit resources where the expected, risk‑adjusted return is highest under uncertainty. Everything else is decoration.
Yet most of the supply chain world still works with a planning logic that treats the future as a handful of carefully scripted “scenarios.” My own view, formed over nearly two decades at Lokad, is that this scenario-centric mindset is not just suboptimal; it is structurally at odds with what a supply chain actually is. Supply chains live in probability spaces, not in storyboards.
In this essay I want to explain why I believe probabilistic forecasting is the natural language of supply chain, and why the scenario-centric mainstream – especially as reified in many IBP and S&OP processes – cannot be patched into something adequate. It has to be replaced.
How the mainstream frames the future
If you look at how large companies plan today, the pattern is remarkably consistent across industries and software vendors.
There is usually a baseline forecast, often a single time series per product family or region, generated by a demand-planning module. Around this baseline, Integrated Business Planning (IBP) processes orchestrate a monthly or quarterly ritual where sales, operations, and finance negotiate a “consensus” or “one” plan. Software suites such as SAP Integrated Business Planning explicitly present scenario planning and what‑if simulations as core capabilities: planners are encouraged to run alternate demand or supply scenarios, compare them on dashboards, and select the one they find most appropriate.
Conceptually, the future lives as a small set of named worlds: base case, optimistic case, pessimistic case, perhaps a disruption scenario or two for good measure. Forecasts inside those worlds are deterministic; uncertainty is handled implicitly through service-level targets, safety-stock formulas, and some judgment. Once the favored scenario has been chosen, the organization is expected to “align” to it and to measure adherence with familiar KPIs.
The process feels structured and collaborative. It produces plans that can be explained in PowerPoint. It satisfies the understandable human desire to tell ourselves a story about tomorrow.
But it is a poor way to think about a system that makes, moves, and prices millions of units under relentless variability.
The irreducible uncertainty of supply chains
In manufacturing, it is often possible to buy your way to stability. Invest in better machines, tighter tolerances, more sensors, and defect rates go down. The randomness shrinks.
Supply chains do not offer this comfort. The uncertainty that matters lives in human behavior and in politics: demand shifts, competitor moves, promotions, price changes, upstream shortages, strikes, regulatory surprises. You can improve your data and your models, and you absolutely should, but you will never purchase a deterministic future.
If you accept this, it leads naturally to a different question. Instead of asking, “What is our plan for next quarter?” you start asking, “Given everything we know today, how does the future likely distribute itself, and how should we bet?”
This is where probabilistic forecasting enters.
What probabilistic forecasting really is
Probabilistic forecasting is often misunderstood as a fancy way to draw confidence intervals around a traditional forecast. That is not the point.
For a supply chain, a probabilistic forecast assigns probabilities to all the outcomes that matter: how many units we might sell next week, how long a supplier might actually take to deliver, how likely a product is to be returned, how often a key machine might fail. Instead of one predicted number for demand and one “average” lead time, we get full probability distributions for both.
This perspective is not new in statistics, but it is a genuine paradigm shift for supply chain practice. In lectures and interviews I have described probabilistic forecasting as one of the most important changes in over a century of forecasting science, not because the mathematics is exotic, but because it lets us finally connect forecasting to decision‑making in a sane way.
Once we have distributions instead of single numbers, we can do something that scenario planning cannot: we can evaluate a decision across all plausible futures at once.
Decisions as economic bets on distributions
Consider a very simple example. You are deciding how much of a product to order for next month.
If you rely on a single forecast, you get a number – say, 1,000 units – and you add some safety stock “just in case.” You might also review a couple of scenarios: what if demand is 20% higher, what if a supplier is late. Each scenario feels like a different world. You adjust the order quantity up or down, negotiate, and eventually pick a number.
If you rely on probabilistic forecasts, the logic is different. You have a distribution for demand; perhaps there is a modest chance that sales will be extremely high, and a non‑trivial chance that they will be very low. You have a distribution for lead time; some deliveries are prompt, some are not. You have economic quantities attached to outcomes: the margin you earn if you sell, the penalty (explicit or implicit) if you stock out, the cost of capital tied up in inventory, the risk of obsolescence.
Now you can compute the expected economic outcome of ordering 800 units, 900 units, 1,000 units, and so on. Each order quantity becomes a bet whose payoff is evaluated across the whole probability space, not just a few handpicked scenarios. You can favor decisions that perform well on average and are robust to the tails, rather than ones that look good in a PowerPoint scenario but crumble when reality deviates slightly from the script.
This is not limited to inventory. The same logic applies to network design, capacity reservation, assortment curation, and even pricing. Everywhere we allocate scarce resources under uncertainty, we can ask the same question: given the probabilistic view of the future and our economic assumptions, which decision has the highest expected, risk‑adjusted return?
Scenario planning, as usually practiced, has no such calculus. At best, it offers a few isolated snapshots and leaves management to eyeball the trade‑offs.
Why a few scenarios are not enough
The first problem with scenario planning is granularity. Modern supply chains operate at a scale that makes the scenario approach almost comically coarse. We might have millions of SKU–location–time combinations to decide on, each with its own demand pattern, its own lead-time profile, its own sensitivities to price and promotion. Against this fine-grained reality we set perhaps three or four narratives about the future.
Even if those narratives were perfect, they would still be too few. But they are not perfect. They are the result of judgment, politics, and habit. Which scenarios get written down is itself a random process, influenced more by organizational anxieties than by statistical evidence.
The second problem is that scenarios are rarely assigned explicit probabilities. We have “base,” “upside,” “downside,” but we do not say whether the upside case is five percent likely or fifty. IBP literature speaks warmly about continuous scenario planning, but in practice it means more and faster simulations, not calibrated probability distributions.
The third problem is that scenario planning tends to operate at a high level of aggregation. We run scenarios on total revenue, total capacity, perhaps a few key customers or product families. Meanwhile, the actual economic damage in a supply chain is done by local mismatches: the one component that is missing and grounds the aircraft, the one fashion item that is grossly overbought, the one region that is persistently under‑served. Those failures almost never show up clearly in aggregate scenario charts.
Scenarios appeal to us because they are narrative, nameable, and discussable. They fit our cognitive limitations. But supply chains are not narrative objects; they are stochastic systems with many degrees of freedom. A handful of stories cannot do justice to that reality.
The hidden cost of chasing accuracy and scenarios
For decades, companies have poured effort into improving forecast accuracy as if it were obviously aligned with better economics. We measure MAPE and similar metrics, launch Forecast Value Add initiatives, and celebrate small improvements as victories. Yet on the ground, the correlation between “better” accuracy and better P&L is often weak, sometimes negative.
In other work I have argued that this fixation on accuracy is a large, slow distraction. You can improve accuracy by predicting more zeros on intermittent demand and, in the process, starve your supply chain of the inventory it needs. You can improve accuracy by aggressively following the latest sales signal and inadvertently amplify bullwhip effects. You can generate impressive scenario decks that drive poor execution.
The deeper problem is that “accuracy” and “scenario coverage” are planning metrics that live in a world of numbers detached from prices. They evaluate how close forecasts are to realized quantities or how neatly scenarios cover a manager’s concerns, but they say nothing about the economic consequences of decisions. A small forecast error on a critical spare part can be much more damaging than a large forecast error on a slow‑moving accessory, yet accuracy metrics weight them similarly.
Probabilistic forecasting allows us to re-center the whole discussion on economics. Instead of asking how close we are to the realized number, we ask whether our decisions, evaluated across the full distribution of outcomes, generate good risk‑adjusted returns. Scenario planning, even when adorned with sophisticated tools, encourages us to optimize the stories rather than the money.
How probabilistic thinking changes practice
Embracing probabilities instead of scenarios has several practical consequences.
First, it forces us to be explicit about where uncertainty actually lives. Demand and lead time are not “parameters” to be fixed in a planning system; they are random variables that must be modeled and continually updated as new data arrives. This is as true for upstream reliability and returns as it is for customer demand.
Second, it requires us to attach money to outcomes. A probabilistic forecast without an economic lens is only marginally better than a deterministic one. We need to know what a stockout costs, what overstock costs, what a lost sale is worth, how to value idle capacity. These quantities are imperfect and sometimes uncomfortable to estimate, but they are the only way to compare decisions sensibly.
Third, it naturally leads to automation. Once you can compute the expected, risk‑adjusted payoff of a replenishment order, a price change, or a transfer, there is no reason to debate every decision in meetings. You can let software issue thousands or millions of small decisions every day, while humans focus on shaping the economic model, validating the assumptions, and handling the situations where the model’s confidence is low.
This is very different from an IBP process whose center of gravity is a monthly scenario workshop. Instead of a planning ritual that periodically re‑authorizes a plan, we get an economic engine that continuously arbitrates trade‑offs under uncertainty.
Where scenarios still belong
I am not arguing that companies should ban the word “scenario.” Imagination is essential in any complex endeavor. Boards and executives need narratives to reason about long‑term investments, strategic risks, regulatory shifts, and technology changes.
However, in a probabilistic supply chain, scenarios play a different role. They are not handcrafted futures that the planning system must follow. They are illustrations drawn from, or constrained by, the underlying probabilistic model.
If we want to explore a severe but plausible downside, we do not invent it from scratch; we stress the distributions in ways that are consistent with history and expert knowledge, and we let the same decision engine compute the consequences. If we want to show the upside potential of a more aggressive pricing strategy, we use the probabilistic model to simulate how demand might respond, and we quantify the range of possible outcomes.
In this way, scenarios become pedagogical views on a probabilistic reality, not substitutes for it.
Moving beyond scenario-centric supply chains
The mainstream playbook has had a long run: deterministic forecasts, safety stocks, monthly consensus plans, and a handful of scenarios on top. It has brought some degree of structure, but it has also locked many organizations into a way of thinking that is increasingly incompatible with the complexity and volatility of modern supply chains.
The alternative is not mystical. It is simply to take uncertainty seriously and to express it in the only language that scales: probability. Once we treat demand, lead times, and other key drivers as random variables; once we attach prices to outcomes; once we evaluate decisions as bets across full distributions rather than as responses to a few stories; scenario planning, in its traditional sense, starts to look like what it is: a coping mechanism for the absence of a proper probabilistic, decision‑centric engine.
We do not need more scenarios. We need better probabilities, and a supply chain that knows what to do with them.
