Demand planners have arrived at a finding no forecasting vendor will put in a brochure. Forecast accuracy improvements, including real, verified, hard-won ones, routinely fail to show up in the P&L. The practitioner press says it plainly, with demand-planning.com documenting what is and is not working with AI in demand planning, and academic work from Walton College on how planners actually adjust AI forecasts filling in the mechanism. The models improved. The money did not move.
The wrong conclusion is that accuracy does not matter. The right one is that the gain dies somewhere between the model and the balance sheet, because nobody built the machinery to convert it.
Where the gains go to die
Follow an accuracy improvement downstream and watch what happens to it at each station.
- The safety stock nobody retuned. Inventory buffers were sized for the old forecast error. The error shrank; the buffers did not. The working capital the improvement should have released is still on the shelf.
- The override. Planners who spent years compensating for a bad forecast keep compensating for the good one, adjusting it back toward instinct. The Walton work shows the adjustment habit persists regardless of forecast quality, and since half the forecast is a person, an unmeasured override layer can cancel the model's entire gain.
- The frozen schedule. Production planned against last cycle's forecast cannot consume this cycle's better one. The improvement arrives, waits outside the frozen window, and expires.
Nothing on that list is a modelling problem, which is why buying a better model changes none of it.
Accuracy is potential energy. Decisions convert it.
Why vendors stay silent
Demand platforms compete on accuracy benchmarks, so the sales motion needs accuracy to imply value all by itself. Admitting a conversion layer exists means admitting the headline metric is an input rather than an outcome, and no accuracy-led vendor volunteers that. The practitioners living with flat P&Ls have no such constraint, which is why the honest version of this argument comes from their side of the table.
Wiring the conversion
Feed the forecast into the settings that spend money, not into slides. Recompute safety stocks, reorder points and coverage targets from how wrong the forecast currently runs, so a smaller error automatically releases the capital it earned. Score overrides, so the planner adjustments that add value are kept and the ones that subtract it become a coaching conversation. And give every forecast a decision that consumes it, because an improvement nobody consumes is a report that cost money.
Foresight is wired into the demand modules this way by construction. Forecasts flow to the settings and decisions that convert them, overrides are scored, and the conversion itself is measured, so the accuracy-to-value question has an answer on a page rather than a shrug. Wiring the decision layer is harder to copy than tuning a model, which is exactly why the accuracy-led market declines to try. Start here.