[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"article-body-the-accuracy-gains-that-never-reach-the-pnl":3},"\nDemand planners have arrived at a finding no forecasting vendor\nwill put in a brochure. Forecast accuracy improvements, including\nreal, verified, hard-won ones, routinely fail to show up in the\nP&L. The practitioner press says it plainly, with\n[demand-planning.com documenting what is and is not working with AI in demand planning](https://demand-planning.com/2026/05/07/from-hype-to-execution-whats-actually-working-and-not-working-with-ai-in-demand-planning/),\nand academic work from\n[Walton College on how planners actually adjust AI forecasts](https://walton.uark.edu/insights/posts/does_ai_change_how_demand_planners_adjust_forecasts.php)\nfilling in the mechanism. The models improved. The money did not\nmove.\n\nThe wrong conclusion is that accuracy does not matter. The right\none is that the gain dies somewhere between the model and the\nbalance sheet, because nobody built the machinery to convert it.\n\n## Where the gains go to die\n\nFollow an accuracy improvement downstream and watch what happens\nto it at each station.\n\n- **The safety stock nobody retuned.** Inventory buffers were\n  sized for the old forecast error. The error shrank; the buffers\n  did not. The working capital the improvement should have\n  released is still on the shelf.\n- **The override.** Planners who spent years compensating for a\n  bad forecast keep compensating for the good one, adjusting it\n  back toward instinct. The Walton work shows the adjustment habit\n  persists regardless of forecast quality, and since\n  [half the forecast is a person](/insights/half-the-forecast-is-a-person),\n  an unmeasured override layer can cancel the model's entire gain.\n- **The frozen schedule.** Production planned against last cycle's\n  forecast cannot consume this cycle's better one. The improvement\n  arrives, waits outside the frozen window, and expires.\n\nNothing on that list is a modelling problem, which is why buying a\nbetter model changes none of it.\n\n*Accuracy is potential energy. Decisions convert it.*\n\n## Why vendors stay silent\n\nDemand platforms compete on accuracy benchmarks, so the sales\nmotion needs accuracy to imply value all by itself. Admitting a\nconversion layer exists means admitting the headline metric is an\ninput rather than an outcome, and no accuracy-led vendor\nvolunteers that. The practitioners living with flat P&Ls have no\nsuch constraint, which is why the honest version of this argument\ncomes from their side of the table.\n\n## Wiring the conversion\n\nFeed the forecast into the settings that spend money, not into\nslides. Recompute safety stocks, reorder points and coverage\ntargets from how wrong the forecast currently runs, so a smaller\nerror automatically releases the capital it earned. Score\noverrides, so the planner adjustments that add value are kept and\nthe ones that subtract it become a coaching conversation. And give\nevery forecast\n[a decision that consumes it](/insights/what-a-prediction-owes-the-decision-it-feeds),\nbecause an improvement nobody consumes is a report that cost\nmoney.\n\nForesight is wired into the demand modules this way by\nconstruction. Forecasts flow to the settings and decisions that\nconvert them, overrides are scored, and the conversion itself is\nmeasured, so the accuracy-to-value question has an answer on a\npage rather than a shrug. Wiring the decision layer is harder to\ncopy than tuning a model, which is exactly why the accuracy-led\nmarket declines to try. [Start here](/contact).\n",1786984937284]