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// Placeholder — logistics

Forecasts the planners actually rely on

A demand model wired straight into the planning tool — monitored, explainable, and quietly retrained every week.

Problem

Demand planning leaned on gut feel and a forecast nobody believed. The previous model lived in a notebook, ran when someone remembered to run it, and offered no explanation for any given number — so planners quietly overrode it, and the overrides became the real plan.

Approach

We built a demand model designed to earn its place in production: measured against the planners' own baseline, served behind a fast API, and wired directly into the tool they already use.

  • Honest evaluation — we report where the model is weak, not just where it wins.
  • Explanations next to every prediction, so a planner can see why.
  • Automated weekly retraining with monitoring that flags drift before it bites.

Result

Stockouts fell by nearly a third, inference stays comfortably under 150ms at the p95, and — the part that matters most — the planners trust it enough to plan around it.

31%less stockout
<150msp95 inference
For the first time the forecast is something the planners argue *with*, not *about*. It earned its place in the workflow.
Placeholder Name · VP of Supply Chain

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