Reordering 400 SKUs every Monday. You're either stocking out or sitting on dead stock.

Stockouts cost you the order, and sometimes the customer. Overstock ties up cash and burns shelf life. Both happen weekly because nobody can per-SKU forecast across hundreds of items by hand.

Reorder when the shelf is empty. Or "last month's run rate."

Reactive, not proactive. Bulk orders to hit supplier MOQs that don't actually match demand. Lead times treated as a guess. Slow SKUs reordered as often as fast ones.

Per-SKU demand forecast × lead time + safety stock. Ranked by risk.

Forecast next 30 days per SKU. Multiply by supplier lead time. Add safety stock from demand variance. Rank reorders by stockout risk × dollar impact. Surfaces what to order this week.

Explainable rules, not black-box ML. You can read why a forecast is high or low. That means operators trust it, edit it when their gut disagrees, and the model learns from the edits.

Confidence shown. Reasoning attached. Format you already use.

Output lands in the systems your team already opens. No new dashboard to log into.

8% monthly stockout → 1.5%. $42K working capital freed.

Same engine forecasts anything time-varying.

Demand, churn, and inventory are three shapes. The underlying engine takes any historical series with a few signals attached and gives you a forecast with confidence and explanations.