July 22, 2026 · 7 min read
Most inventory software is excellent at telling you what happened and terrible at telling you what's about to happen. A client running twelve warehouses across three states came to us with accurate stock counts and still-frequent stockouts — the system knew exactly how much was on the shelf, but nobody could reliably predict how much would be needed next week.
The existing reorder logic worked off a trailing 30-day average, which flattens exactly the signals that matter most: a product that sells steadily Monday to Thursday and spikes on weekends looks, on average, like it sells evenly every day. That flattening was quietly causing the Friday stockouts that were costing the most in lost sales.
We rebuilt the reorder engine around a forecasting model that weighs recent sales velocity, day-of-week seasonality, and promotional lift separately instead of blending them into one number. Each SKU gets its own reorder point recalculated nightly, rather than a static threshold set once and forgotten. Slow-moving stock and fast-moving stock are no longer governed by the same rule of thumb.
A dashboard full of predictions is only useful if it changes what a warehouse manager does that morning. We surfaced forecasts directly inside the purchase-order screen as a single recommended reorder quantity, with the reasoning one click away, so accepting the system's suggestion became the fastest path rather than an extra step.
Within one full sales cycle, stockouts dropped by 35% and the client cut roughly 20 hours a month of manual reorder review across their warehouse team. Just as importantly, overstock on slow-moving lines fell too — the forecast was as good at telling teams what not to reorder as what to.
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