Supply chain is where a forecasting error gets a price tag attached almost immediately. Forecast demand a little high and you are holding inventory you did not need. A little low and you are stocked out, disappointing customers and scrambling to expedite. Both happen for the same reason: the forecast stopped matching what people actually bought.
Demand does not hold still
A demand model fit to last quarter assumes last quarter behavior. Then a promotion lands, a competitor changes price, the weather turns, a product goes viral or goes cold. The planning system keeps projecting the old pattern, and the gap between forecast and actual shows up as either a warehouse full of the wrong thing or a shelf that is empty.
Lead times make drift expensive
What makes supply chain drift especially painful is the lag. You commit to orders weeks or months ahead based on the forecast, so by the time the error reveals itself, the money is already spent and the inventory is already in motion. There is no quick undo.
Keep the planning system, fix the forecast
You do not have to replace your demand planning or ERP to fix this. A layer sits on top, watches how the forecast performs against real sales, learns where it is now biased (by SKU, by region, by channel), and corrects the output continuously. Your systems stay in place and your data stays yours. The forecast just tracks demand more closely as it shifts.
Stockouts and overstock are the same error, pointed in opposite directions. Both come from a forecast that quit keeping up with demand.
- Catches bias at the SKU and region level, where it hurts.
- Works on top of your existing planning stack.
- Improves the longer it runs on your demand.
Demand is just a time series that keeps moving. We proved continuous correction in weather first, where you can check a forecast against reality every single day, and the same method keeps a demand forecast honest.
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