Demand forecasting is the bridge between what sold and what to stock. This topic covers the main forecasting methods, how to match a method to a demand pattern, and the honest limits of any forecast, including what to do about the error that always remains.
Every stocking decision is a bet on future demand. Forecasting makes that bet explicit and, done well, smaller. It does not remove uncertainty; it sizes it, so you can decide how much buffer the error is worth.
This topic covers the main families of method, how to choose between them by demand pattern, how to measure forecast accuracy honestly, and how the forecast hands off to safety stock and reorder policy.
Demand forecasting is the process of estimating future demand for each item, usually from sales history adjusted for trend, seasonality, promotions, and known events. The forecast drives purchasing, safety stock, and capacity decisions.
Match the method to the demand pattern. Steady demand suits moving averages or exponential smoothing. Seasonal demand needs a method that models seasonality explicitly. Intermittent, lumpy demand needs methods built for it, such as Croston's, because standard smoothing overstocks it.
Because demand is partly random. The useful question is not whether the forecast is wrong but how wrong, and in which direction. That is why forecasting pairs with safety stock: the forecast sets the expected level, and safety stock absorbs the error around it.
AvanSaber's inventory practice operates across the topics covered on this pillar: case-by-case implementation projects, operational audits, and ongoing advisory for mid-market and enterprise inventory teams. The practitioners who write here are the practitioners who deliver the engagements.