Demand forecasting is the process of predicting future demand for a product using historical sales data, trend analysis, and contextual factors — producing a quantitative estimate that drives every downstream procurement decision from order quantity to safety stock sizing.
Quick answers
What is demand forecasting? Demand forecasting uses historical sales patterns to predict how much of each item you will sell in a future period. The forecast feeds directly into reorder point calculations, safety stock sizing, and purchase order timing. Without a forecast, every order is a guess.
What methods work for SMBs? Two cover most cases. Simple moving average — average the last N periods of demand. Exponential smoothing — weight recent periods more heavily than older ones. Both are computable in a spreadsheet. The right method depends on the item's demand pattern, which is where SBC classification matters: smooth demand responds well to exponential smoothing; intermittent or lumpy demand requires bias-corrected methods like the Syntetos-Boylan Approximation.
How accurate does my forecast need to be? Exact prediction is impossible. What matters is measuring the error. Evaluate on held-out periods against a simple baseline. MAPE is undefined when actual demand is zero and can be unstable near zero, so it is often unsuitable for intermittent demand. Choose a compatible error measure and assess shortages and stock alongside it. See forecast accuracy evaluation.
What about seasonality? If your product has predictable demand cycles (holiday spikes, summer slowdowns), apply a seasonal index: multiply the baseline forecast by a ratio derived from the same period in prior years. Without seasonal adjustment, your forecast will systematically under-order before peaks and over-order before troughs.