Demand forecasting is the process of predicting future demand for a product using historical sales data, trend analysis, and contextual factors — producing an estimate that informs replenishment alongside usable stock, incoming supply, lead time and supplier constraints.
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. Confirmed customer orders and operational requirements can also drive buying; a statistical forecast is one input to the decision.
What methods can a small team evaluate? Start with a naïve forecast (the last observed value), a moving average and simple exponential smoothing. For seasonal demand, include a seasonal naïve baseline. Intermittent-demand methods such as SBA are candidates when demand has many zeros. Select using relevant held-out data and purchasing outcomes, not a universal method rule.
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? Compare equivalent periods across repeated cycles, accounting for promotions, availability and changes in the business. A seasonal index can be useful when that pattern is stable; a few rising weeks do not establish seasonality.