Demand forecasting is the practice of estimating how much of a product customers are likely to buy over a future period. Retailers use the forecast to decide how much stock to order and where to place it, so they can meet sales without carrying excess.
Forecasts are built from historical sales data, adjusted for patterns and known events. Simple methods use averages and recent trends, while more advanced ones account for seasonality, price changes, promotions, weather, public holidays and local events. Machine learning models can combine many of these factors and update as new sales arrive. The output is usually a projected quantity per product and location for coming weeks.
Retailers use forecasts for buying decisions, allocation between stores, markdown planning and promotional preparation. Hospitality venues forecast covers and menu mix to plan ingredient orders and rostering. Forecasting at store and SKU level is more useful but harder than at company level, because small numbers are noisy and new products have no history.
Pitfalls include poor or incomplete data, such as sales recorded only when stock was available, which understates true demand. Out-of-stock periods, one-off events and data errors can all mislead a model, so human review still matters. Forecast accuracy should be tracked and compared against actual results, and the process should be adjusted when results drift.
Demand forecasting matters because ordering too little loses sales and ordering too much ties up cash and leads to markdowns. Better forecasts improve availability, reduce waste and help teams plan with more confidence.