Demand forecasting uses historical sales data, market trends, seasonality patterns, and external factors to predict future product demand. Accurate forecasting enables optimal inventory levels—enough to meet demand without overstocking.
Modern demand forecasting increasingly uses machine learning models that can identify complex patterns humans might miss. These models consider factors like marketing calendars, competitor actions, weather, economic indicators, and social trends.
Poor forecasting creates expensive problems: underforecasting causes stockouts and lost sales, while overforecasting creates excess inventory requiring markdowns. Tracking competitor out-of-stock and markdown rates can indicate forecasting accuracy in the market.