What is Demand Forecasting?
Demand forecasting is the use of historical data and statistical or machine-learning models to predict future product demand.
Definition
Demand forecasting is the analytical practice of estimating how much of a product customers will buy in future periods, typically using time-series history, statistical models, and increasingly machine learning. Common techniques include moving averages, exponential smoothing, and regression that account for trend, seasonality, and external drivers. The forecast underpins inventory targets, replenishment, capacity planning, and budgeting. Forecast accuracy is measured with metrics such as MAPE or bias so models can be tuned and trusted.
How Demand Forecasting Works in ERP
ERP and planning modules apply forecasting algorithms to item-level sales history, automatically selecting or fitting models that capture trend and seasonality. The generated forecast flows into safety-stock, reorder-point, and MRP calculations to position inventory and production. Systems track actuals against forecast to compute error metrics, and some apply machine learning to incorporate causal factors like price and promotions.
ERP Vendors with Strong Demand Forecasting
Frequently Asked Questions
How is forecast accuracy measured?
Common metrics include Mean Absolute Percentage Error (MAPE), which expresses average error as a percentage, and forecast bias, which shows whether forecasts consistently run high or low. Tracking these over time reveals whether models are improving and where they break down. The right target accuracy depends on the item's demand volatility.
Which forecasting method should I use?
Stable, high-volume items often do well with exponential smoothing or moving averages, while items with strong seasonality or causal drivers benefit from seasonal models or regression. Intermittent, lumpy demand may need specialized methods such as Croston's. Many ERP systems automatically test several models and pick the best fit per item.