Historical demand data refers to records of past customer orders or consumption, typically tracked by product, location, and time. It is a foundational element in demand forecasting and inventory optimization, serving as the baseline for understanding demand patterns, seasonality, and trends. Analyzing historical demand allows supply chain planners to build statistical and machine learning models that predict future demand. Accurate historical data enhances forecast accuracy, supports demand segmentation, and aids in determining optimal inventory levels, reorder points, and safety stock requirements. Inconsistencies or gaps in this data can lead to poor forecasting and suboptimal inventory decisions. Beyond forecasting, historical demand data supports root-cause analysis for service level failures, helps identify demand anomalies, and enables SKU rationalization. It also assists in identifying product life cycles and planning promotional strategies. The quality, granularity, and accessibility of historical demand data directly impact a company’s ability to align inventory with market needs efficiently.
