Predictive replenishment anticipates when customers will need to replenish inventory by analyzing sales forecasts and inventory levels. Typically, merchandise is sold to stores on a calendar basis or as a reaction to a purchase order, not based on actual consumption. Stores risk running out of inventory when actual consumption patterns vary from the set schedule, or they must hold excess inventory which ties up working capital. Predictive replenishment considers multiple factors such as seasonality, inventory, ordering patterns, lead time forecasts, special orders, product lifecycle phase, and service level goals, to improve replenishment forecasts for the next period. Most predictive replenishment systems are collaborative, linked with customer's demand forecasting or point of sale systems to automatically gather input into the replenishment forecasting models. Predictive replenishment can also be applied to industrial situations, such as component or raw material inventory in a factory, or spare parts inventory at a utility.
Increased forecast accuracy, Better management and response to demand volatility, Coherency of siloed business functions, Increased customer satisfaction, Increased revenue and profits, Reduction in out of stock products, Decreased transportation spend, Collaborative decision making and information flows, Proactive planning approach, Demand visibility