Calibrated AI Forecasting and Strategic Safety-Stock Placement: Field Evidence from an Indian Multi-Echelon Supply Chain

Abstract

Supply-chain planners need forecasts that translate into service and cost outcomes, not only lower error. This study examines whether explicitly making uncertainty explicit at forecast time improves operational performance in practice. We deploy an end-to-end system for the India operations of a consumer goods firm. The system combines multi-horizon probabilistic demand forecasting, stochastic inventory optimization that allocates safety stock across echelons, and an agent-based simulator that stress tests replenishment rules under monsoon and festival conditions before rollout. Forecasts produce calibrated prediction intervals, which are validated using reliability diagnostics. The optimizer converts these distributions into base stock targets for factories, regional distribution centers, and stores. A cluster-randomized stepped-wedge field trial over six months measures the impact on key performance indicators used in planning. Service level increased by 2.1 percentage points. Stockouts fell by 21.8 percent. Safety stock declined by 13.5 percent. Inventory turnover rose by 9.2 percent. Total landed cost per case decreased by 4.7 percent. The main contribution of this article is to provide field evidence and a deployable design showing how calibrated AI forecasts, combined with strategic safety-stock placement, raise reliability while releasing working capital in a multi-echelon supply chain.