Application Of AI To The Multi-Echelon Inventory Optimization Problems

Abstract

We investigate the benefits of blackbox optimization (BO) and deep reinforcement learning (DRL) for inventory replenishment problems in multi-echelon supply chains. We present benchmark results against commonly used single echelon heuristics and highlight the strengths and weakness of these AI methods in supply chains and the best ways to leverage them in complex network optimization settings.