Dynamic reordering and inspection for the multi-item Inventory Record Inaccuracy problem

Inventory Record Inaccuracy (IRI) is a significant challenge in inventory management, caused by discrepancies between actual stock and inventory records due to factors such as spoilage, theft, and obsolescence. Despite extensive academic focus on robust ordering and inventory inspection planning, current literature either considers these in isolation, or focuses on stylized single-item scenarios, not addressing the full dynamics of reordering and inspection in warehouses with many SKUs. We consider such a large warehouse with several heterogeneous items that are subject to shrinkage and limited inspection opportunities. Our approach jointly optimizes reordering and inspection decisions in a dynamic decision framework, incorporating both inspection and travel times. We introduce an algorithmic pipeline for dynamic reordering and inspection that combines a neural network to decide on replenishments and a mixed-integer program to determine an optimal inspection subset. Our results show that ignoring shrinkage can increase costs by up to 95.3% and inspection already becomes viable at a relatively low level of IRI. Our proposed policy saves 8.3% in total costs compared to a static reordering and inspection benchmark, and 21.8% compared to a no-inspection policy.