Attributing Inventory Performance via Shapley-Based Counterfactual Decomposition

Inventory systems are typically evaluated using aggregate performance metrics such as out-of-stock and average inventory. In supply chain management, it is important to understand the underlying reasons for a period's performance— specifically, how previous inventory management decisions, such as order placement, lead to the result and what their contributions are. Traditional methods are often restrictive and cannot be applied to broader cases. This paper proposes a Shapley-based decomposition framework that attributes the realized performance gap between the observed inventory policy and optimized reference policy to individual decisions. A numerical experiment on a simulated finite-horizon periodic-review inventory system with stochastic demand and lead time is conducted to illustrate the basic idea of the method. Compared to traditional methods, the proposed approach directly explains a realized benchmark-relative performance difference and is applicable to integer-constrained, non-differentiable, and simulation-based inventory systems. It enables transparent inventory management performance evaluation and effective root-cause analysis.