Exploiting Random Lead Times for Significant Inventory Cost Savings

Taking Advantage of the Lead Time Randomness in Supply Chains Randomness in lead times is a major—and increasingly important—issue of inventory management, as a variety of risk factors motivate companies to diversify their supply sources and rely on distributed networks of suppliers. In “Exploiting Random Lead Times for Significant Inventory Cost Savings,” A. Stolyar and Q. Wang show that, surprisingly, instead of being a damaging factor to supply chain performance, randomness may be harnessed for potentially very substantial reductions of inventory costs. Specifically, the theoretical analysis and simulation results in the paper demonstrate that, under certain conditions, appropriately designed novel policies can significantly outperform the conventional base stock policies.

Understanding the Performance of Capped Base-Stock Policies in Lost-Sales Inventory Models

Single-sourcing lost-sales inventory systems with lead times are notoriously difficult to optimize. In this paper, we propose a new family of capped base-stock policies and provide a new perspective on constructing a practical hybrid policy combining two well-known heuristics: base-stock and constant-order policies. Each capped base-stock policy is associated with two parameters: a base-stock level and an order cap. We prove that for any fixed order cap, the capped base-stock policy converges exponentially fast in the base-stock level to a constant-order policy, providing a theoretical foundation for a phenomenon by which a capped dual-index policy converges numerically to a tailored base-surge policy recently observed in other work in a different but related dual-sourcing inventory model. As a consequence, there exists a sequence of capped base-stock policies that are asymptotically optimal as the lead time grows. We also numerically demonstrate its superior performance in general (including small lead times) by comparing it with otherwell-known heuristics.