Dual Sourcing Made Easy: Distributionally Robust Optimization of Inventory Systems Under Independent Demand

• 成果类型: Article
• 署名作者: Jiang, Songchen; Li, Zhaolin; Bi, Sheng; Teo, Chung-Piaw; Huang, Min
• 署名单位: Northeastern University - China; National University of Singapore; University of Sydney; Shanghai University of Finance & Economics
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2024.1481
• 发表日期: 2026
• 关键词: dual-sourcing inventory management; tailored base-surge (TBS) policy; distributionally robust optimization; Stochastic independence; closed-form solution; policies; optimality; QUEUE

摘要

We generalize Scarf's classical min-max newsvendor model from a singleperiod setting to a multiperiod inventory system with independent demand across periods. This extension leverages mean-variance analysis to capture the dynamic effects of lead times, yielding closed-form expressions for the optimal base-stock level. As a concrete application, we study a single-product, dual-sourcing system with constant lead times and backlogging. We show that the optimal tailored base-surge policy admits a tractable closed-form approximation, with the base stock explicitly calibrated to account for leadtime effects. This provides a simple, distribution-free rule for trading off inventory cost against service level in a dual-sourcing system. Empirical validation using data from a multinational food manufacturer demonstrates the model's practical advantages. Applying our method to historical demand and sourcing data improves service levels and reduces stockouts compared with traditional approaches, while maintaining cost-effectiveness. The model's capacity to adapt base-stock levels to different lead times and demand conditions proved especially valuable in mitigating the impact of supply chain volatility. These findings confirm the theoretical performance of our approach and highlight its potential as a scalable, cost-effective tool for firms facing lead-time demand uncertainty.