Inventory Pooling Under Heavy-Tailed Demand

• 成果类型: Article
• 署名作者: Bimpikis, Kostas; Markakis, Mihalis G.
• 署名单位: Stanford University; Pompeu Fabra University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2015.2204
• 发表日期: 2016
• 页码: 1800-1813
• 关键词: Inventory Management; inventory pooling; Demand uncertainty; heavy-tailed distributions

摘要

Risk pooling has been studied extensively in the operations management literature as the basic driver behind strategies such as transshipment, manufacturing flexibility, component commonality, and drop shipping. This paper explores the benefit of risk pooling in the context of inventory management using the canonical model first studied in Eppen [Eppen GD (1979) Effects of centralization on expected costs in a multi-location newsboy problem. Management Sci. 25(5): 498-501]. Specifically, we consider a single-period, multilocation newsvendor model, where n different locations face independent and identically distributed demands and linear holding and backorder costs. We show that Eppen's celebrated result, i.e., that the expected cost savings from centralized inventory management scale with the square root of the number of locations, depends critically on the light-tailed nature of the demand uncertainty. In particular, we establish that the benefit from pooling relative to the decentralized case, in terms of both expected cost and safety stock, is equal to n((alpha-1)/alpha) for a class of heavy-tailed demand distributions (stable distributions), whose power-law asymptotic decay rate is determined by the parameter alpha is an element of(1,2). Thus, the benefit from pooling under heavy-tailed demand uncertainty can be significantly lower than root n, which is predicted for normally distributed demands. We discuss the implications of this result on the performance of periodic-review policies in multiperiod inventory management, as well as for the profits associated with drop-shipping fulfillment strategies. Corroborated by an extensive simulation analysis with heavy-tailed distributions that arise frequently in practice, such as power law and log normal, our findings highlight the importance of taking into account the shape of the tail of demand uncertainty when considering a risk pooling initiative.