“Multilocation Newsvendor Problem: Centralization and Inventory Pooling” by Prof. Sean Zhou
Prof. Sean X. Zhou
Department of Decision Sciences and Managerial Economics
The Chinese University of Hong Kong
We study a multilocation newsvendor model with a retailer owning multiple retail stores, each of which is operated by a manager who decides its order quantity for filling random customer demand of a product. The store managers and the retailer are all risk-averse while the managers are more risk-averse than the retailer. We adopt conditional value-at-risk (CVaR) as the performance measure and consider two alternative strategies to improve the performance of the system. First, the retailer centralizes the ordering decisions. Second, the managers still decide the order quantity for their own store whereas their inventories are pooled together. We analyze and compare the optimal order quantities and the resulting CVaR values of the systems and study their comparative statistics. We find that when there is no inventory pooling, each store has a higher inventory level in the centralized system than in the decentralized system. More interestingly, centralization brings positive benefits to the retailer as long as some store manager(s) is strictly more risk-averse than the retailer. When there is inventory pooling, the ordering decisions in a decentralized system depends on how the additional profit from pooling is allocated among the stores. We consider several allocation rules and show that as long as the store managers are sufficiently more risk-averse than the retailer or the demands are very heavy-tailed, inventory pooling brings less benefit than centralization. We further derive a lower bound on the value of centralization and an upper bound on the value of inventory pooling. Our analytical results are illustrated using a data set from an online retailer in China and various comparative statics are further examined via extensive numerical experiments.
This is joint work with Zhenyu Hu (NUS) and Chaolin Yang (SUFE).