Managing Newsvendors via Demand Shifting

The increasingly prevalent “fulfillment by platform” practice in the digital economy presents platforms with the challenge of managing the inventory decisions of self-interested third-party sellers. In this work, we explore a novel idea of demand shifting as an incentive instrument for this problem. By committing to a rule that reallocates demand among substitutable sellers, the platform changes the demand distribution that each seller expects to face and thereby, influences the seller’s decentralized inventory decision. We formulate this interaction as a Stackelberg game between a platform and a continuum of third-party sellers. The platform designs a demand-shifting policy to maximize its profit, whereas sellers choose inventory in response to their postshift demand in a newsvendor manner. We establish in closed form the platform’s optimal policy. It has a simple defluctuating structure, which collapses part of the seller’s demand distribution into a point mass around the induced inventory level, thereby reducing local demand uncertainty and making the desired inventory decision optimal for the seller. Operationally, this policy resembles risk pooling, but unlike standard ex post substitution, it also shapes sellers’ ex ante inventory incentives. When shifting is inexpensive, the platform can achieve the centralized benchmark, and sellers strictly benefit from demand shifting. When shifting is costly, demand shifting remains valuable to the platform, but customer service level may decrease in certain primitive regimes.

Funding: This work was supported by the Guangdong Key Lab of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001], the Shenzhen Municipal Stable Support Plan Program [Project C10120230116], and the National Natural Science Foundation of China [Projects 72192805, 72394361, 72401245, and 72501250].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2024.1396.