Intertemporal Demand Allocation for Inventory Control in Online Marketplaces

TL;DR

This paper explores how online marketplaces can strategically allocate intertemporal demand to influence seller inventory decisions and safety stocks without direct control, using informational mechanisms.

Contribution

It introduces a model demonstrating how demand allocation affects seller safety-stock needs and provides guidelines for minimizing forecast uncertainty while balancing platform fulfillment and seller inventory.

Findings

Uniform splitting minimizes forecast uncertainty among sellers.

Higher forecast uncertainty can be achieved with simple low-memory routing rules.

Routing rules preventing sellers from inferring demand increase forecast uncertainty.

Abstract

Online marketplaces increasingly do more than simply match buyers and sellers: they route orders across competing sellers and, in many categories, offer ancillary fulfillment services that make seller inventory a source of platform revenue. We investigate how a platform can use intertemporal demand allocation to influence sellers' inventory choices without directly controlling stock. We develop a model in which the platform observes aggregate demand, allocates orders across sellers over time, and sellers choose between two fulfillment options, fulfill-by-merchant (FBM) and fulfill-by-platform (FBP), while replenishing inventory under state-dependent base-stock policies. The key mechanism we study is informational: by changing the predictability of each seller's sales stream, the platform changes sellers' safety-stock needs even when average demand shares remain unchanged. We focus on nondiscriminatory allocation policies that give sellers the same demand share and forecast risk. Within this class, uniform splitting minimizes forecast uncertainty, whereas any higher level of uncertainty can be implemented using simple low-memory allocation rules. Moreover, increasing uncertainty above the uniform benchmark requires routing rules that prevent sellers from inferring aggregate demand from their own sales histories. These results reduce the platform's problem to choosing a level of forecast uncertainty that trades off adoption of platform fulfillment against the inventory held by adopters. Our analysis identifies demand allocation as a powerful operational and informational design lever in digital marketplaces.