Intertemporal Demand Allocation for Inventory Control in Online Marketplaces
2026-04-08 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors study how online marketplaces can influence how much inventory sellers keep without directly controlling their stock. They show that the platform can change how predictable a seller's sales are by deciding how to spread orders over time and among sellers. This affects how much extra inventory sellers feel they need to store as a safety buffer. The authors find that giving sellers an equal share of orders consistently makes sales most predictable, while other ways to allocate orders can create more uncertainty. This helps platforms balance encouraging sellers to use platform-based fulfillment services against the inventory those sellers hold.
online marketplaceinventory managementorder allocationdemand forecastingfulfillment optionsbase-stock policyforecast uncertaintysafety stockintertemporal demand allocationplatform fulfillment
Authors
Rene Caldentey, Tong Xie
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.