Problem
Research questions and friction points this paper is trying to address.
intertemporal demand allocation
inventory control
online marketplaces
forecast uncertainty
fulfillment services
Intertemporal Demand Allocation for Inventory Control in Online Marketplaces
🤖 AI Summary
This study investigates how online platforms can influence sellers’ replenishment decisions through intertemporal order allocation without directly controlling their inventory. The authors develop a model in which the platform observes aggregate demand and dynamically allocates orders to sellers who choose between fulfillment-by-merchant (FBM) and fulfillment-by-platform (FBP), employing state-dependent base-stock policies. Integrating dynamic inventory management, information design, and stochastic allocation mechanisms, the analysis reveals that uniform order allocation minimizes sellers’ demand forecasting uncertainty. Even when average demand shares remain unchanged, modulating the predictability of sales flows can effectively adjust safety stock levels. The platform can implement low-memory routing rules to differentially manage uncertainty across sellers, but must prevent them from inferring aggregate demand from their own realized sales. These findings highlight a critical trade-off between promoting FBP adoption and exerting indirect control over inventory decisions.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
intertemporal demand allocation
forecast uncertainty
inventory control
fulfillment choice
information design
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