Online markets are characterized by competition and limited demand information. In E-commerce, firms compete against each other using data-driven dynamic pricing and ordering strategies. To successfully manage both inventory levels as well as offer prices is a highly challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. Currently, retailers lack the possibility to test and evaluate their algorithms appropriately before releasing them into the real world. To study joint dynamic ordering and pricing competition on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics. Further, we deployed and compared different pricing and ordering strategies, from simple rule-based ones to highly sophisticated data-driven strategies which are based on state-of-the-art demand learning techniques and efficient dynamic optimization models.
On Policies for Single-Leg Revenue Management with Limited Demand Information
