Inventory and Pricing Optimization for Emerging Supply Chain Models

Abstract

Increases in online sales and a renewed focus on sustainability are driving many operational changes for modern supply chains. E-commerce enables quick and low-cost price changes that emphasize the importance of revenue management strategies. These trends have also aided the rise of resale and rental options, which benefit from the broader supply and demand bases that e-commerce offers. Additionally, customer expectations for swift deliveries require retailers to hold inventory in many locations that are closer to customers. In turn, these expectations complicate inventory management and increase the risk of overstock. This work investigates inventory and pricing strategies to combat these challenges. More specifically, we address the following, novel problems within the supplychain domain: 1) network size reduction for inventory transshipment using new clustering algorithms to minimize distance and correlation between locations; 2) joint pricing and inventory management for resale firms under uncertain supply; 3) multi-product revenue management with rental and resale options. To solve these problems, we utilize and develop new clustering, stochastic optimization, and nonlinear optimization techniques. This work incorporates data-driven, behavioral, and theoretical approaches to answer these emerging questions in operations research. Our results demonstrate the value of reducing demand correlation between locations when dividing large networks for improving inventory related profits and the robustness of the profit improvements from correlation reduction to changes in inventory transshipment types and strategies. In addition, they show the impacts of customer price- and quality- sensitivity on the optimal used supply acceptance rate and pricing strategy, the value of incorporating uncertain demand along with uncertain supply when optimizing responsive pricing strategies, and the potential positive and negative impacts of customer donation of used goods on resale supply chains. Lastly, our results capture the impact of quoted buyback prices on customers' valuations of new, used, and rental items and the relative value customers place on new, used, and rental products in physical and digital formats.