Operating distribution center networks at scale: Efficient formulations and policies

Abstract

How should retailers and sellers distribute inventory across a large network of (potentially hundreds of) distribution centers? Motivated by the emerging challenges of inventory allocation facing volatile demand, we develop distributionally robust network inventory management policies with cross-fulfillment using a scenario-based approach that is adaptable to feature information and demand correlation. We analytically demonstrate the value of scenario modeling for a two-location newsvendor problem. The key technical development is a computationally efficient linear programming formulation for large networks, which we further incorporate into a dynamic inventory policy combined with look-ahead approximation. We validate the superior performance of the proposed policies using real-world transaction data from a leading logistics service provider and a major retailer in China. The developed policy outperforms existing data-driven approaches and can save up to 19% of operating costs over the current policy employed by the company.