Clustering retail stores for inventory transshipment

Rapid growth in the world of e-commerce is forcing traditional retailers to rethink their operational efficiency and revenue/cost streams. Retailers are facing increasing pressure to better utilize in-store inventory and reduce end-of-season markdowns. Transshipments between stores can re-balance inventory levels while preventing shortages and backorders. However, the joint inventory and transshipment problem is difficult to optimize in an extensive network, and many retailers lack the capital and managerial bandwidth required to create a system capable of transshipments between any two stores in a network of hundreds. We consider two multi-location newsvendor problems with reactive and proactive transshipments, respectively. We propose clustering stores into transshipment groups while considering both demand correlation and physical distance between locations. Since minimizing demand correlation does not follow the triangle inequality, we introduce two modified clustering algorithms that outperform existing algorithms for distance- and correlation-based clustering. For a given set of clusters, we mathematically model these two problems considering total system profit. In addition, we quantify the performance of distance-based and correlation-based clustering under several inventory ordering and transshipment policies to show the robustness of our results. To verify our methods, we test the proposed methodology using grocery sales data from IRi over three years in Houston and Dallas, TX and liquor licensee purchase information over three years from the Iowa state government. We present correlation, transshipment, and profitability analysis. We show that when demand correlation is present and stable over time, clustering based on demand correlation can maintain high transshipments and profits while decreasing solution complexity.