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This paper presents a model for product assortment optimization for a network of retail stores operating in various locations of a company. Driven by the local market information of each retail store, the model determines the right products to include in a store’s assortment and which stores to ship from in the store network. The model first learns the global patterns of the frequent itemsets based on association rule mining to extract patterns of products with corresponding sales benefits. It then encodes the pattern information into the development of a global optimization formulation, which maximizes the revenue of the company in aggregate and identifies the optimal solution for each local store by taking into account the possibility of shipments in the network. We use the transactional level data from an industry leading plastics manufacturer and retailer in the United States to demonstrate the utility of the model.

Product Assortment Decisions for a Network of Retail Stores using Data Mining with Optimization