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Assortment planning at a retailer entails both selecting the set of products to be carried and setting inventory levels for each product. The authors study an assortment planning model in which consumers might accept substitutes when their favorite product is unavailable. They develop an algorithmic process to help retailers compute the best assortment for each store. First, they present a procedure for estimating the parameters of substitution behavior and demand for products in each store. Second, they propose an iterative optimization heuristic for solving the assortment planning problem. Third, they establish new structural properties (based on the heuristic solution) that relate the products included in the assortment and their inventory levels to product characteristics such as gross margin, case-pack sizes, and demand variability.
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