Assortment and Pricing with Demand Learning
نویسندگان
چکیده
Retailers, from fashion stores to grocery stores, have to decide what range of products to offer (assortment planning) and what prices to charge (price optimization). New business trends, such as mass customization and shorter product life cycles, make predicting demand more difficult, which in turn complicates assortment planning and price optimization. We propose and study a stochastic dynamic programming model for simultaneously making assortment and pricing decisions that incorporates demand learning using Bayesian updates. Analytical as well as computational results obtained using the model demonstrate the value of demand learning and provide managerial insights that may help improve a retailer’s profitability. keywords: assortment planning, price optimization, demand learning, Bayesian updating, stochastic dynamic programming
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