Pricing to Accelerate Demand Learning in Dynamic Assortment Planning for Perishable Products

Retailers, from fashion stores to grocery stores, have to decide what range of products to offer, i.e., their product assortment. New business trends, such as mass customization and shorter product life cycles, make predicting demand more difficult, which in turn complicates assortment planning. We propose and study a stochastic dynamic programming model for simultaneously making assortment and pricing decisions that incorporates demand learning using Bayesian updates. We analytically show that it is profitable for the retailer to give price discounts early on the sales season to accelerate demand learning. Our computational results demonstrate the benefits of such a policy and provide managerial insights that may help improve a retailer’s profitability. keywords: assortment planning, demand learning, Bayesian updating, stochastic dynamic programming, retailing

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