Pricing to accelerate demand learning in dynamic assortment planning for perishable products

Masoud Talebian, Natashia Boland and Martin Savelsbergh

European Journal of Operational Research, 2014, vol. 237, issue 2, 555-565

Abstract: Retailers, from fashion stores to grocery stores, have to decide what range of products to offer, i.e., their product assortment. Frequent introduction of new products, a recent business trend, makes 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 which incorporates demand learning using Bayesian updates. We show analytically that it is profitable for the retailer to use price reductions early in the sales season to accelerate demand learning. A computational study demonstrates the benefits of such a policy and provides managerial insights that may help improve a retailer’s profitability.

Keywords: Assortment planning; Demand learning; Bayesian updating; Stochastic dynamic programming; Retailing (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:237:y:2014:i:2:p:555-565

DOI: 10.1016/j.ejor.2014.01.045

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