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

Masoud Talebian (Masoud.Talebian***at***newcastle.edu.au)
Natashia Boland (Natashia.Boland***at***newcastle.edu.au)
Martin Savelsbergh (Marin.Savelsbergh***at***newcastle.edu.au)

Abstract: Retailers, from fashion stores to grocery stores, have to decide what range of products to off er, 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, price optimization, demand learning, Bayesian updating, stochastic dynamic programming

Category 1: Applications -- OR and Management Sciences

Category 2: Applications -- OR and Management Sciences (Marketing )

Citation:

Download: [PDF]

Entry Submitted: 01/16/2012
Entry Accepted: 01/16/2012
Entry Last Modified: 04/01/2014

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