NDLI: Mixed bundle retailing under a stochastic market

Content Provider	Springer Nature Link
Author	Chew, Ek Peng Lee, Loo Hay Wang, Qiang
Copyright Year	2014
Abstract	Bundling is a pervasive marketing strategy in real business. In this paper, we study the strategy of mixed bundling under a stochastic market for two products for a retailer who has monopolistic power in the market, and the bundle consists of one unit of each individual product. The retailer needs to make joint pricing and inventory decisions with the aim of maximizing expected profit. Firstly, the relationship between the prices and market shares of the three bundle variations in the mixed bundling strategy is modeled after the reservation price model, which is commonly adopted in the bundling literature. Based on the market shares, a two-stage stochastic model is proposed to determine inventory decisions, including the ordering decision before the selling season starts and the allocation decisions after demands are realized. Global concavity in the order quantities is demonstrated. For experimental purposes, an algorithm incorporating both pricing and inventory decisions is presented. We measure the importance of incorporating inventory matters into bundling decisions under the stochastic market by calculating expected loss of profit, which can exceed 5 % under some parameter settings. In the numerical experiments, we identify two attributes of mixed bundling performance, which are bundling pricing effect and inventory pooling effect.
Starting Page	606
Ending Page	629
Page Count	24
File Format	PDF
ISSN	19366582
Journal	Flexible Services and Manufacturing Journal
Volume Number	27
Issue Number	4
e-ISSN	19366590
Language	English
Publisher	Springer US
Publisher Date	2014-12-06
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Inventory Pricing Mixed bundling Stochastic programming Manufacturing, Machines, Tools Operations Management Operation Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Management Science and Operations Research

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