Quick response policy with Bayesian information updates

Tsan Ming Choi, Duan Li, Houmin Yan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

59 Citations (Scopus)

Abstract

In this paper we investigate the quick response (QR) policy with different Bayesian models. Under QR policy, a retailer can collect market information from the sales of a pre-seasonal product whose demand is closely related to a seasonal product's demand. This information is then used to update the distribution for the seasonal product's demand by a Bayesian approach. We study two information update models: one with the revision of an unknown mean, and the other with the revision of both an unknown mean and an unknown variance. The impacts of the information updates under both models are compared and discussed. We also identify the features of the pre-seasonal product which can bring more significant profit improvement. We conclude that an effective QR policy depends on a precise information update model as well as a selection of an appropriate pre-seasonal product as the observation target.
Original languageEnglish
Pages (from-to)788-808
Number of pages21
JournalEuropean Journal of Operational Research
Volume170
Issue number3
DOIs
Publication statusPublished - 1 May 2006

Keywords

  • Bayesian information updates
  • Inventory
  • Quick response policy
  • Supply chain management

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Modelling and Simulation
  • Transportation

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