Quick response policy with Bayesian information updates

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)788-808
Journal / PublicationEuropean Journal of Operational Research
Issue number3
Online published11 Sep 2004
Publication statusPublished - 1 May 2006
Externally publishedYes


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.

Research Area(s)

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