Product pricing using adaptive real-time probability of acceptance estimations based on economic regimes

A.C. Hogenboom, W. Ketter, Jan Dalen, van, U. Kaymak, J. Collins, Alok Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

In today's complex supply chains, product pricing is a vital, yet non-trivial task. We propose a product pricing approach using adaptive real-time probability of acceptance estimations based on economic regimes. Radial Basis Function Networks are trained to estimate parameters for double-bounded log-logistic distributions assumed to be underlying daily offer prices, using information available real-time. The relation between data and parameters is dynamically modeled using economic regimes (characterizing market conditions) and error terms (accounting for customer feedback). Given the parametric approximations of price distributions, acceptance probabilities are estimated using a closed-form mathematical expression, which is used to determine the price yielding a desired quota. The approach is implemented in the MinneTAC agent and tested against a price-following product pricing method in the TAC SCM game. Performance significantly improves; more customer orders are obtained against higher prices and profits more than double.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Electronic Commerce (ICEC 2009), August 12 - 15, 2009 , Taipei, Taiwan
Place of PublicationTaipei, Taiwan
PublisherAssociation for Computing Machinery, Inc
Pages176-185
ISBN (Print)978-1-60558-586-4
DOIs
Publication statusPublished - 2009
Event8th International Conference on Entertainment Computing - Paris, France
Duration: 3 Sep 20095 Sep 2009

Conference

Conference8th International Conference on Entertainment Computing
Abbreviated titleICEC 2009
CountryFrance
CityParis
Period3/09/095/09/09

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