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

Alexander Hogenboom, Wolfgang Ketter, Jan Van Dalen, Uzay Kaymak, John Collins, Alok Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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 (US)
Title of host publicationProceedings of the 11th International Conference on Electronic Commerce, ICEC 2009
Pages176-185
Number of pages10
DOIs
StatePublished - Nov 30 2009
Event11th International Conference on Electronic Commerce, ICEC 2009 - Taipei, Taiwan, Province of China
Duration: Aug 12 2009Aug 15 2009

Publication series

NameACM International Conference Proceeding Series

Other

Other11th International Conference on Electronic Commerce, ICEC 2009
CountryTaiwan, Province of China
CityTaipei
Period8/12/098/15/09

Keywords

  • Dynamic pricing
  • Economic regimes
  • Machine learning
  • Supply chain management
  • TAC SCM

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