Wolf Ketter's Publications

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Identifying and Predicting Economic Regimes in Supply Chains Using Sales and Procurement Information

Frederik Hogenboom, Wolfgang Ketter, Jan van Dalen, Uzay Kaymak, John Collins, and Alok Gupta. Identifying and Predicting Economic Regimes in Supply Chains Using Sales and Procurement Information. In Eleventh International Conference on Electronic Commerce (ICEC 2009), pp. 19–28, ACM, August 2009.

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Abstract

We investigate the effects of adding procurement information (component offer prices) to a sales-based economic regime model, which is used for strategic, tactical, and operational decision making in dynamic supply chains. The performance of the regime model is evaluated through experiments with the MinneTAC trading agent, which competes in the TAC SCM game. We find that the new regime model has a similar overall predictive performance as the existing model. Regime switches are predicted more accurately, whereas the prediction accuracy of dominant regimes is slightly worse. However, by adding procurement information, we have enriched the model and we have further opportunities for applications in the procurement market, such as procurement reserve pricing.

@INPROCEEDINGS{FHogenboom09ICEC,
  AUTHOR =       {Frederik Hogenboom and Wolfgang Ketter and Jan van Dalen and Uzay Kaymak and John Collins and Alok Gupta},
  TITLE =        {{Identifying and Predicting Economic Regimes in Supply Chains Using Sales and Procurement Information}},
  BOOKTITLE =    {{Eleventh International Conference on Electronic Commerce (ICEC 2009)}},
  YEAR =         {2009},
  pages =        {19--28},
  month =        {August},
  publisher =    {{ACM}},
  abstract =     {{We investigate the effects of adding procurement information
  (component offer prices) to a sales-based economic regime model, which is
  used for strategic, tactical, and operational decision making in dynamic
  supply chains. The performance of the regime model is evaluated through
  experiments with the MinneTAC trading agent, which competes in the TAC SCM
  game. We find that the new regime model has a similar overall predictive
  performance as the existing model. Regime switches are predicted more
  accurately, whereas the prediction accuracy of dominant regimes is slightly
  worse. However, by adding procurement information, we have enriched the model
  and we have further opportunities for applications in the procurement market,
  such as procurement reserve pricing.}},
  keywords =     {{Economic regimes, machine learning, supply chain management, TAC SCM, trading agent}},
  bib2html_pubtype = {Refereed Conference},
  bib2html_rescat = {Trading Agents: Supply-Chain Management},
}

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