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Implementation of a demand planning system using advance order information

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  • Haberleitner, Helmut
  • Meyr, Herbert
  • Taudes, Alfred

Abstract

In times of demand shocks, when quantitative forecasting based on historical time series becomes obsolete, the only information about future demand is "advance demand information", i.e. interpreting early customer bookings as an indicator of not yet known demand. This paper deals with a forecasting method which selects the optimal forecasting model type and the level of integration of advance demand information, depending on the patterns of the particular time series. This constitutes the applicability of the procedure within an industrial application where a large number of time series is automatically forecasted in a flexible and data-driven way. The architecture of such a planning system is explained and using real-world data from a make-to-order industry it is shown that the system is flexible enough to cover different demand patterns and is well-suited to forecast demand shocks.

Suggested Citation

  • Haberleitner, Helmut & Meyr, Herbert & Taudes, Alfred, 2010. "Implementation of a demand planning system using advance order information," International Journal of Production Economics, Elsevier, vol. 128(2), pages 518-526, December.
    • Handle: RePEc:eee:proeco:v:128:y:2010:i:2:p:518-526
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    References listed on IDEAS

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    5. Kekre, Sunder & Morton, Thomas E. & Smunt, Timothy L., 1990. "Forecasting using partially known demands," International Journal of Forecasting, Elsevier, vol. 6(1), pages 115-125.
    6. Meyr, H., 2003. "Die Bedeutung von Entkopplungspunkten für die operative Planung von Supply Chains," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 36064, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.

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