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Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting

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  • Murphy Choy
  • Michelle L. F. Cheong

Abstract

Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand forecasting techniques may fail given the unusual characteristics of the function. Proper identification of the underlying demand function and using the most appropriate forecasting technique becomes critical. In this paper, we will attempt to explore the key characteristics of the different types of demand function and relate them to known statistical distributions. By fitting statistical distributions to actual past demand data, we are then able to identify the correct demand functions, so that the the most appropriate forecasting technique can be applied to obtain improved forecasting results. We applied the methodology to a real case study to show the reduction in forecasting errors obtained.

Suggested Citation

  • Murphy Choy & Michelle L. F. Cheong, 2011. "Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting," Papers 1110.0062, arXiv.org.
  • Handle: RePEc:arx:papers:1110.0062
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    File URL: http://arxiv.org/pdf/1110.0062
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    References listed on IDEAS

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    1. Bartezzaghi, Emilio & Verganti, Roberto, 1995. "Managing demand uncertainty through order overplanning," International Journal of Production Economics, Elsevier, vol. 40(2-3), pages 107-120, August.
    2. Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
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