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Service Parts Management pp 31–52Cite as

Distributional Assumptions for Parametric Forecasting of Intermittent Demand

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Abstract

Parametric approaches to stock control rely upon a demand distributional assumption and the employment of an appropriate forecasting procedure for estimating the moments of such a distribution. For the case of fast demand items the Normality assumption is typically sufficient. However, spare parts typically exhibit intermittent or irregular demand patterns that may not be represented by the Normal distribution. The objective of this work is three-fold: first, we conduct an empirical investigation that enables the analysis of the goodness-of-fit of various continous and discrete, compound and non-compound, two-parameter statistical distributions used in the literature in the context of intermittent demand; second, we crictically link the results to theoretical expectations; third, we provide an agenda for further research in this area. We use three empirical datasets for the purposes of our analysis that collectively constitute the individual demand histories of approximately 13,000 SKUs. Our work allows insights to be gained on the suitability of various distributions in a spare parts context.

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Acknowledgements

The research described in this chapter has been partly supported by the Engineering and Physical Sciences Research Council (EPSRC, UK) grants no. EP/D062942/1 and EP/G006075/1. More information on the former project may be obtained at http://www.business.salford.ac.uk/research/ommss/projects/Forecasting/. In addition, we acknowledge the financial support received from the Royal Society, UK: 2007/Round 1 Inter. Incoming Short Visits—North America.

Author information

Authors and Affiliations

  1. University of Salford, Salford, UK

    Aris A. Syntetos & David Lengu

  2. BEM Bordeaux Management School, Bordeaux, France

    M. Zied Babai

  3. DePaul University, Chicago, IL, USA

    Nezih Altay

Authors
  1. Aris A. Syntetos
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  2. M. Zied Babai
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  3. David Lengu
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  4. Nezih Altay
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Corresponding author

Correspondence to Aris A. Syntetos .

Editor information

Editors and Affiliations

  1. , Department of Management, DePaul University, E. Jackson Blvd. 1, Chicago, 60604, Illinois, USA

    Nezih Altay

  2. , Robins School of Business, University of Richmond, Westhampton Way 28, Richmond, 23173, Virginia, USA

    Lewis A. Litteral

Appendix

Appendix

2.1.1 Goodness-of-Fit Results

Fig. A1
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Dataset #2—goodness-of-fit results for Poisson distribution

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Fig. A2
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Dataset #2—goodness-of-fit results for the NBD

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Fig. A3
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Dataset #2—goodness-of-fit results for the stuttering Poisson

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Fig. A4
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Dataset #2—goodness-of-fit results for the normal distribution

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Fig. A5
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Dataset #2—goodness-of-fit results for gamma distribution

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Fig. A6
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Dataset #3—goodness-of-fit results for the Poisson distribution

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Fig. A7
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Dataset #3—goodness-of-fit results for the NBD

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Fig. A8
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Dataset #3—goodness-of-fit results for the stuttering Poisson

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Fig. A9
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Dataset #3—goodness-of-fit results for the normal distribution

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Fig. A10
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Dataset #3—goodness-of-fit results for gamma distribution

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Syntetos, A.A., Babai, M.Z., Lengu, D., Altay, N. (2011). Distributional Assumptions for Parametric Forecasting of Intermittent Demand. In: Altay, N., Litteral, L. (eds) Service Parts Management. Springer, London. https://doi.org/10.1007/978-0-85729-039-7_2

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  • DOI: https://doi.org/10.1007/978-0-85729-039-7_2

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  • Online ISBN: 978-0-85729-039-7

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