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On the calculation of safety stocks

Author

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  • Syntetos, A.A.
  • Teunter, R.H.

    (Groningen University)

Abstract

In forecasting and inventory control textbooks and software applications, the variance of the cumulative lead-time forecast error is, almost invariably, taken as the sum of the error variances of the individual forecast intervals. For stationary demand and a constant lead time, this implies multiplying the single period variance (or Mean Squared Error) by the lead-time. This standard approach is shown in this paper to always underestimate the true lead-time demand variability, resulting in too low safety stocks and poor service. For two of the most widely applied forecasting techniques (Single Exponential Smoothing and Simple Moving Average) we present corrected expressions and show that the error in the standard approach is often considerable. The same fundamental problem exists for all forecasting techniques and all demand processes, and so this issue deserves wider recognition and offers ample opportunities for further research.

Suggested Citation

  • Syntetos, A.A. & Teunter, R.H., 2014. "On the calculation of safety stocks," Research Report 14003-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  • Handle: RePEc:gro:rugsom:14003-opera
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    File URL: http://hdl.handle.net/11370/d5e027d2-fd84-46c2-8259-96585495c23e
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    References listed on IDEAS

    as
    1. R D Snyder & A B Koehler & J K Ord, 1999. "Lead time demand for simple exponential smoothing: an adjustment factor for the standard deviation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(10), pages 1079-1082, October.
    2. A A Syntetos & J E Boylan & J D Croston, 2005. "On the categorization of demand patterns," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 495-503, May.
    3. L W G Strijbosch & R M J Heuts & E H M van der Schoot, 2000. "A combined forecast—inventory control procedure for spare parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(10), pages 1184-1192, October.
    4. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.
    5. Everette S. Gardner, 1990. "Evaluating Forecast Performance in an Inventory Control System," Management Science, INFORMS, vol. 36(4), pages 490-499, April.
    6. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737.
    7. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    8. M M Ali & J E Boylan, 2011. "Feasibility principles for Downstream Demand Inference in supply chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 474-482, March.
    9. R Fildes & B Kingsman, 2011. "Incorporating demand uncertainty and forecast error in supply chain planning models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 483-500, March.
    10. Harvey, Andrew & Snyder, Ralph D., 1990. "Structural time series models in inventory control," International Journal of Forecasting, Elsevier, vol. 6(2), pages 187-198, July.
    11. Stephen C. Graves, 1999. "A Single-Item Inventory Model for a Nonstationary Demand Process," Manufacturing & Service Operations Management, INFORMS, vol. 1(1), pages 50-61.
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