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Forecasting the forecastability quotient for inventory management

Author

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  • Hill, Arthur V.
  • Zhang, Weiyong
  • Burch, Gerald F.

Abstract

This research develops and empirically tests a model for estimating the economic advantage of using a time phased order point system (TPOP) with time series forecasting rather than a simple reorder point system in an independent demand inventory management context. We define the forecastability quotient (Q) to support this economic analysis. We implement TPOP in our empirical analysis via double exponential smoothing with a damped trend, and implement ROP through a simple moving average.

Suggested Citation

  • Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    • Handle: RePEc:eee:intfor:v:31:y:2015:i:3:p:651-663
    DOI: 10.1016/j.ijforecast.2014.10.006
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    References listed on IDEAS

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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Taylor, James W., 2010. "Reply to the discussion of: Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 658-660, October.
    3. Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552.
    4. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    5. Peter M. Catt, 2009. "Forecastability: Insights from Physics, Graphical Decomposition, and Information Theory," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 13, pages 24-33, Spring.
    6. Taylor, James W., 2010. "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 627-646, October.
    7. Teunter, Ruud & Sani, Babangida, 2009. "On the bias of Croston's forecasting method," European Journal of Operational Research, Elsevier, vol. 194(1), pages 177-183, April.
    8. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    9. McKenzie, Eddie & Gardner Jr., Everette S., 2010. "Damped trend exponential smoothing: A modelling viewpoint," International Journal of Forecasting, Elsevier, vol. 26(4), pages 661-665, October.
    10. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    11. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
    12. Govindan, R.B. & Wilson, J.D. & Eswaran, H. & Lowery, C.L. & Preißl, H., 2007. "Revisiting sample entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 158-164.
    13. Lauge Valentin, 2007. "Use Scaled Errors Instead of Percentage Errors in Forecast Evaluations," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 7, pages 17-22, Summer.
    14. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    15. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    16. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    17. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    18. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    19. Rob J. Hyndman, 2006. "Another Look at Forecast Accuracy Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 43-46, June.
    20. P. J. Harrison, 1967. "Exponential Smoothing and Short-Term Sales Forecasting," Management Science, INFORMS, vol. 13(11), pages 821-842, July.
    21. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    22. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265.
    23. John Boylan, 2009. "Toward a More Precise Definition of Forecastability," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 13, pages 34-40, Spring.
    24. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    25. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
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