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Biasedness of Forecasts Errors for Intermittent Demand Data

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  • Mariusz Doszyn

Abstract

Purpose: Intermittent demand is defined as infrequent or sporadic. Many forecasting errors are inappropriate for intermittent data. In some periods, there could be no demand, so division by zero must be avoided. Usually, forecasts are computed for many products; therefore, errors should be scale-independent (or relative). Many ex-post forecast errors, such as MASE (Mean Absolute Scaled Error) or MAE (Mean Absolute Error), indicate as best very low forecasts, sometimes even zero forecasts. Therefore, many researchers think that measures taking into account stock and consumer service levels should be used instead of conventional forecasts. It might suggest that typical forecast errors are useless for intermittent data. In this article, the contradictory hypothesis is verified. It is stated that only unbiased forecast errors should be used if the conclusions are to be correct. Design/Methodology/Approach: Definition of unbiased forecast error is proposed and verified for popular forecast errors, such as ME (Mean Error), MSE (Mean Square Error), MAE, or MASE. The theoretical properties of these errors are considered concerning their biasedness. Forecasts are made based on Croston’s and TSB methods, but also average and median were used as forecasting methods to emphasize conclusions. Findings: In the empirical example, forecast errors are computed for intermittent demand times series to verify theoretical conclusions. The general conclusion is that only unbiased forecast errors provide proper indications according to forecast accuracy. This finding is true in general, not only for intermittent demand. Practical Implications: Presented considerations might be useful for enterprises dealing with intermittent demand forecasting such as distribution centers, warehouse centers, and so on. Originality/value: To the author’s knowledge, forecast error bias was not analyzed before in the literature. A new forecast error is proposed, which was named RMSSE (Root Mean Square Scaled Error).

Suggested Citation

  • Mariusz Doszyn, 2020. "Biasedness of Forecasts Errors for Intermittent Demand Data," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 1113-1127.
  • Handle: RePEc:ers:journl:v:xxiii:y:2020:i:special1:p:1113-1127
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    References listed on IDEAS

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    1. Prestwich, S.D. & Tarim, S.A. & Rossi, R. & Hnich, B., 2014. "Forecasting intermittent demand by hyperbolic-exponential smoothing," International Journal of Forecasting, Elsevier, vol. 30(4), pages 928-933.
    2. Wallström, Peter & Segerstedt, Anders, 2010. "Evaluation of forecasting error measurements and techniques for intermittent demand," International Journal of Production Economics, Elsevier, vol. 128(2), pages 625-636, December.
    3. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    4. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    5. 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.
    6. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    7. Mariusz Doszyń, 2019. "Intermittent demand forecasting in the Enterprise: Empirical verification," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(5), pages 459-469, August.
    8. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    9. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    10. 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.
    11. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
    12. R H Teunter & L Duncan, 2009. "Forecasting intermittent demand: a comparative study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 321-329, March.
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    More about this item

    Keywords

    Unbiasedness of forecasts errors; intermittent demand forecasting; RMSSE (Root Mean Square Scaled Error); Croston’s method; TSB method.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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