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Exponential Smoothing and Short-Term Sales Forecasting

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  • P. J. Harrison

    (Imperial Chemical Industries Limited)

Abstract

Non-seasonal forecasting methods are examined by considering demand generating processes which are reasonable and general descriptions of customer demand and for which the popular predictors are shown to be optimal. Tests of the adequacy of the generating processes are described. The way in which the forecasting errors vary with the forecasting period is examined, and it is shown that this is dependent not only on the length of the period but also on the values of the forecasting parameters. The sensitivity of the predictors to departures from the optimal parameters is investigated, and the long debated comparison of Holt's linear growth predictor (1957) and Brown's linear growth predictor (1959) is examined. It is shown for the assumed generating model and for forecasting parameters lying within the usual limits, that even if there is an infinite amount of data available to establish the optimum forecasting parameters, the standard error of the one step ahead predictor exceeds that of the Holt predictor by no more than 1.6 percent. The generalized polynomial generating process is shown to have as its optimal least squares predictor the corresponding Box-Jenkins polynomial predictor (1962).

Suggested Citation

  • P. J. Harrison, 1967. "Exponential Smoothing and Short-Term Sales Forecasting," Management Science, INFORMS, vol. 13(11), pages 821-842, July.
    • Handle: RePEc:inm:ormnsc:v:13:y:1967:i:11:p:821-842
    DOI: 10.1287/mnsc.13.11.821
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    Cited by:

    1. Hoang-Sa Dang & Ying-Fang Huang & Chia-Nan Wang & Thuy-Mai-Trinh Nguyen, 2016. "An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry," Sustainability, MDPI, vol. 8(10), pages 1-14, October.
    2. Merigó, José M. & Palacios-Marqués, Daniel & Ribeiro-Navarrete, Belén, 2015. "Aggregation systems for sales forecasting," Journal of Business Research, Elsevier, vol. 68(11), pages 2299-2304.
    3. 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.
    4. Peter Armitage & Cho Ng & Peter C. Young, 1989. "A systems approach to recursive economic forecasting and seasonal adjustment," Discussion Paper / Institute for Empirical Macroeconomics 8, Federal Reserve Bank of Minneapolis.
    5. 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.
    6. Udenio, Maximiliano & Vatamidou, Eleni & Fransoo, Jan C., 2023. "Exponential smoothing forecasts: Taming the Bullwhip Effect when demand is seasonal," Other publications TiSEM 8fca6329-83b9-4a49-a2aa-e, Tilburg University, School of Economics and Management.
    7. Snyder, Ralph D. & Koehler, Anne B. & Ord, J. Keith, 2002. "Forecasting for inventory control with exponential smoothing," International Journal of Forecasting, Elsevier, vol. 18(1), pages 5-18.
    8. Wong, W.K. & Guo, Z.X., 2010. "A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm," International Journal of Production Economics, Elsevier, vol. 128(2), pages 614-624, December.
    9. Zomchak, Larysa & Umrysh, Hryhorii, 2017. "Моделювання Й Прогнозування Виробництва М’Яса Та Яєць В Україні За Допомогою Сезонної Arima-Моделі," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 3(3), September.
    10. Gaalman, Gerard & Disney, Stephen M., 2006. "State space investigation of the bullwhip problem with ARMA(1,1) demand processes," International Journal of Production Economics, Elsevier, vol. 104(2), pages 327-339, December.
    11. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    12. Saligari, Grant R. & Snyder, Ralph D., 1997. "Trends, lead times and forecasting," International Journal of Forecasting, Elsevier, vol. 13(4), pages 477-488, December.
    13. Manikas, Andrew S. & Patel, Pankaj C., 2016. "Managing sales surprise: The role of operational slack and volume flexibility," International Journal of Production Economics, Elsevier, vol. 179(C), pages 101-116.
    14. Francisco J. Díaz-Borrego & María del Mar Miras-Rodríguez & Bernabé Escobar-Pérez, 2019. "Looking for Accurate Forecasting of Copper TC/RC Benchmark Levels," Complexity, Hindawi, vol. 2019, pages 1-16, April.
    15. 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.
    16. Mirko Kremer & Enno Siemsen & Douglas J. Thomas, 2016. "The Sum and Its Parts: Judgmental Hierarchical Forecasting," Management Science, INFORMS, vol. 62(9), pages 2745-2764, September.
    17. Godfrey, Gregory A. & Powell, Warren B., 2000. "Adaptive estimation of daily demands with complex calendar effects for freight transportation," Transportation Research Part B: Methodological, Elsevier, vol. 34(6), pages 451-469, August.
    18. Mirko Kremer & Brent Moritz & Enno Siemsen, 2011. "Demand Forecasting Behavior: System Neglect and Change Detection," Management Science, INFORMS, vol. 57(10), pages 1827-1843, October.

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