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Nonlinear identification of judgmental forecasts effects at SKU level


  • Juan R. Trapero
  • Robert Fildes
  • Andrey Davydenko


Prediction of demand is a key component within supply chain management. Improved accuracy in forecasts directly affects all levels of the supply chain, reducing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which provides an initial forecast subsequently modified by the expert's judgment. This paper outlines a new methodology based on state-dependent parameter (SDP) estimation techniques to identify the nonlinear behaviour of such managerial adjustments. This non‐parametric SDP estimate is used as a guideline to propose a nonlinear model that corrects the bias introduced by the managerial adjustments. One‐step‐ahead forecasts of stock‐keeping unit sales sampled monthly from a manufacturing company are utilized to test the proposed methodology. The results indicate that adjustments introduce a nonlinear pattern, undermining accuracy. This understanding can be used to enhance the design of the forecasting support system in order to help forecasters towards more efficient judgmental adjustments. Copyright (C) 2010 John Wiley & Sons, Ltd.

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  • Juan R. Trapero & Robert Fildes & Andrey Davydenko, 2011. "Nonlinear identification of judgmental forecasts effects at SKU level," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 490-508, August.
  • Handle: RePEc:jof:jforec:v:30:y:2011:i:5:p:490-508

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    1. repec:nsr:niesrd:181 is not listed on IDEAS
    2. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-252, July.
    3. James Mitchell & Richard J. Smith & Martin R. Weale, 2002. "Quantification of Qualitative Firm-Level Survey Data," Economic Journal, Royal Economic Society, vol. 112(478), pages 117-135, March.
    4. Busetti, Fabio & Harvey, Andrew, 2003. "Seasonality Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 420-436, July.
    5. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2004. "Dating Business Cycles: A Methodological Contribution with an Application to the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(4), pages 537-565, September.
    6. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
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    Cited by:

    1. Trapero, Juan R. & Pedregal, Diego J. & Fildes, R. & Kourentzes, N., 2013. "Analysis of judgmental adjustments in the presence of promotions," International Journal of Forecasting, Elsevier, vol. 29(2), pages 234-243.
    2. Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
    3. Trapero, Juan R. & Pedregal, Diego J., 2016. "A novel time-varying bullwhip effect metric: An application to promotional sales," International Journal of Production Economics, Elsevier, vol. 182(C), pages 465-471.
    4. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.


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