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Nonlinear identification of judgmental forecasts effects at SKU level

Author

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  • Juan R. Trapero
  • Robert Fildes
  • Andrey Davydenko

Abstract

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.

Suggested Citation

  • 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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    File URL: http://hdl.handle.net/10.1002/for.1184
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    Citations

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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. Madhukar Nagare & Pankaj Dutta & Naoufel Cheikhrouhou, 2016. "Optimal ordering policy for newsvendor models with bidirectional changes in demand using expert judgment," OPSEARCH, Springer;Operational Research Society of India, vol. 53(3), pages 620-647, September.
    3. Zhu, Tianyuan & Balakrishnan, Jaydeep & da Silveira, Giovani J.C., 2020. "Bullwhip effect in the oil and gas supply chain: A multiple-case study," International Journal of Production Economics, Elsevier, vol. 224(C).
    4. 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.
    5. 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.
    6. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    7. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    8. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.
    9. 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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