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Judgmental forecast adjustments over different time horizons

Author

Listed:
  • Maud van den Broeke

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Shari de Baets

    (Vlerick Business School, Ghent)

  • Ann Vereecke

    (Vlerick Business School, Ghent)

  • Philippe Baecke

    (Vlerick Business School, Ghent)

  • Karlien Vanderheyden

    (Vlerick Business School, Ghent)

Abstract

Accurate demand forecasting is the cornerstone of a firm's operations. The statistical system forecasts are often judgmentally adjusted by forecasters who believe their knowledge can improve the final forecasts. While empirical research on judgmental forecast adjustments has been increasing, an important aspect is under-studied: the impact of these adjustments over different time horizons. Collecting data from 8 business cases, retrieving over 307,200 forecast adjustments, this work assesses how the characteristics (e.g., size and direction) and accuracy of consecutive adjustments change over different time horizons. We find that closer to the sales point, the number of adjustments increases and adjustments become larger and more positive; and that adjustments, both close and distant from the sales point, can deteriorate the final forecast accuracy. We discuss how these insights impact operational activities, such as production planning.

Suggested Citation

  • Maud van den Broeke & Shari de Baets & Ann Vereecke & Philippe Baecke & Karlien Vanderheyden, 2019. "Judgmental forecast adjustments over different time horizons," Post-Print hal-03001747, HAL.
    • Handle: RePEc:hal:journl:hal-03001747
    DOI: 10.1016/j.omega.2018.09.008
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    References listed on IDEAS

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    Cited by:

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    2. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    3. Stefano Frizzo Stefenon & Laio Oriel Seman & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2023. "Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices," Energies, MDPI, vol. 16(3), pages 1-18, January.
    4. 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.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. 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.

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