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Integrating human judgement into quantitative forecasting methods: A review

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Cited by:

  1. Katsagounos, Ilias & Thomakos, Dimitrios D. & Litsiou, Konstantia & Nikolopoulos, Konstantinos, 2021. "Superforecasting reality check: Evidence from a small pool of experts and expedited identification," European Journal of Operational Research, Elsevier, vol. 289(1), pages 107-117.
  2. Xiaodan Zhu & Anh Ninh & Hui Zhao & Zhenming Liu, 2021. "Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3231-3252, September.
  3. Lee Mason & Amy Berrington de Gonzalez & Montserrat Garcia-Closas & Stephen J Chanock & Blànaid Hicks & Jonas S Almeida, 2023. "Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-12, April.
  4. Yang, Y. & Lin, J. & Liu, G. & Zhou, L., 2021. "The behavioural causes of bullwhip effect in supply chains: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 236(C).
  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. Tamer Boyaci, & Caner Canyakmaz, & Francis de Véricourt,, 2020. "Human and machine: The impact of machine input on decision-making under cognitive limitations," ESMT Research Working Papers ESMT-20-02, ESMT European School of Management and Technology.
  7. Franco, L. Alberto & Hämäläinen, Raimo P. & Rouwette, Etiënne A.J.A. & Leppänen, Ilkka, 2021. "Taking stock of behavioural OR: A review of behavioural studies with an intervention focus," European Journal of Operational Research, Elsevier, vol. 293(2), pages 401-418.
  8. Hyunjin Kim & Edward L. Glaeser & Andrew Hillis & Scott Duke Kominers & Michael Luca, 2024. "Decision authority and the returns to algorithms," Strategic Management Journal, Wiley Blackwell, vol. 45(4), pages 619-648, April.
  9. Maya Balakrishnan & Kris Johnson Ferreira & Jordan Tong, 2026. "Human-Algorithm Collaboration with Private Information: Naïve Advice-Weighting Behavior and Mitigation," Management Science, INFORMS, vol. 72(1), pages 265-284, January.
  10. Blair Flicker, 2026. "Managerial Insight and “Optimal” Algorithms," Management Science, INFORMS, vol. 72(1), pages 128-147, January.
  11. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
  12. Petropoulos, Fotios & Spiliotis, Evangelos, 2025. "Judgmental selection of parameters for simple forecasting models," European Journal of Operational Research, Elsevier, vol. 323(3), pages 780-794.
  13. 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.
  14. Neukirchen, Thomas & Klumpp, Matthias, 2019. "Digital logistics, strategic cognitive readiness and employee training," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg Int, volume 27, pages 117-150, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
  15. Ginanneschi, Marco, 2021. "Long-term strategic thinking, the Themis method and the future of food," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
  16. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
  17. Abolghasemi, Mahdi & Ganbold, Odkhishig & Rotaru, Kristian, 2025. "Humans vs. large language models: Judgmental forecasting in an era of advanced AI," International Journal of Forecasting, Elsevier, vol. 41(2), pages 631-648.
  18. Felipe Caro & Anna Sáez de Tejada Cuenca, 2023. "Believing in Analytics: Managers’ Adherence to Price Recommendations from a DSS," Manufacturing & Service Operations Management, INFORMS, vol. 25(2), pages 524-542, March.
  19. Perera, H. Niles & Fahimnia, Behnam, 2024. "Multi-period ordering decisions in the presence of retail promotions," European Journal of Operational Research, Elsevier, vol. 319(3), pages 763-776.
  20. Fotios Petropoulos & Enno Siemsen, 2023. "Forecast Selection and Representativeness," Management Science, INFORMS, vol. 69(5), pages 2672-2690, May.
  21. Edmundas Kazimieras Zavadskas & Romualdas Bausys & Ingrida Lescauskiene & Ana Usovaite, 2020. "MULTIMOORA under Interval-Valued Neutrosophic Sets as the Basis for the Quantitative Heuristic Evaluation Methodology HEBIN," Mathematics, MDPI, vol. 9(1), pages 1-19, December.
  22. Majid Karimi & Stanko Dimitrov, 2024. "To Subsidize Or Not to Subsidize: A Comparison of Market Scoring Rules and Continuous Double Auctions for Price Discovery," Information Systems Frontiers, Springer, vol. 26(2), pages 801-823, April.
  23. Christiane B. Haubitz & Cedric A. Lehmann & Andreas Fügener & Ulrich W. Thonemann, 2021. "The Risk of Algorithm Transparency: How Algorithm Complexity Drives the Effects on Use of Advice," ECONtribute Discussion Papers Series 078, University of Bonn and University of Cologne, Germany.
  24. Cedric A. Lehmann & Christiane B. Haubitz & Andreas Fügener & Ulrich W. Thonemann, 2022. "The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3419-3434, September.
  25. Andres Eberhard Friedl Ackermann & Virginia Fani & Romeo Bandinelli & Miguel Afonso Sellitto, 2025. "Short-Term Prediction in an Emergency Healthcare Unit: Comparison Between ARIMA, ANN, and Logistic Map Models," Forecasting, MDPI, vol. 7(3), pages 1-16, September.
  26. Claudia Schütze & Catherine Cleophas & Monideepa Tarafdar, 2020. "Revenue management systems as symbiotic analytics systems: insights from a field study," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 1007-1031, November.
  27. George Athanasopoulos & Rob J Hyndman & Mitchell O'Hara-Wild, 2021. "The Road to Recovery from COVID-19 for Australian Tourism," Monash Econometrics and Business Statistics Working Papers 1/21, Monash University, Department of Econometrics and Business Statistics.
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