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The human factor in supply chain forecasting: A systematic review

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  • Perera, H. Niles
  • Hurley, Jason
  • Fahimnia, Behnam
  • Reisi, Mohsen

Abstract

Demand forecasts are the lifeblood of supply chains. Academic literature and common industry practices indicate that demand forecasts are often subject to human interventions. Judgmental forecasting or judgmental forecast adjustments can cause both positive and negative repercussions to the rest of the supply chain. This paper provides the first systematic literature review of judgmental forecasting and adjustments focusing on key features that impact various decisions in supply chains. A carefully assembled and shortlisted literature pool is analyzed for systematic mapping of the published works using bibliometric tools. The primary sub streams of research within the broader scope of the field are synthesized from a rigorous keyword cluster analysis and a thorough discussion is presented. Our review concludes by encapsulating the key learnings from four decades of academic research in judgmental forecasting and suggests future research avenues to expand our understanding of the role of humans in demand forecasting and supply chain decision-making.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:274:y:2019:i:2:p:574-600
    DOI: 10.1016/j.ejor.2018.10.028
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    2. Hareer Fatima Ahmed & Amin Hosseinian-Far & Rasoul Khandan & Dilshad Sarwar & Khushboo E-Fatima, 2022. "Knowledge Sharing in the Supply Chain Networks: A Perspective of Supply Chain Complexity Drivers," Logistics, MDPI, vol. 6(3), pages 1-20, September.
    3. Ulpan Tokkozhina & Ana Lucia Martins & Joao C. Ferreira, 2023. "Multi-tier supply chain behavior with blockchain technology: evidence from a frozen fish supply chain," Operations Management Research, Springer, vol. 16(3), pages 1562-1576, September.
    4. 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.
    5. 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).
    6. 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.
    7. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
    8. Rajaguru, Rajesh & Matanda, Margaret Jekanyika & Verma, Prikshat, 2023. "Information system integration, forecast information quality and market responsiveness: Role of socio-technical congruence," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    9. Hewage, Harsha Chamara & Perera, H. Niles & De Baets, Shari, 2022. "Forecast adjustments during post-promotional periods," European Journal of Operational Research, Elsevier, vol. 300(2), pages 461-472.
    10. De Baets, Shari & Harvey, Nigel, 2020. "Using judgment to select and adjust forecasts from statistical models," European Journal of Operational Research, Elsevier, vol. 284(3), pages 882-895.
    11. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    12. 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.
    13. Angelopoulos, Spyros & Bendoly, Elliot & Fransoo, Jan C. & Hoberg, Kai & Ou, Carol & Tenhiälä, Antti, 2023. "Digital transformation in operations management: Fundamental change through agency reversal," Other publications TiSEM 373742f5-0b87-4276-9ed6-8, Tilburg University, School of Economics and Management.
    14. 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).
    15. Khosrowabadi, Naghmeh & Hoberg, Kai & Imdahl, Christina, 2022. "Evaluating human behaviour in response to AI recommendations for judgemental forecasting," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1151-1167.
    16. 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.
    17. 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.
    18. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    19. Nimni Pannila & Madushan Madhava Jayalath & Amila Thibbotuwawa & Izabela Nielsen & T.G.G. Uthpala, 2022. "Challenges in Applying Circular Economy Concepts to Food Supply Chains," Sustainability, MDPI, vol. 14(24), pages 1-24, December.

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