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OM Forum—Supply Chain Management in the AI Era: A Vision Statement from the Operations Management Community

  • Maxime C. Cohen*
  • , Tinglong Dai*
  • , Georgia Perakis*
  • , Narendra Agrawal
  • , Gad Allon
  • , Robert N. Boute
  • , Gérard P. Cachon
  • , Zhe Chen
  • , Morris Cohen
  • , Rares Cristian
  • , Vinayak Deshpande
  • , Francis de Véricourt
  • , Jan C. Fransoo
  • , Joren Gijsbrechts
  • , Pavithra Harsha
  • , Ming Hu
  • , Pınar Keskinocak
  • , Caleb Kwon
  • , Hau Lee
  • , Sheng Liu
  • Konstantina Mellou, Ishai Menache, Jason Miller, Serguei Netessine, Tava Lennon Olsen, Jeevan Pathuri, Robert Peels, Yongzhi Qi, Ananth Raman, Anne Robinson, Zuo-Jun Max Shen, Masha Shunko, David Simchi-Levi, Hannah Smalley, Jing-Sheng Song, Jayashankar M. Swaminathan, Christopher Tang, Sridhar Tayur, Maxi Udenio, Jan A. Van Mieghem, Yuqian Xu, Dennis J. Zhang
*Corresponding author for this work

Research output: Indexed journal article Articlepeer-review

1 Citation (Scopus)

Abstract

Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impact depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management: intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decision making, risk management, and human–machine collaboration and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI’s promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human–AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.
Original languageEnglish
Pages (from-to)687-705
Number of pages19
JournalManufacturing & Service Operations Management
Volume28
Issue number3
DOIs
Publication statusPublished - 1 May 2026

Keywords

  • artificial intelligence
  • human–AI collaboration
  • machine learning
  • operations management
  • optimization
  • resilience
  • supply chain management

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