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AI-Powered Supply Chain and Operations Management (SCOM): Capabilities and Challenges

In: Handbook of Ripple Effects in the Supply Chain

Author

Listed:
  • Yuhong Li

    (Old Dominion University)

  • Kedong Chen

    (Rensselaer Polytechnic Institute)

  • Jiawei Zhang

    (PRA Group)

Abstract

Artificial intelligence (AI) has emerged as one of the most transformative technologies that revolutionize various sectors including supply chain and operations management (SCOM). AI’s ability to process large datasets, generate predictions, and optimize decision-making processes offers significant operational advantages. However, the integration of AI also introduces challenges, particularly from the aspects of data management and integration, operational and technical challenges in decision-making, and relational and ethical considerations. The enhanced interconnectedness, when combined with AI-powered automation, also significantly amplifies the ripple effect throughout the supply chain when disruptions occur. This chapter explores the capabilities and challenges of AI in SCOM, focusing on data management, decision-making under uncertainties, and buyer-supplier relationship management in the context of resilience and ripple effect. Through a systematic review of the literature, we aim to provide a comprehensive understanding of AI’s role in SCOM and present frameworks for managing the complexities and risks associated with AI-powered operations.

Suggested Citation

  • Yuhong Li & Kedong Chen & Jiawei Zhang, 2025. "AI-Powered Supply Chain and Operations Management (SCOM): Capabilities and Challenges," International Series in Operations Research & Management Science, in: Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov (ed.), Handbook of Ripple Effects in the Supply Chain, edition 0, pages 139-165, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-85508-5_7
    DOI: 10.1007/978-3-031-85508-5_7
    as

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