Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation - IMT Atlantique
Article Dans Une Revue International Journal of Production Research Année : 2024

Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation

Résumé

This research examines the transformative potential of artificial intelligence (AI) in general and Gen-erative AI (GAI) in particular in supply chain and operations management (SCOM). Through thelens of the resource-based view and based on key AI capabilities such as learning, perception,prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 dis-tinct SCOM decision-making areas. These areas include but are not limited to demand forecasting,inventory management, supply chain design, and risk management. With its outcomes, this studyprovides a comprehensive understanding of AI and GAI’s functionality and applications in the SCOMcontext, offering a practical framework for both practitioners and researchers. The proposed frame-work systematically identifies where and how AI and GAI can be applied in SCOM, focussing ondecision-making enhancement, process optimisation, investment prioritisation, and skills develop-ment. Managers can use it as a guidance to evaluate their operational processes and identify areaswhere AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. Theresearch underscores that AI and GAI, with their multifaceted capabilities and applications, opena revolutionary potential and substantial implications for future SCOM practices, innovations, andresearch.
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hal-04591724 , version 1 (29-05-2024)

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Ilya Jackson, Dmitry Ivanov, Alexandre Dolgui, Jafar Namdar. Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. International Journal of Production Research, 2024, pp.1 - 26. ⟨10.1080/00207543.2024.2309309⟩. ⟨hal-04591724⟩
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