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Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework

Author

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  • Amine Belhadi
  • Sachin Kamble
  • Samuel Fosso Wamba
  • Maciel M. Queiroz

Abstract

Artificial Intelligence (AI) offers a promising solution for building and promoting more resilient supply chains. However, the literature is highly dispersed regarding the application of AI in supply-chain management. The literature to date lacks a decision-making framework for identifying and applying powerful AI techniques to build supply-chain resilience (SCRes), curbing advances in research and practice on this interesting interface. In this paper, we propose an integrated Multi-criteria decision-making (MCDM) technique powered by AI-based algorithms such as Fuzzy systems, Wavelet Neural Networks (WNN) and Evaluation based on Distance from Average Solution (EDAS) to identify patterns in AI techniques for developing different SCRes strategies. The analysis was informed by data collected from 479 manufacturing companies to determine the most significant AI applications used for SCRes. The findings show that fuzzy logic programming, machine learning big data, and agent-based systems are the most promising techniques used to promote SCRes strategies. The study findings support decision-makers by providing an integrated decision-making framework to guide practitioners in AI deployment for building SCRes.

Suggested Citation

  • Amine Belhadi & Sachin Kamble & Samuel Fosso Wamba & Maciel M. Queiroz, 2022. "Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework," International Journal of Production Research, Taylor & Francis Journals, vol. 60(14), pages 4487-4507, July.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:14:p:4487-4507
    DOI: 10.1080/00207543.2021.1950935
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    Citations

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    Cited by:

    1. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    2. Taiwen Feng & Zhihui Si & Wenbo Jiang & Jianyu Tan, 2024. "Supply chain transformational leadership and resilience: the mediating role of ambidextrous business model," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    3. Simon Kaggwa & Tobechukwu Francisa Eleogu & Franciscamary Okonkwo & Oluwatoyin Ajoke Farayola & Prisca Ugomma Uwaoma & Abiodun Akinoso, 2024. "AI in Decision Making: Transforming Business Strategies," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(12), pages 423-444, January.
    4. Manikandan Rajagopal & Ramkumar Sivasakthivel, 2024. "An Empirical Framework Using Weighted Feed Forward Neural Network for Supply Chain Resilience (SCR) Strategy Selection," SN Operations Research Forum, Springer, vol. 5(2), pages 1-19, June.
    5. Talaei-Khoei, Amir & Yang, Alan T. & Masialeti, Masialeti, 2024. "How does incorporating ChatGPT within a firm reinforce agility-mediated performance? The moderating role of innovation infusion and firms’ ethical identity," Technovation, Elsevier, vol. 132(C).
    6. Hind Aboussikine & Sonia Bendimerad & Thierry Sauvage & Mohamed Haouari, 2023. "Comment l’Intelligence Artificielle dompte la traçabilité des processus Supply Chain ? Application à NOZ France," Post-Print hal-04536092, HAL.
    7. Mahmoud Z. Mistarihi & Ghazi M. Magableh, 2023. "Prioritization of Supply Chain Capabilities Using the FAHP Technique," Sustainability, MDPI, vol. 15(7), pages 1-19, April.

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