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Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review

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  • Efpraxia D. Zamani

    (The University of Sheffield)

  • Conn Smyth

    (NUI Galway)

  • Samrat Gupta

    (Indian Institute of Management Ahmedabad)

  • Denis Dennehy

    (Swansea University)

Abstract

Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.

Suggested Citation

  • Efpraxia D. Zamani & Conn Smyth & Samrat Gupta & Denis Dennehy, 2023. "Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review," Annals of Operations Research, Springer, vol. 327(2), pages 605-632, August.
    • Handle: RePEc:spr:annopr:v:327:y:2023:i:2:d:10.1007_s10479-022-04983-y
    DOI: 10.1007/s10479-022-04983-y
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    References listed on IDEAS

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