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

Zamani, Efpraxia D.; Smyth, Conn; Gupta, Samrat; Dennehy, Denis

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.

Citation

Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 327(2), 605-632. https://doi.org/10.1007/s10479-022-04983-y

• Journal Article Type: Article
• Acceptance Date: Sep 6, 2022
• Online Publication Date: Sep 30, 2022
• Publication Date: 2023-08
• Deposit Date: Aug 16, 2023
• Journal: Annals of Operations Research
• Print ISSN: 0254-5330
• Electronic ISSN: 1572-9338
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 327
• Issue: 2
• Pages: 605-632
• DOI: https://doi.org/10.1007/s10479-022-04983-y
• Keywords: Management Science and Operations Research; General Decision Sciences
• Public URL: https://durham-repository.worktribe.com/output/1718757