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Mapping the Role and Impact of Artificial Intelligence and Machine Learning Applications in Supply Chain Digital Transformation: A Bibliometric Analysis

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  • Jeetu Rana

    (Indian Institute of Management Lucknow)

  • Yash Daultani

    (Indian Institute of Management Lucknow)

Abstract

Today, manufacturing enterprises are adopting emerging Industry 4.0 technologies to create industrial intelligence-driven smart factories. This trend, in turn, is stimulating the advent of intelligent supply chains that can sync and support the rapid evolution of advanced industrial practices via supply chain digital transformation. Specifically, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as vital breakthrough technologies that can help firms enhance profit margins, reduce supply chain costs, deliver excellent customer service, and make their supply chains intelligent. This paper identifies and analyzes 338 most influential research papers to scientifically examine the linkages among the AI-ML techniques and their applications in the SCM domain through bibliometric and network analysis, descriptive data analysis, and visual representation, thus furnishing a perspicacious knowledge base. The main contribution of this paper is to identify the unexplored potential and the contexts in which AI and ML can be used in managing and transforming supply chains digitally, including the aspects of intelligent and interpretative evolutions. Additionally, a fundamental contribution of this work is a comprehensive mind map that makes it possible to visualize, understand, and simulate the wide spectrum of findings from the bibliometric analyses. Finally, the study presents research gaps, implications, and future scope as a point of reference for researchers and practitioners.

Suggested Citation

  • Jeetu Rana & Yash Daultani, 2023. "Mapping the Role and Impact of Artificial Intelligence and Machine Learning Applications in Supply Chain Digital Transformation: A Bibliometric Analysis," Operations Management Research, Springer, vol. 16(4), pages 1641-1666, December.
    • Handle: RePEc:spr:opmare:v:16:y:2023:i:4:d:10.1007_s12063-022-00335-y
    DOI: 10.1007/s12063-022-00335-y
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    References listed on IDEAS

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    1. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
    2. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    3. Alexandre Moeuf & Robert Pellerin & Samir Lamouri & Simon Tamayo-Giraldo & Rodolphe Barbaray, 2018. "The industrial management of SMEs in the era of Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1118-1136, February.
    4. Mohamed M. Dhiaf & Osama F. Atayah & Nohade Nasrallah & Guilherme F. Frederico, 2021. "Thirteen years of Operations Management Research (OMR) journal: a bibliometric analysis and future research directions," Operations Management Research, Springer, vol. 14(3), pages 235-255, December.
    5. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    6. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    7. Kumar, V. & Ramachandran, Divya & Kumar, Binay, 2021. "Influence of new-age technologies on marketing: A research agenda," Journal of Business Research, Elsevier, vol. 125(C), pages 864-877.
    8. María Pinto & Antonio Pulgarín & M. Isabel Escalona, 2014. "Viewing information literacy concepts: a comparison of two branches of knowledge," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 2311-2329, March.
    9. Chang-Ping Hu & Ji-Ming Hu & Yan Gao & Yao-Kun Zhang, 2011. "A journal co-citation analysis of library and information science in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 657-670, March.
    10. Kohtamäki, Marko & Parida, Vinit & Oghazi, Pejvak & Gebauer, Heiko & Baines, Tim, 2019. "Digital servitization business models in ecosystems: A theory of the firm," Journal of Business Research, Elsevier, vol. 104(C), pages 380-392.
    11. Henry Small, 1999. "Visualizing science by citation mapping," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(9), pages 799-813.
    12. Xinxin Wang & Zeshui Xu & Marinko Škare, 2020. "A bibliometric analysis of Economic Research-Ekonomska Istraživanja (2007–2019)," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 865-886, January.
    13. Waltman, Ludo & van Eck, Nees Jan & Noyons, Ed C.M., 2010. "A unified approach to mapping and clustering of bibliometric networks," Journal of Informetrics, Elsevier, vol. 4(4), pages 629-635.
    14. Caviggioli, Federico & Ughetto, Elisa, 2019. "A bibliometric analysis of the research dealing with the impact of additive manufacturing on industry, business and society," International Journal of Production Economics, Elsevier, vol. 208(C), pages 254-268.
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    2. Ajay Kumar Pandey & Yash Daultani & Saurabh Pratap & Andrew W. H. Ip & Fuli Zhou, 2025. "Analyzing Industry 4.0 Adoption Enablers for Supply Chain Flexibility: Impacts on Resilience and Sustainability," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 26(1), pages 1-24, March.

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