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

Jeetu Rana () and Yash Daultani ()
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Jeetu Rana: Indian Institute of Management Lucknow
Yash Daultani: Indian Institute of Management Lucknow

Operations Management Research, 2023, vol. 16, issue 4, No 1, 1666 pages

Abstract: 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.

Keywords: Supply chain digital transformation; Industry 4.0; Artificial intelligence; Machine learning; Bibliometric analysis; Intelligent supply chains (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12063-022-00335-y

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