The use of artificial intelligence to advance sustainable supply chain: retrospective and future avenues explored through bibliometric analysis

Ibtissam Zejjari () and Issam Benhayoun ()
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Ibtissam Zejjari: USMBA - Université Sidi Mohamed Ben Abdellah
Issam Benhayoun: UMI - جامعة مولاي إسماعيل = Université Moulay Ismaïl

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Abstract: Abstract Keeping up with the hastily growing economy implies undergoing unremitting transformation permanently. In the field of supply chain, such progress can only be guaranteed via the exploration of new horizons and innovative solutions in response to the constraints of the global market. Emerging technologies, particularly artificial intelligence, offer promising avenues for enhancing supply chain processes, with sustainability ascending as a critical consideration. Despite the recent surfacing of AI-driven applications, scant attention has been devoted to exploring their full potential within supply chain operations, particularly in conjunction with SDGs. Recognizing the untapped opportunities presented by the implementation of AI for a sustainable supply chain this study undertakes a bibliometric analysis of 236 research papers sourced from the Web of science database. The analysis utilizes R language BiblioShiny to examine the extracted papers, and dissect patterns, trends, and relationships among key concepts and themes as well as prominent topics, impactful authors, and leading journals and countries in this domain. The findings reveal substantial growth in research related to SCM, AI, and sustainability as the UK leads this field of study with 132 articles followed by India, China and the USA. Eventually, the National University of Singapore came first in terms of paper affiliations, followed by De La Salle University, and London Metropolitan University. These results only prove that sustainability is becoming more critical in the equation of AI-driven supply chains especially with the current socio-political and economic circumstances, constituting a solid base for further academic research and more innovations in the managerial and business-related policies in this field.

Keywords: Sustainable supply chain Artificial intelligence Sustainability Bibliometric analysis R language; Sustainable supply chain; Artificial intelligence; Sustainability; Bibliometric analysis; R language (search for similar items in EconPapers)
Date: 2024-07-31
New Economics Papers: this item is included in nep-sea
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Published in Discover Sustainability, 2024, 5 (1), pp.174. ⟨10.1007/s43621-024-00364-6⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04671595

DOI: 10.1007/s43621-024-00364-6

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