Reviewing the applications of artificial intelligence in sustainable supply chains: exploring research propositions for future directions

Naz, Farheen, Agrawal, Rohit, Kumar, Anil, Gunasekaran, Angappa, Majumdar, Abhijit and Luthra, Sunil (2022) Reviewing the applications of artificial intelligence in sustainable supply chains: exploring research propositions for future directions. Business Strategy and the Environment. ISSN 0964-4733 (In Press)

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Official URL: https://doi.org/10.1002/bse.3034

Abstract / Description

The sustainable supply chain (SSC) has received significant attention worldwide in the last few years because it integrates sustainability dimensions into its process. Recent artificial intelligence (AI) based advancements in technology make it possible to overcome many problems associated with supply chain (SC) networks. The current study was performed to explore the role of AI in establishing a SSC. The research contribution in the field of AI and SSC was examined through a systematic literature review. A total of 353 articles were gathered from the SCOPUS database in the selected research field. A big data based approach, namely Structural Topic Modelling (STM), was applied to generate emerging thematic topics from shortlisted articles of AI in SSC. Moreover, using R-package, a bibliometric analysis was conducted to examine the research trends in the field of AI in SSC. The research trends were then analyzed, with generated thematic topics discussed. The proposed research framework can help researchers and practitioners to develop an SSC model using AI-based techniques. Propositions for future research are given and implications are suggested.

Item Type: Article
Uncontrolled Keywords: artificial intelligence; big data analytics; business strategy; carbon emissions; environment; sustainable supply chain; sustainability; sustainable development
Subjects: 600 Technology > 650 Management & auxiliary services
Department: Guildhall School of Business and Law
Depositing User: Anil Kumar
Date Deposited: 21 Feb 2022 09:36
Last Modified: 11 Apr 2022 10:10
URI: http://repository.londonmet.ac.uk/id/eprint/7209

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