Generative AI for enhanced operations and supply chain management

In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting w...

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Veröffentlicht in:Journal of Infrastructure, Policy and Development Jg. 8; H. 10; S. 6637
Hauptverfasser: Khlie, Khaoula, Benmamoun, Zoubida, Jebbor, Ikhlef, Serrou, Driss
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 27.09.2024
ISSN:2572-7923, 2572-7931
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Zusammenfassung:In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.
ISSN:2572-7923
2572-7931
DOI:10.24294/jipd.v8i10.6637