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Cited by 289

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management

Fosso Wamba, Samuel, Guthrie, Cameron, Queiroz, Maciel M., Minner, Stefan·2024·International Journal of Production Research

Abstract

ChatGPT and generative artificial intelligence (Gen-AI) are transforming firms and supply chains. However, the empirical literature reporting the benefits, challenges, and outlook of these nascent technologies in operations and supply chain management (OSCM) is limited. This study surveys current projects and perceptions of these technologies in US (n = 119) and UK (n = 181) supply chains. We found that projects range from proof-of-concept to full implementation, with a main focus on operational gains, such as improved customer satisfaction, cost minimisation, and process efficiencies. The main challenges concern data, technological and organisational issues. Expected benefits are dominated by cost savings and enhanced customer experience, but also include increased automation and sustainability. Industries were found to cluster around six groups according to perceived benefits and implementation challenges. Our findings contribute to the emerging literature on Gen-AI use in OSCM, and to management practice by mapping the benefits, challenges, outlook, and maturity level of Gen-AI projects in supply chains.

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Crossref · 10.1080/00207543.2023.2294116

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Metadata

Authors
Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S.
Venue
International Journal of Production Research
Published
2023-12-20
Volume
62
Issue
16
Pages
5676-5696
ISSN
  • 0020-7543 (ISSN-L)
  • 1366-588X
OpenAlex
W4390055472
Cited by
289
References
11
FWCI
53.91
Type
journal article
Language
en
Open access
closed

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.