Dubey, Rameshwar, Gunasekaran, Angappa, Papadopoulos, Thanos (2024) Benchmarking operations and supply chain management practices using generative AI: Towards a theoretical framework. Transportation Research Part E: Logistics and Transportation Review, 189 . Article Number 103689. ISSN 1366-5545. E-ISSN 1878-5794. (doi:10.1016/j.tre.2024.103689) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:106696)
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| Official URL: https://doi.org/10.1016/j.tre.2024.103689 |
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Abstract
Generative Artificial Intelligence (Gen AI) is an up-and-coming technological innovation that has the potential to revolutionise businesses and create significant value. Despite garnering excitement from some quarters, there are still people who are sceptical about its benefits and even fearful of its impact, particularly in the supply chain context, where it is not yet fully understood. To help academics and practitioners better understand the practical implications of Gen AI in benchmarking supply chain management practices, we propose a theoretical toolbox. This toolbox draws from ten popular organisational theories and provides a comprehensive framework for evaluating the usefulness of Gen AI. By expanding theoretical boundaries, the toolbox provides a deeper understanding of the practical applications of Gen AI for researchers and practitioners in supply chain management.
| Item Type: | Article |
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| DOI/Identification number: | 10.1016/j.tre.2024.103689 |
| Uncontrolled keywords: | generative artificial intelligence (Gen AI); artificial intelligence supply chain management; benchmarking; organisational theories |
| Subjects: | H Social Sciences |
| Institutional Unit: | Schools > Kent Business School |
| Former Institutional Unit: |
Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
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| Funders: | University of Kent (https://ror.org/00xkeyj56) |
| Depositing User: | Thanos Papadopoulos |
| Date Deposited: | 26 Jul 2024 09:38 UTC |
| Last Modified: | 22 Jul 2025 09:20 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/106696 (The current URI for this page, for reference purposes) |
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https://orcid.org/0000-0001-6821-1136
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