Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunities
Supply chain management (SCM) using artificial intelligence (AI) transforms business practices by encouraging sustainability. Gaining insight into AI's role in improving supply chain effectiveness and lowering environmental impact is essential as demand for sustainable practices rises. This stu...
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| Format: | Article |
| Language: | English |
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Universitat Politècnica de València
2025-07-01
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| Series: | International Journal of Production Management and Engineering |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/IJPME/article/view/22632 |
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| author | S. Porkodi V.V. Phani Kumar Tantravahi Bassam Khalil Hamdan Tabash |
| author_facet | S. Porkodi V.V. Phani Kumar Tantravahi Bassam Khalil Hamdan Tabash |
| author_sort | S. Porkodi |
| collection | DOAJ |
| description | Supply chain management (SCM) using artificial intelligence (AI) transforms business practices by encouraging sustainability. Gaining insight into AI's role in improving supply chain effectiveness and lowering environmental impact is essential as demand for sustainable practices rises. This study aims to investigate how AI contributes to sustainability in SCM and determine the primary challenges and opportunities associated with implementing AI. The study aims to provide an extensive review of AI's potential to assist sustainable and green supply chain practices. This standard and strategic literature review was conducted employing the Scopus database. The five-stage methodology was adopted in the review process, which includes pilot search, locating studies, study selection, synthesis analysis, and reporting. The choice of 82 relevant studies on AI and sustainable SCM was made during the review after the exclusion of irrelevant articles. The review emphasises AI's significant role in enhancing sustainability in SCM by reducing environmental impact, improving resource efficiency, and promoting green practices. However, the study also highlights the identification of challenges such as integration complexity, implementation cost, and technological limitations and future agenda. |
| format | Article |
| id | doaj-art-3b10f1b9ae3d4bd9b776fbc45ba30d45 |
| institution | Matheson Library |
| issn | 2340-4876 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | International Journal of Production Management and Engineering |
| spelling | doaj-art-3b10f1b9ae3d4bd9b776fbc45ba30d452025-07-31T07:28:27ZengUniversitat Politècnica de ValènciaInternational Journal of Production Management and Engineering2340-48762025-07-0113213715810.4995/ijpme.2025.2263221822Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunitiesS. Porkodi0https://orcid.org/0000-0001-9105-5450V.V. Phani Kumar Tantravahi1https://orcid.org/0009-0003-6353-5029Bassam Khalil Hamdan Tabash2https://orcid.org/0009-0007-8204-0944University of Technology and Applied SciencesUniversity of Technology and Applied SciencesUniversity of Technology and Applied SciencesSupply chain management (SCM) using artificial intelligence (AI) transforms business practices by encouraging sustainability. Gaining insight into AI's role in improving supply chain effectiveness and lowering environmental impact is essential as demand for sustainable practices rises. This study aims to investigate how AI contributes to sustainability in SCM and determine the primary challenges and opportunities associated with implementing AI. The study aims to provide an extensive review of AI's potential to assist sustainable and green supply chain practices. This standard and strategic literature review was conducted employing the Scopus database. The five-stage methodology was adopted in the review process, which includes pilot search, locating studies, study selection, synthesis analysis, and reporting. The choice of 82 relevant studies on AI and sustainable SCM was made during the review after the exclusion of irrelevant articles. The review emphasises AI's significant role in enhancing sustainability in SCM by reducing environmental impact, improving resource efficiency, and promoting green practices. However, the study also highlights the identification of challenges such as integration complexity, implementation cost, and technological limitations and future agenda.https://polipapers.upv.es/index.php/IJPME/article/view/22632artificial intelligencesupply chain managementsustainabilitygreen practiceslogisticsenvironmental performance |
| spellingShingle | S. Porkodi V.V. Phani Kumar Tantravahi Bassam Khalil Hamdan Tabash Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunities International Journal of Production Management and Engineering artificial intelligence supply chain management sustainability green practices logistics environmental performance |
| title | Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunities |
| title_full | Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunities |
| title_fullStr | Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunities |
| title_full_unstemmed | Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunities |
| title_short | Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunities |
| title_sort | transforming sustainable and green supply chains with artificial intelligence a strategic review and future research opportunities |
| topic | artificial intelligence supply chain management sustainability green practices logistics environmental performance |
| url | https://polipapers.upv.es/index.php/IJPME/article/view/22632 |
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