Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review
This research presents a comprehensive bibliometric review of the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing production efficiency and fostering sustainable development. With the increasing focus on sustainability, AI and ML technologies have emerged as pivotal tools...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Sustainability |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsus.2024.1508647/full |
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| author | Aristidis Bitzenis Nikos Koutsoupias Marios Nosios |
| author_facet | Aristidis Bitzenis Nikos Koutsoupias Marios Nosios |
| author_sort | Aristidis Bitzenis |
| collection | DOAJ |
| description | This research presents a comprehensive bibliometric review of the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing production efficiency and fostering sustainable development. With the increasing focus on sustainability, AI and ML technologies have emerged as pivotal tools for optimizing industrial processes, improving resource management and minimizing environmental impacts. The study analyzes key ML algorithms in various production settings. This study conducts systematic bibliometric analysis using the Scopus database and Bibliometrix R package, examining global trends, key collaborations, and thematic focuses on AI and ML applications in production efficiency and sustainable development. Novel contributions include uncovering underexplored ethical dimensions of AI adoption and emphasizing the pivotal role of SMEs and developing economies in advancing sustainable practices. Key research trends identified include the integration of AI with sustainable energy management, circular economy practices, and precision agriculture. Furthermore, the analysis reveals geographical contributions, with countries like China, the United States, and the United Kingdom leading in research output and impact. Despite the promising advancements, the review identifies gaps in ethical considerations, especially in data privacy and labor market implications, and suggests avenues for future research, including the implementation of AI and ML in developing economies and Small and Medium Enterprises (SMEs). |
| format | Article |
| id | doaj-art-fc33275f9eba4b868316bdf5e102ef33 |
| institution | Kabale University |
| issn | 2673-4524 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sustainability |
| spelling | doaj-art-fc33275f9eba4b868316bdf5e102ef332025-01-13T06:11:02ZengFrontiers Media S.A.Frontiers in Sustainability2673-45242025-01-01510.3389/frsus.2024.15086471508647Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric reviewAristidis BitzenisNikos KoutsoupiasMarios NosiosThis research presents a comprehensive bibliometric review of the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing production efficiency and fostering sustainable development. With the increasing focus on sustainability, AI and ML technologies have emerged as pivotal tools for optimizing industrial processes, improving resource management and minimizing environmental impacts. The study analyzes key ML algorithms in various production settings. This study conducts systematic bibliometric analysis using the Scopus database and Bibliometrix R package, examining global trends, key collaborations, and thematic focuses on AI and ML applications in production efficiency and sustainable development. Novel contributions include uncovering underexplored ethical dimensions of AI adoption and emphasizing the pivotal role of SMEs and developing economies in advancing sustainable practices. Key research trends identified include the integration of AI with sustainable energy management, circular economy practices, and precision agriculture. Furthermore, the analysis reveals geographical contributions, with countries like China, the United States, and the United Kingdom leading in research output and impact. Despite the promising advancements, the review identifies gaps in ethical considerations, especially in data privacy and labor market implications, and suggests avenues for future research, including the implementation of AI and ML in developing economies and Small and Medium Enterprises (SMEs).https://www.frontiersin.org/articles/10.3389/frsus.2024.1508647/fullartificial intelligencemachine learningbibliometricssustainable developmentproduction efficiency |
| spellingShingle | Aristidis Bitzenis Nikos Koutsoupias Marios Nosios Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review Frontiers in Sustainability artificial intelligence machine learning bibliometrics sustainable development production efficiency |
| title | Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review |
| title_full | Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review |
| title_fullStr | Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review |
| title_full_unstemmed | Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review |
| title_short | Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review |
| title_sort | artificial intelligence and machine learning in production efficiency enhancement and sustainable development a comprehensive bibliometric review |
| topic | artificial intelligence machine learning bibliometrics sustainable development production efficiency |
| url | https://www.frontiersin.org/articles/10.3389/frsus.2024.1508647/full |
| work_keys_str_mv | AT aristidisbitzenis artificialintelligenceandmachinelearninginproductionefficiencyenhancementandsustainabledevelopmentacomprehensivebibliometricreview AT nikoskoutsoupias artificialintelligenceandmachinelearninginproductionefficiencyenhancementandsustainabledevelopmentacomprehensivebibliometricreview AT mariosnosios artificialintelligenceandmachinelearninginproductionefficiencyenhancementandsustainabledevelopmentacomprehensivebibliometricreview |