Role of AI in Sustainable Supply Chain Management

This is the first study systematically and bibliometrically reviewed the research efforts on the role of AI in sustainable supply chain management involving 1000+ documents published from 2006 to 2026. In this regard, the field increased its growth rate from 36.5% annually until roughly 2019 when it increased to almost 500 publications per year (R² = 0.95); average citations peaked in the years of 2018 and 2022 and thereafter steadily declined most likely due to the citation lag rather than a drop in the quality of the manuscripts. For the second objective, the leading sources were Sustainability (Switzerland), the Journal of Cleaner Production and the International Journal of Production Research, which had a higher concentration than expected of sources according to consideration of Bradford's law, and China, India and the USA were the countries with the highest output of Journals. One special characteristic of the field is collaboration; each document is on average written by 4.24 collaborators, 39.82% of the documents have foreign collaborators and only 115 documents were authored by one person out of 5,374 authors. The third objective focused conceptualizing the link between AI, sustainability and supply chain management; and conceptualizing machine learning, blockchain, decision-support systems and the circular economy. In line with its fourth objective, the themes evolved from 2006–2022 to supply chain management as the main theme in 2023–2024, moving onto the integration of AI with the optimisation of the supply chain in 2025–2026, while themes of resilience, blockchain, and the Internet of Things will feature prominently as well. The fifth objective is met in our study. Academically it gives a reproducible map of the field as well as revealing how there has been a move from isolated topics to become an integrated decision-process, and this forms the basis for using OR techniques e.g. optimisation, forecasting and simulation in sustainable supply chain issues. Managerially, these results can guide manufacturers, e.g. logistics providers and retailers, and food and agriculture companies to prioritize tools such as machine learning, predictive analytics and resilience-oriented tools and to determine which journals and research groups are of greater relevance for their needs (Khanashyam et al., 2025). Social consequences include significant support from emerging economies and a high degree of international cooperation—as well as a certain awareness of sustainable development, emission reduction, and the circular economy – but which will be weighed against the possibilities of disadvantage for individual firms, if AI is used unwisely. The study was limited by time and data availability, as it relied on a single database (Scopus), English-language journal publications and one search string, and as the 2026 data were incomplete at the time of analysis. It further outlines literature structure and growth rather than attempts to rate the value of any individual studies. The future research studies which can fill these gaps involve enlarging the database represented by this article to other databases like Web of Science etc., taking the expanded dataset into consideration and using OR techniques to specific sustainable supply chain decision-making.  

Abbas, A. F., Fernando, Y., Chin, T. A., & Fahim, N. A. (2026). AI-driven green supply chain management: a scientometric review of global research trends. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-026-03406-6 Adamowski, J. F. (2008). Peak daily water demand forecast modeling using artificial neural networks. Journal of Water Resources Planning and Management, 134(2), 119–128. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(119) Amarasuriya, S., & Abston, K. A. (2026). Procurement 4.0: A Three-Pillar Framework for Ethical AI Adoption in Small Businesses. Journal of Small Business Strategy, 36(2), 27–37. https://doi.org/10.53703/001c.157792 Baruah, A., & Moshref-Javadi, M. (2026). Generative Artificial Intelligence in Supply Chain: Review, Trends, and Future Directions. Logistics, 10(8). https://doi.org/10.3390/logistics10080190 Ben Youssef, A., Zeqiri, A., & Mbang, S. (2026). Digital twin use-case prioritisation for sustainability in smart manufacturing: A multi-criteria decision analytics approach. Socio-Economic Planning Sciences, 106. https://doi.org/10.1016/j.seps.2026.102553 Bhavikatta, N. B., & Goluguri, N. V. R. (2026). Multi dataset LSTM analysis for accurate and sustainable supply chain forecasting. Discover Sustainability, 7(1). https://doi.org/10.1007/s43621-026-02828-3 Bhuiyan, M. N. I., Saha, B. K., & Satter, M. A. (2026). Smart biosensing packaging and artificial intelligence-integrated edible sensors for detecting shelf life: a paradigm shift in real-time food safety monitoring. Journal of Food Science and Technology. https://doi.org/10.1007/s13197-026-06803-8 Chowdhury, S., Ren, S., & Richey, R. G. (2025). Leveraging artificial intelligence to facilitate green servitization: Resource orchestration and Re-institutionalization perspectives. International Journal of Production Economics, 281. https://doi.org/10.1016/j.ijpe.2025.109519 Di Vaio, A., Boccia, F., Landriani, L., & Palladino, R. (2020). Artificial intelligence in the agri-food system: Rethinking sustainable business models in the COVID-19 scenario. Sustainability (Switzerland), 12(12). https://doi.org/10.3390/SU12124851 El-Sattar, H. K. A., Elshambakey, M., Saleh, A., & Antar, S. (2026). Integrating physical modeling with artificial intelligence for predicting fish survival zones in polluted rivers to maintain a sustainable aquaculture industry. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-57547-6 Esho, E. O., Akinyelu, A. A., & Dinis, M. A. P. (2026). Sustainable generative AI and quantum computing: review assessment on the environmental impact of generative AI and quantum technologies. Frontiers in Sustainability, 7. https://doi.org/10.3389/frsus.2026.1726832