Home / Current Issue / Paper 1720153
AI-Driven Predictive Analytics for Sustainable and Resilient Supply Chains: An Integrated Machine Learning Framework
Subject area: Management and Commerce · Area of research: Supply Chain Analytics
DOI: https://doi.org/10.64388/IREV10I1-1720153
Abstract
Supply chains are getting disrupted more often because of pandemics, conflicts, climate change and market volatility. This shows that we need sustainable supply chain management. Artificial Intelligence (AI) and predictive analytics can help improve forecasting, risk management and environmental sustainability. Most studies focus on individual AI applications not on combining multiple machine learning techniques. This study proposes a framework that combines demand forecasting, supplier risk prediction, inventory optimization and sustainability assessment to improve supply chain resilience and performance. We will collect data from manufacturing and logistics organizations. Use machine learning algorithms to evaluate the relationships between AI capability, predictive analytics, supply chain resilience, sustainability and operational performance. This framework should improve decision-making reduce costs and enhance performance.
Keywords
Artificial Intelligence, Predictive Analytics Machine Learning, Supply Chain Resilience, Sustainable Supply Chain
References
[1] Agrawal, R., Gopal, K., Sharma, Y., & Singh, R. K. (2021). Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research. Operations Management Research, 15, 378–398.
[2] Jahin, M. A., Naife, S. A., Saha, A. K., & Mridha, M. F. (2024). AI in supply chain risk assessment: A systematic literature review and bibliometric analysis. arXiv.
[3] Naz, F., et al. (2022). Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions. Business Strategy and the Environment, 31(5), 2400–2429.
[4] Qu, C., & Kim, E. (2024). Reviewing the roles of AI-integrated technologies in sustainable supply chain management: Research propositions and a framework for future directions. Sustainability, 16(14), 6186.
[5] Smyth, C., Dennehy, D., Wamba, S. F., Scott, M., & Harfouche, A. (2024). Artificial intelligence and prescriptive analytics for supply chain resilience: A systematic literature review and research agenda. International Journal of Production Research, 62(23), 8537–8561.
[6] Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research, 327(2), 995–1022.
[7] Sinha, M., & Agarwal, S. (2025). A systematic review of AI-powered predictive analytics for supply chain resilience and risk mitigation. SSRN.
[8] Al-Naimi, M. S. (2024). The impact of AI in supply chain resilience: A systematic mapping review. Gateway Journal for Modern Studies and Research.
[9] Darkoh, G. O. (2026). AI-enabled sustainable supply chain management: A systematic review of resilience, traceability and ESG performance. Journal of Scientific Research and Reports, 32(6), 631–647.
[10] Hasan, M. R. (2024). AI-enhanced decision-making for sustainable supply chains: Reducing carbon footprints in the USA. arXiv.
[11] Anumula, S. K. (2025). Design-based supply chain operations research model: Fostering resilience and sustainability in modern supply chains. arXiv.
[12] Noman, A. A., Akter, U. H., Pranto, T. H., & Haque, A. K. M. B. (2022). Machine learning and artificial intelligence in circular economy: A bibliometric analysis and systematic literature review. arXiv.
[13] Smyth, C., Dennehy, D., Wamba, S. F., Scott, M., & Harfouche, A. (2024). Artificial intelligence and prescriptive analytics for supply chain resilience: A systematic literature review and research agenda. International Journal of Production Research. Bottom of Form
How to cite this paper
@article{1720153,
author = {Dr. E. Vanitha, Dr. R. Sivasankari, Dr. Y. Salini},
title = {AI-Driven Predictive Analytics for Sustainable and Resilient Supply Chains: An Integrated Machine Learning Framework},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {3452-3458},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1720153.pdf},
abstract = {Supply chains are getting disrupted more often because of pandemics, conflicts, climate change and market volatility. This shows that we need sustainable supply chain management. Artificial Intelligence (AI) and predictive analytics can help improve forecasting, risk management and environmental sustainability. Most studies focus on individual AI applications not on combining multiple machine learning techniques. This study proposes a framework that combines demand forecasting, supplier risk prediction, inventory optimization and sustainability assessment to improve supply chain resilience and performance. We will collect data from manufacturing and logistics organizations. Use machine learning algorithms to evaluate the relationships between AI capability, predictive analytics, supply chain resilience, sustainability and operational performance. This framework should improve decision-making reduce costs and enhance performance.},
keywords = {Artificial Intelligence, Predictive Analytics Machine Learning, Supply Chain Resilience, Sustainable Supply Chain},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1720153}
}