AI-Driven Predictive Analytics for Sustainable and Resilient Supply Chains: An Integrated Machine Learning Framework

@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}
  }