• Shalini Dinesh
• Vijaya Manohar Cherukuru
• Dinesh Dakshinamoorthy
• Abraaz Mohammed Khaja

AI is changing the whole dimension of how supply chains are managed and optimized. With AI, supply chain professionals can, more than ever, predict demand with a high level of accuracy, know in advance which disruptions might occur, and maintain optimum inventory levels. This improves the total efficiency while cutting down on costs and minimizing the risks of excess stock or stockouts. According to recent research by Zamani et al. in 2023 and Belhadi et al. in 2024, AI and big data analytics are supposed to be very effective at enhancing resilience in supply chains through better resource management and maintenance of performance in supply chains. Of relevance is the capability of machine learning algorithms—considered a subset of AI—to make predictions and provide valuable insights through their ability to process vast data amounts. As supply chains are becoming more global and interdependent, AI-driven predictive analytics becomes very instrumental in being able to navigate these complexities and uncertainties by taking a more proactive stance toward the management of the supply chain. It is in this transformative period of supply chain management that AI-driven predictive analytics ushers in an epoch characterized by data-driven decision-making, resilient, and agile supply chains.

Supply Chain Disruptions, Machine Language, Artificial Intelligence, Robotics

"PREDICTIVE ANALYTICS FOR SUPPLY CHAIN DISRUPTIONS: THE ROLE OF AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.f572-f580, September-2022, Available :http://www.jetir.org/papers/JETIR2209598.pdf

"PREDICTIVE ANALYTICS FOR SUPPLY CHAIN DISRUPTIONS: THE ROLE OF AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppf572-f580, September-2022, Available at : http://www.jetir.org/papers/JETIR2209598.pdf