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@article{196696,
        author = {Avantika Arvind Misale and Muskan Nitin Sohaney and Pratik Anil Deokar and Rawate Sahil Vitthal and Dr. Sunita Parinam},
        title = {Next-Gen Supply Chain Optimizer: An Agentic AI Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3479-3488},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196696},
        abstract = {Traditional supply chain management often suffers from inefficiencies due to inaccurate demand forecasting, poor inventory control, and an inability to adjust to unexpected disruptions. These issues show the weaknesses of traditional systems, which usually depend on fixed planning and manual steps. This paper presents an Agentic AI-driven framework that aims to create a smart, flexible, and independent system for optimizing supply chains. The problem is framed as a real-time decision-making process. The system examines changing data inputs, such as sales, inventory levels, and logistics, to take proactive corrective actions. The suggested solution uses machine learning for predicting demand, optimization methods for planning inventory and logistics, and autonomous agents for making decisions and simulating scenarios in real-time. Through conceptual modeling and a review of existing research, this study shows that an Agentic AI approach can greatly improve efficiency, lower logistics costs, and strengthen supply chain resilience. The findings build a foundation for creating a new generation of intelligent supply chain systems that can optimize themselves and handle disruptions effectively.},
        keywords = {Agentic AI, Supply Chain Optimization, Reinforcement Learning, Predictive Analytics, Autonomous Decision-Making, Inventory Management, Logistics.},
        month = {April},
        }