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@article{180307,
        author = {Siva Kannan Ganesan},
        title = {SUPPLY CHAIN TECHNOLOGY MODERNIZATION: USING PREDICTIVE ANALYSIS OPTIMIZE EFFICIENCY AND INVENTORY ACCURACY},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1109-1117},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180307},
        abstract = {This paper explores the application of predictive analytics in optimizing supply chain efficiency and inventory accuracy. As global supply chains grow more complex and dynamic, traditional inventory management practices are increasingly inadequate. Predictive analytics, leveraging machine learning, statistical models, and real-time data, offers a data-driven approach to forecasting demand, managing inventory, and mitigating risks. The paper presents a proposed framework for integrating predictive analytics into supply chain operations, addressing key challenges such as data quality, system integration, and external disruptions. Despite the promising benefits, the adoption of predictive analytics faces limitations, including high initial costs, technological complexity, and reliance on accurate data. The paper concludes by identifying areas for future research, including improving data integration, developing scalable models for small and medium-sized enterprises (SMEs), and enhancing decision-making through real-time analytics. This paper contributes to the growing body of literature on predictive analytics in supply chain management and offers practical insights for businesses looking to optimize their supply chain operations.},
        keywords = {Predictive analytics, supply chain management, inventory optimization, machine learning, demand forecasting, data integration, operational efficiency, risk management, decision support systems, real-time analytics, supply chain resilience.},
        month = {June},
        }