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@article{171050,
        author = {Arpita Bhausaheb Surkunde},
        title = {The Impact of Big Data Analytics on Efficient Inventory Management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2808-2811},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171050},
        abstract = {In the modern era of digital transformation, big data analytics has emerged as a powerful tool for optimizing inventory management processes. By harnessing vast amounts of data generated across supply chains, organizations can gain actionable insights into demand forecasting, inventory levels, and replenishment strategies. This research explores the impact of big data analytics on enhancing inventory management efficiency, focusing on key metrics such as cost reduction, improved order accuracy, and minimized stockouts and overstocking. The study delves into the application of predictive analytics, real-time monitoring, and machine learning algorithms to identify patterns, predict demand fluctuations, and automate decision-making processes. Furthermore, it examines the challenges of integrating big data analytics into traditional inventory systems, including data quality, infrastructure requirements, and the need for skilled personnel. The findings underscore the transformative potential of big data analytics in enabling data-driven inventory strategies, fostering resilience, and achieving a competitive edge in dynamic markets. This paper contributes to the growing body of literature by providing insights into how big data analytics revolutionizes inventory management for sustainable business operations.},
        keywords = {Big Data Analytics, Inventory Management, Supply Chain Optimization, Data-Driven Decision Making, Predictive Analytics, Demand Forecasting, Demand Planning, Supply Chain Efficiency},
        month = {December},
        }