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@article{205334,
        author = {Mrs. S. Suganya and Ms. S. Prabhavathi},
        title = {AN INTELLIGENT INVENTORY MANAGEMENT SYSTEM WITH SALES PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5999-6003},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205334},
        abstract = {Inventory management plays a crucial role in ensuring the smooth operation of businesses by maintaining optimal stock levels and reducing losses caused by overstocking or stock shortages. This project, titled "An Intelligent Inventory Management System with Sales Prediction Using Machine Learning," is developed based on the Inventory Management System framework and enhanced with machine learning capabilities to improve decision-making. The system provides functionalities such as product management, stock tracking, supplier management, sales recording, and inventory monitoring through a user-friendly interface. In addition to traditional inventory operations, a machine learning-based sales prediction module is integrated to analyze historical sales data and forecast future product demand. By utilizing predictive algorithms, the system helps businesses estimate upcoming sales trends, optimize inventory levels, and automate replenishment planning. The predicted sales insights enable managers to make data-driven decisions, reduce inventory holding costs, minimize stockouts, and improve overall operational efficiency. The proposed system combines inventory automation with intelligent forecasting techniques, offering a smart and scalable solution for modern retail and warehouse management environments.},
        keywords = {Inventory Management, Machine Learning, Sales Prediction, Demand Forecasting, Long Short-Term Memory (LSTM), Inventory Optimization, Predictive Analytics.},
        month = {June},
        }