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@article{176049,
        author = {Teja Sri Bheemarasetti and Damarasinghu Vinay Kumar and Gaddi Prasad and Koppada Deeksha and Chokkakula Devi},
        title = {Intelligent Canteen Management Using Machine Learning for Real-Time Ordering and Sustainable Operations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5295-5302},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176049},
        abstract = {Canteen management in colleges often faces several challenges that negatively affect the experience of students, faculty, and canteen  staff. These issues include long queues, missed meals, and food wastage, all of which lead to dissatisfaction. Traditional methods of order management and inventory control are prone to inefficiencies, human errors, and difficulty in predicting demand. This results in either food shortages or surplus, contributing to waste. The Smart Canteen Management System provides an effective solution by integrating real-time data processing and machine learning algorithms. The system predicts demand trends and optimizes inventory management, ensuring that food is available when needed. Users can place pre-orders and track their orders through a digital platform, reducing wait times and improving convenience. By accurately forecasting demand, the system minimizes food wastage, contributing to sustainability. This technological advancement enhances operational efficiency, streamlines order processing, and improves overall user satisfaction. The Smart Canteen Management System transforms traditional canteen operations, offering a more efficient, user-friendly experience for all stakeholders.},
        keywords = {Canteen digitalization, Demand forecasting, Inventory Optimization, Machine Learning in Canteens, Pre-Ordering System.},
        month = {April},
        }