AI-Driven Restaurant Inventory and Waste Management System Using Short-Term Demand Forecasting

This research presented an AI-powered system for restaurant inventory and waste management aimed at improving operational efficiency and promoting sustainability. The study addressed key challenges faced by the hospitality sector, including inaccurate demand estimation, inefficient inventory tracking, and high levels of food waste. By integrating real-time inventory monitoring with machine learning-based demand forecasting, the proposed system enables more informed and proactive decision-making. The use of predictive models allows restaurants to align stock levels with expected demand, thereby reducing overstocking and minimizing the risk of shortages. In addition, the incorporation of a waste segregation mechanism ensures that food waste is handled systematically and responsibly. A significant contribution of this work is the inclusion of a food redistribution module, which connects surplus food with non-governmental organizations. This not only helps in reducing waste but also supports social welfare by making excess food available to those in need. The results indicate that the combined approach of prediction, monitoring, and redistribution can create both economic and environmental benefits. Overall, the proposed system demonstrates how the integration of artificial intelligence with traditional inventory practices can transform restaurant operations into a more efficient, data-driven, and sustainable model. A key strength of the proposed system lies in its ability to transform raw operational data into actionable insights, enabling restaurants to move from reactive management to predictive decision-making. However, the system’s performance depends on the availability and quality of historical data. Limited or noisy data may affect the accuracy of demand predictions.

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