International Journal of Science and Technology (IJST)

AI-Based Predictive Waste Reduction System in Supermarkets: A Dashboard-Driven Approach

Food waste represents one of the significant global sustainability challenges, with supermarkets being substantial contributors due to inadequacies in demand forecasting and inventory management. This paper presents an AI-Based predictive waste reduction system designed for supermarket environments. The system integrates machine learning-driven risk scoring, real-time inventory monitoring, and an interactive analytics dashboard to identify high-risk products and deliver actionable recommendations such as dynamic discounting, stock redistribution, and donation management. Built on a modular React.js/TypeScript architecture with Tailwind CSS styling, the platform offers multi-store support, configurable alert thresholds, and a responsive user interface. Empirical testing demonstrates the system’s capacity to reduce food waste by up to 47% and generate daily savings of approximately $2,847/store through proactive interventions. The paper details the system architecture, feature design, database schema, and evaluation results. It also looks ahead to real world use, highlighting how the system could relate to IoT sensor and integrated with cloud-based machine learning to make it practical, scalable and ready for use.