This research presents the development and deployment of an intelligent inventory management and sales analytics platform specifically designed for supermarket operations. The system addresses critical inefficiencies in traditional manual inventory processes through integration of React.js frontend architecture, Flask-based RESTful API backend, and SQLite database management with advanced machine learning algorithms. Three specialized ML components form the analytical core: Facebook Prophet for temporal sales forecasting, Apriori algorithm for market basket analysis, and Random Forest classification for automated reorder predictions. The platform features real-time inventory monitoring, predictive demand analytics, cross-selling recommendations, and comprehensive reporting dashboards. Extensive testing across multiple retail environments demonstrates 94% accuracy in sales predictions, 91% precision in reorder classifications, and 87% user satisfaction rates. The modular architecture supports deployment scalability from single-store operations to multi-branch retail chains while maintaining cost-effectiveness for small-to-medium enterprises. Implementation results show 38% reduction in stockout incidents, 32% decrease in excess inventory costs, and 24% improvement in overall operational efficiency.
Abdul Majid K (Sat,) studied this question.
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