AI-Driven Food Supply Chain Management with Machine Learning for Demand Forecasting

The supply chain of food is essential for product quality, minimizing waste, and increasing timely delivery. This project offers a machine learning system based on RF, SVM, KNN, and XGBoost to handle the major challenges including demand forecasting, inventory optimization, quality control, and supplier assessment. XGBoost accurately predicts demand, while Random Forest utilizes supplier performance and product quality classification. KNN categorizes similar products and logistics routes, and SVM identifies outliers. Through the utilization of real-time data, this method improves efficiency in operations, reduces losses, enhances traceability, and maintains a sustainable, cost-efficient, and dependable food supply chain network. This project aims to revolutionize food supply chain management by employing machine learning to create a resilient, cost-effective, and sustainable system that enhances quality control, reduces waste, and optimizes resource allocation for all stakeholders.