Reducing Food Waste via AI Based Supply Chain Optimization

Abstract

Food waste is a critical global issue, with nearly one third of all food produced being lost or wasted annually, leading to significant economic, environmental, and social consequences. This paper proposes an AI based supply chain optimization framework to minimize food waste by improving demand forecasting, inventory management, and distribution efficiency. Leveraging machine learning (ML) algorithms such as time series forecasting, reinforcement learning, and prescriptive analytics the system dynamically adjusts procurement, storage, and logistics decisions in real time. By analyzing historical sales data, weather patterns, and market trends, the AI model reduces overstocking, spoilage, and inefficiencies in perishable goods supply chains. A case study in the retail sector demonstrates a 20 30% reduction in food waste while maintaining service levels. The results highlight AI’s potential to transform food supply chains into sustainable, waste aware ecosystems, aligning with SDG 12 (Responsible Consumption and Production).