ENHANCING FOOD SUPPLY CHAIN EFFICIENCY USING AI-BASED DEMAND FORECASTING

Abstract

Food supply chain management is a crucial component of the agriculture and retail sectors, aimed at ensuring the efficient production, transportation, and distribution of food products to end consumers. In India, this supply chain faces significant challenges, including excessive food wastage, demandsupply mismatches, and delays in logistics. According to reports, nearly 40% of the food produced in India is wasted annually due to systemic inefficiencies, resulting in economic losses of approximately ₹92,000 crores (around $12 billion). With the country's population projected to reach 1.6 billion by 2050, it is imperative to address these inefficiencies to ensure long-term food security and economic resilience. Traditional methods for demand forecasting—such as historical averages, static rule-based systems, and manual forecasting based on human intuition—often fall short. These approaches fail to capture dynamic market variables such as seasonal variations, pricing changes, and promotional activities, leading to overproduction, underutilization, and significant resource wastage. The increasing complexity of food supply chains, driven by urbanization, shifting consumer behavior, and climate variability, underscores the need for more intelligent forecasting solutions. This project proposes a machine learning-based approach using Convolutional Neural Network (CNN) regressors to enhance food supply chain efficiency. The model extracts both spatial and temporal features from historical sales and contextual data, allowing for precise weekly demand predictions across different food categories and regions. Convolutional layers are particularly adept at identifying patterns such as seasonal trends, promotional influences, and regional consumption dynamics. These insights enable improved inventory management by aligning stock levels with actual demand, thereby reducing spoilage and overstocking. Additionally, the system supports dynamic pricing strategies by incorporating real-time pricing trends and promotional data. It also enhances logistics planning by forecasting demand at various nodes within the supply chain. By leveraging CNN-based regression models, the proposed system delivers accurate demand forecasting, minimizes food wastage, optimizes resource allocation, and supports data-driven decision-making in pricing and inventory control. This intelligent forecasting approach paves the way for a more sustainable and efficient food supply chain in India.