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, demand
supply 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.