Predictive Analytics: A Machine Learning Approach for Insights in Food Production and Sales

Food wastage is one of the most persistent challenges of the food industry, leading to immense economic and environmental losses. The root cause of such a problem lies in mistaken demand prediction, which means overproduction, waste spoilage, and improper allocation of resources. It needs powerful systems that can accurately predict future demand according to historical trends and contextual information related to it. A predictive system leveraging Long Short-Term Memory networks addresses the challenges of food wastage and inefficient demand forecasting in the food industry. This helps the system to forecast the food quantities and transactions using the historical sales data incorporated with features such as day, month, and item-specific attributes with 89.68% accuracy. The same helps in reducing the wastes and optimization of inventory management and better use of resources, thus yielding major economic and environmental benefits toward sustainable food production.