Reducing Food Waste Using Machine Learning Models: Forecasting and Optimization Approaches

Abstract

Food waste is a serious problem, with approximately one-third of all food produced globally being wasted each year. This issue not only exacerbates food insecurity but also has significant environmental impacts, such as greenhouse gas emissions, land use, water consumption, and loss of biodiversity, as well as economic losses. Economically, food waste represents a substantial loss of resources, including labour, energy, and capital invested in food production, processing, and distribution. This problem is recognized as a global crisis not only due to inefficient use of resources but also because of its impact on food security. With the rapidly growing global population, addressing food waste has become an urgent necessity to ensure sustainable food systems. Machine learning (ML) offers innovative solutions to this challenge by using large datasets and advanced algorithms to predict food demand more accurately, optimize inventory management, and enhance supply chain efficiency. ML has significant potential in reducing food waste because it can better predict future demands based on past data and adjust stock levels accordingly. This is particularly advantageous in managing perishable foods, as they have a higher likelihood of being wasted. Machine learning algorithms can analyze large datasets to more accurately predict food demand, optimize inventory management, and improve supply chain efficiency. These algorithms, categorized into three main approaches supervised learning, unsupervised learning, and reinforcement learning can be used in various ways to reduce food waste.