Artificial Intelligence in Food Supply Chains: Distribution Optimization and Waste Reduction

One-third of the food made for people to eat gets. Thrown away but the tools used to fight this issue mostly only look at how well we can predict it (Onyeaka et al. 2025). This chapter says that just predicting won't cut down on loss. Waste happens at points where decisions are made. How much to restock when to send out goods how to divide up products when to lower prices and how to get rid of things. These points aren’t fixed just by better predictions. The chapter connects three ways of looking at the problem with the real-life ways that fresh food ends up being thrown guessing how much people will want figuring out how long food will last based on basic chemistry and temperature effects and planning how to deliver food in a way that deals with it being perishable time limits and pollution. The proof includes a 14.8% drop in food waste per store and 26,705 tonnes of CO₂ not released because of AI in stores (Onyeaka et al. 2025) predicting how long food will last with changing storage conditions (Rashvand et al., 2025) using AI to watch and support recycling efforts (Onyeaka et al. 2023) and a look at 104 studies about food loss before it gets to people between 2015 and 2024 (Ma et al. 2026) that showed prevention isn’t getting much attention as fixing things later. Three main points are made: that knowing what is happening matching loss with solutions keeping limits and making sure everyone has the right reasons to help together set limits on what AI can do; that expanding the cold chain is only worth it if the extra energy needed is less than the waste it stops; and that when people say something is good, for the environment they need to say exactly what they mean show how sure they are and tell where the waste is really going.