Keeping food safe during delivery can be a challenge, especially when temperatures cannot easily be controlled. This talk shows how computer- based models can help predict and manage food safety risks during the “last mile” of delivery, from warehouses to restaurants or customers. Using real examples from several companies, the presentation explains how temperature and time affect the growth of bacteria such as Listeria or Salmonella. By entering real-world temperature data into free modeling tools like ComBase, we can predict bacterial growth and assess food safety risk. These models give a practical and reliable way to understand risk and make better decisions about delivery times and temperatures. In most cases, the models are “fail-safe,” meaning they overestimate risk rather than underestimate it, helping companies stay safe. Predictive modeling gives food businesses and regulators a science-based way to keep food safe, reduce waste, and maintain quality throughout the delivery process.