AI in Transport & Logistics
Predictive Fleet Maintenance
AI based predictive maintenance uses a variety of data from IoT sensors imbedded in vehicles, fleet data, weather data, and more to determine which components should be replaced before they break down or cause an accident. AI models can look for patterns in data that indicate failure modes for specific components or generate more accurate predictions of the lifespan for a component given environmental conditions and usage. When specific failure signals are observed, or component aging criteria are met, the components can then be replaced during scheduled maintenance windows. AI systems can even warn drivers and fleet managers that components may fail soon, so that they can take proactive measures to change vehicles to keep scheduled appointments.
Depending upon the types of goods they produce, manufacturers have to ensure that they arrive in good condition. According to the Food and Agricultural Organizations of the United Nations, approximately 40% of food is lost on average in post-harvest and processing stages, in developing countries. This obviously impacts the food manufacturing, processing and transportation companies. Moreover, most industrial manufacturers are sensitive to increases in any transportation cost.
Quality management of products through the transit is crucial. Manufacturers can predict the quality of their products under given transit conditions, hence giving them the opportunity to improve refrigeration (for perishable products) or optimize routes (for raw materials and finished goods). AI based transportation optimization leverages route information, weather data, fuel cost and other such factors to arrive at the best possible route as well as to predict the quality of goods at the destination.