Challenges

Organizations that depend on a fleet of vehicles for daily operations understand how crucial it can be to keep them running smoothly. Missed appointment for service providers result in customer churn and lost revenues from new installations. Traditional preventative maintenance processes require vehicles to be repaired at regular intervals based on time or usage. These methods, however, still result in instances of vehicle breakdown resulting in accidents, idle workers, lost revenues and angry customers. In addition, preventative maintenance strategies may replace parts that still have significant working life, which can be a waste of time and money.

Opportunity

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.