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The problem we are facing is truck downtime and associated costs, as well as the complexity of managing heterogeneous fleets. Leveraging the power of data analytics, we aim for a long enough prediction horizon in order to predict failures weeks or even months before they occur. The relevant historical and real-time data for our models is collected by the hardware that is already installed in the trucks (telematics device).
Through predictive maintenance, we can optimize the maintenance schedule and provide information when to replace components most cost-effective. The value to the fleet managers is increased transparency on the current fleet health status and increased uptime and reliability.