How do you know if a truck needs maintenance before it breaks down or if breaks down anywhere in the country, know the exact location and send help to fix them right where they are or bring them to the closest maintenance shop? And do this for a fleet of 400,000 trucks. As part of the Connected Fleet, Penske collects IoT data including the Odometer, Location, and Engine codes of all the trucks realtime and ingest into their Data lake to derive real-time analytics. Penske identifies when a truck is going to break down before it does and perform maintenance in advance to reduce the downtime and keep the uptime SLO. Data ingestion platform leverages Spring Boot, SCDF, Kafka Streams, and fit for purpose Databases like Postgres, MySQL, Gemfire, and Greenplum. Once data is available, Machine learning applied to create models for performing maintenance on trucks in advance. You will learn how Penske has leveraged Spring technologies and Data solutions to scale the solution on Tanzu Platform.
IoT Scale Event-Stream Processing for Predictive Maintenance at Penske