Cleveland, Ohio
Nicholas Biggar
Hayden District Director
Greater Cleveland Regional Transit Authority
The predictive maintenance program at the Greater Cleveland Regional Transit Authority began in 2015. Now in our fourth year with the program, we have had successes and lessons learned. Ultimately, we view this maintenance philosophy as our strategy going forward and are committed to it for the long-term.
Our first step toward implementation was to hire a consultant group who had experience with predictive maintenance at other transit properties so that we could learn the methodology. Together with the consultant, we analyzed historical maintenance records and parts usage of our highest profile but least reliable bus fleet: the HealthLine. This effort identified parts to be replaced and maintenance tasks to be performed at annualized intervals throughout the life of the fleet. We also needed to educate the workforce on our shift in maintenance strategy. We did this through a series of newsletters explaining the premise of predictive maintenance and why we felt that this was the best path forward.
Our implementation of predicative maintenance on the HealthLine fleet was not without challenges. By starting on a fleet that had been in operation since 2008, we essentially performed a mid-life overhaul of each bus at our first maintenance interval. This led to delays in production. To address these delays, we decided to combine maintenance intervals to ensure future compliance with the program. This required great collaboration amongst several departments.
While rolling out predictive maintenance to the HealthLine vehicles, we began expanding this program to include our newest bus fleets, purchased in 2015 and 2017. Through the course of our expansion, we have become smarter and more nimble in our approach. We have identified a “prototype bus” that goes through each maintenance interval several months ahead of the rest of the fleet. This allows us to identify any issues with the parts kits before moving into production on the rest of the fleet. Earlier this year, we added a formal Quality Assurance check performed by our Fleet Management department, to ensure that all work was completed correctly and that all work orders are closed.
To analyze the effectiveness of this program, we track our ROI through the service reliability of our fleets (miles between service interruption) and inventory costs. Parts costs per vehicle mile has increased, as expected, but so has vehicle reliability. We view this as an acceptable cost for a more reliable service. In 2017, GCRTA achieved 15,000 miles between service interruption system-wide, far surpassing our 8,000 MBSI goal. The 2015 bus fleet, the first fleet to go through our predictive maintenance program since inception, averaged over 30,000 miles between service interruption.
Our implementation of predictive maintenance was not without its road bumps. Ultimately we view this approach as our standard maintenance strategy for the future. We currently have three fleets enrolled in our predictive maintenance program and are committed to adopting this model for all new fleets going forward.
