Isn’t predicting future maintenance needs what many, if not all, fleet managers desire? We’re not just talking about getting advance warnings via remote diagnostic technology or how ongoing fluid analysis programs can extend drain intervals for engine oil, transmission oil, and engine coolant. We’re talking about giving fleets the ability to know when—based on accumulated data and analytical tools—specific components, such as water pumps or wheel hubs, need to be replaced before they fail, thus leaving a truck (not to mention its revenue-generating cargo) stranded on the side of the road.

Does that capability exist today? To some degree, yes, say experts. Yet at the same time, developing such “predictive models” is no simple task, stresses Michael Riemer, vice president-products and channel marketing for maintenance software firm Decisiv.

METHODS & MODELS

It is not simple because there are multiple factors that contribute to various failures in a fairly complex environment such as a heavy-duty truck,” he explains.

“That being said, though, being able to capture mileage, engine hours, gas usage, idling time, and other ‘metered’ truck activities are, at a minimum, a good starting place,” Riemer notes. “Next, you need to have enough data to create a statistically relevant model. This means that you have to have enough trucks and enough data about those trucks as well as the ability to analyze the various possible contributing factors. Finally and most importantly, you need a method for capturing all this data—the telematics data, the repair data, etc. —and the tools to then develop a predictive model.”

This last piece is the most immature in the current truck maintenance ecosystem, he points out. “Our research shows that access to a complete set of information about all service and repair events across internal and external locations is still very difficult to find and manage,” Riemer says.

Traditionally, he explains, many of the telematics systems, OEM warranty and repair data, intelligence gleaned from unique algorithms tied to fault codes, VMRS (vehicle maintenance reporting standards) codes, repair management systems, and driving behavior (hard braking, fast acceleration, hard turning) all remain in separate information silos with little or no ability to cross-mingle such data sets.

“Thus, we are still very early in terms of being able to provide true predictive models,” he emphasizes. “This does not mean that some OEMs are not already using their proprietary knowledge about their trucks or engines, or that fleets, especially very large ones, can collect bits and pieces of data to create more intelligent preventive maintenance schedules. But that is not truly predictive maintenance.”

Still, Alex Ognjanac, vice president-sales and marketing for telematics provider Isotrak, contends that trucks continue to evolve and provide not only actual or historical data but true predictive data as well.“

This is vital [because] one breakdown can cost a tremendous amount of money in lost or perishable inventory or missed deadlines,” he says. “[Truck] manufacturers and [telematic] application providers alike are focusing more attention on other elements of a vehicle that have traditionally not been given much attention, such as engine temperature, oil pressure, tire pressure, and more—with all of those leading to [vehicle] efficiency improvements and savings for the carrier.”