The Benefits of Predictive Maintenance

Real-time technologies help reduce over-maintenance and more

The data collected from the vehicles includes many buckets of both detailed and summarized statistics, including:

  • Miles per gallon.
  • Gallons of fuel consumed.
  • Gallons of diesel exhaust fluid consumed.
  • Total miles.
  • Trip miles.
  • Engine hours.
  • Idle time.
  • Sleeper time.
  • Miles run in top gear.
  • Miles traveled in cruise control.
  • Number of hard stops.
  • Miles traveled with an in-dash MIL lamp on.
  • Top speed.
  • Time at various speeds.
  • Time/miles in engine RPM bands.

This data can be analyzed to determine the performance of both the vehicle and the driver.

Telematics technology has become so helpful to fleets using it they are now beginning to ask suppliers to add sensors onto the vehicles to gather more data and monitor more systems, such as: tire pressures, oil quality, filter plugging status, battery life, brake life, alternator performance and starter amp draw.

Of course none of this data is of any value to the fleet if they don’t start to collect and systematically analyze it.

It is easy to imagine a future where fluids and components are not changed based simply on time, miles and or some statistic generated in a lab environment on expected life. Rather, they are changed based on actual performance degradation onboard the vehicle in real-time.

Predictive maintenance helps technicians determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach offers cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted.


Without a doubt, strategic predictive maintenance is a key component of achieving lowest cost of ownership. This is an untapped opportunity for fleet managers to lower their costs through more sophisticated, data-driven predictive maintenance systems that are currently available.

Nowadays, fleet managers are more aware of costs and simply must produce more uptime and fewer breakdowns. Predictive maintenance systems can help with this by supporting vehicles more proactively, than reactively.

Making proper use of the data is the key. That data can and will influence driver behavior for optimum operation, which means reduced maintenance costs, and determining a fleet’s individual, accurate maintenance schedules.


Realizing the overall net savings potential of shortening the equipment lifecycle may be the most under-utilized predictive maintenance strategy of all.

The data mining capabilities today, matched with good analytics, can actually pinpoint the moment when the cost of even the most efficient preventive maintenance schedules, combined with other factors, becomes higher than the overall cost of replacement. Determining that tipping point, and acting on it quickly, is truly a critical predictive maintenance decision.

Elaborating on the benefits of good, solid vehicle performance data and lifecycle management, John Flynn, CEO of truck lessor Fleet Advantage, says: “Applying business intelligence tools to that data can get you a true total cost of ownership. And that goes beyond fuel consumption, and considers a wide range of lifecycle costs that change over time.

“Total cost of ownership isn’t a simple math exercise done when you originally purchased a truck. Fuel costs change, the fuel economy potential of equipment changes, replacement part costs change.

“The most accurate lifecycle analysis needs real-time data because the factors that drove the initial lifecycle decision have changed, and so should the vehicles replacement strategy.”


Still another paradigm on the horizon in predictive maintenance consists of not just relying on manufacturers’ standards, but creating a fleet’s own, more tailored, more accurate maintenance approach. This could take into account the nuances and differences in trucking operations, such as truckload versus pickup and delivery.

There is a downside to generalizing assumptions about loose data, including the risk of running up costs and under-estimating the extended life value of some components.

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