Using data to move toward predictive maintenance

Oct. 24, 2016
Analyzing repair data can help avoid unplanned service failures

For every day a truck is down for service, it can cost your fleet $800–$1,000 or more, according to Decisiv’s Michael Riemer. While many service providers are working with fleets to significantly reduce the time it takes to complete a repair — employing a triage type system, or streamlining the communication process, for instance — a better strategy is to eliminate the need for the repair in the first place.

While no one has yet to manufacturer a truck that never breaks down, you can take steps to reduce the number of breakdowns your fleet experiences and data can help.

The first step is to collect data each-and-every-time the truck is in the shop — your own or that of your outside service provider. This includes data from PMIs, PM service, roadside breakdowns and other service events. It also needs to include data from DVIRs. You need as much information as you can on each truck in your fleet. And the data needs to be collected by individual truck.

Next, devise a way to look at that data based on asset type. If you are running a particular brand of truck, with a particular engine all of the same model year and spec’d the same way, you will want to look at the data to see wear and failure trends on the various components spec’d on the vehicle.

If your analysis shows that a certain component consistently fails at 100,000 miles, doesn’t it make sense to execute a service campaign to get all similar vehicles in to replace that component as the vehicles get close to the 100,000-mile mark? I realize this means you will have to take the truck off the road to be serviced, but a scheduled service appointment costs less than an on-road breakdown.  You avoid emergency roadside assistance calls, tow charges and perhaps even more important, you don’t have irate drivers sitting on the side of the road.

There will be an initial time and cash outlay when setting up a system to allow you to look at all this data, but once you get the system up-and-running you can define parameters and focus on items that fall outside of the normal operating life for various components.

In time you will move to a predictive maintenance model which will allow you to focus on maximizing uptime rather than reducing downtime. And these days, uptime is the name of the game.

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