Predictive fleet maintenance: Bridging the gap between knowledge and action
It’s time to let go of the current popular myth that trucking, as a profession, is outdated.
Did you know that up to 80% of U.S goods are still being shipped primarily by truck? That’s right, trucking remains the dominant mode of transportation for the U.S., followed by ocean liners and rail. Not only is the American trucking industry dominant, it’s innovative. There are startups popping up everywhere to offer new technology to the industry in the form of sensors and other data collection devices, navigation, driver safety, and more… just name the challenge and someone is creating new tech for the trucking industry to support it.
This new technology is vital to the success of the industry, but it isn’t currently being used to maximum advantage. If you have sensors collecting real-time data from your assets on the road, but you aren’t applying that knowledge in real time to keep up with maintenance, fuel costs, route optimization, or driver safety, why are you collecting it?
A smarter use of your data would be to use AI to predict outcomes enough to increase asset up-time while decreasing cost per mile.
Frazzled, overworked fleet managers and operations managers usually take a reactive approach to fleet maintenance. You have to! You are often short drivers as well as time and are somewhat removed from the data all of this new technology is collecting. However, having your drivers pull their rig into the nearest repair shop when maintenance is needed then hop right back on the road is reactive (and unnecessary).
Why? Reactive maintenance means that you aren’t tracking the recurring problems of a truck (or the road the truck is on, or the route time, or the fuel efficiency), you’re just losing time scrambling to fix issues as they happen. This is crushing your cost per mile. In fact, if something happens to a truck on the road, it is 10 times costlier to fix than if the fleet manager had been able to predict it and fix it in the service center. This is where data science comes in.
Let’s look at some hard facts.
When fleet maintenance is done reactively, trucks are in the shop every 14 – 17 days.
Only 25% of a reactive fleet maintenance budget goes to scheduled maintenance, which means 65% of your fleet maintenance budget is thrown after maintenance done on the fly, and only 10% is left for the DOT-required proactive maintenance you need to do.
There has to be a better way – a way to make the entire process more predictable and efficient without creating areas of risk for the truck or driver. That’s where data science comes in.
Imagine if you could combine your per truck (per asset) data with data from all of your repair shops and repair records, road conditions, sensors, weather reports, navigation times, and more. Then, you could predict fleet maintenance, which would save you significant costs per mile per asset and improve the performance of your entire fleet. That makes your job as an operations manager or fleet manager much easier and less stressful overall.
We’ve not only imagined it – we’re putting action behind our words and leveraging data science for predictive fleet maintenance across the industry. Ask us how.
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