Telematics in the heavy-duty trucking industry is creating large amounts of data, which will increase as trucks become more technologically advanced with sensors being added to seemingly every component on the vehicle. All this data can be used to provide predictive maintenance information for fleets to decrease their trucks’ downtimes.
However, the key isn’t necessarily more data. It’s clean, optimized data that provides actionable information for aftermarket distributors, suppliers and fleets.
“The very business model of commercial vehicles has changed fundamentally. In 2022, the problem to solve is what the truck can do for me,” in the data it provides, said Sandeep Kar, Noregon chief strategy officer, during a session, “Paving the Way: How the Heavy-Duty Aftermarket Can Profit from Telematics and Predictive Maintenance,” held last week at Heavy Duty Aftermarket Week 2022 in Grapevine, Texas.
If a fault light comes on in a truck in transit, “technology exists today to guide that vehicle to a service and maintenance location. That service and maintenance location now knows this vehicle is coming in with this part or service requirement, which reduces dwell time,” Kar said. “This is where the aftermarket has a huge advantage over the OES channel. If we can get it right, we can steal business away from the OES channel and we can reduce dwell time and get the driver back on the road. There are some mission-critical benefits the aftermarket can provide fleets and monetize that opportunity. That’s the future.”
Ben Johnson, director, product management, Mitchell 1, said if the vehicle is transmitting any data, it can go to a fleet management system which can evaluate the data and the fleet manager can run that data through a service that identifies the fault code.
“We know what generally causes this code and we know the severity of that code so now the first decision is whether the truck still safe to drive. Many times, they are [safe to drive] and you can direct them to finish their payload and then we’ll schedule an appointment to have them in. If that’s not the case, you have to make a decision based on what the data is telling you. Is it such a problem that is going to take that truck down for an extended amount of time? If so, you might want to start logistically thinking about another tractor to meet this payload to make sure the payload gets to where it needs to,” Johnson said.
He said there are a number of good parts distributors in the heavy-duty industry, but out of the 30-some electronic parts catalog interfaces Mitchell 1 has, very few of them have a robust selection of heavy-duty parts in them, which means there are phone calls to be made to find the people who can get the parts.
A more efficient way is to quickly be able to find those needed parts via analysis of data coming from the vehicle and being able to look into the supply chain to locate the parts. It might be with a local business or some Midwest distribution center, for example.
“Where does it physically reside so I can figure out the best way to get it onto the vehicle. To me that’s the missing link. We’ve invested a lot in AI and [tools] that analyze repair orders, the history of the vehicle, etc., so we can help predict the future,” Johnson added.
Kar addressed the demand side first.
“Yes, all that information is available but is it important to a fleet manager or a warehouse distributor today? The challenge in the industry is there’s too much data and too little information. We’re working with the data. The end user only wants mission-critical information that is actionable,” he said. “From a parts warehouse distributor perspective, it is that information that this part is required to be delivered at a particular shop at this time. If we can make it happen, we can create value in the process.”
The two panelists agreed the time is now for parts distributors and suppliers to invest in this technology.
“If you’re not interested in jumping in now, you will probably be selling or going out of business later. I don’t want to sound that stark but that’s what I believe to be the reality. Fleets that are responsible for getting products here to there are going to be counting on parts distribution, service networks, people who can make sure they can help meet commitments, and if you’re not one of those on that list, I don’t know what your future holds,” Johnson said.
Kar added, “We’ve been trying for the longest time to push this technology but now the time has come where the market is pulling it because systems are getting sensorized. The moment you put a sensor in a vehicle system, what ends up happening is that system now generates data. Once that data is available, it will drive decision making. If you do not join this information revolution, you will be left out.”
So, the question becomes how long before telematics, data and predictive maintenance come to full fruition for the heavy-duty industry.
“The predictive maintenance is as good as the data driving the analysis. You have to structure the data so you can work with it. There is data coming in from various vehicle systems, components and modules, but the data is unstructured, it needs to be scrubbed. Once you have a massive data ‘lake’ that’s deep and wide, then you can add more confidence to the analytics. This change is happening, and I can venture a prediction this will happen in the next five years where every sensorized vehicle will start offering creative maintenance solutions,” Kar said.
Johnson agreed. “The information is there. The problem is they’re all in ponds and not in a great data lake. Until we can get more of a mass of that information, the better it becomes. But the capabilities are here today. It’s just a matter of [cleaning] and outputting that information. It’s a pretty exciting time.”
