In the age of the digitized, diagnostic-rich connected vehicle, it’s software the underpins nearly every operation. It presents challenges for shops who maintain those vehicles, but there are also opportunities as those shops arm themselves with a bit of technology of their own.
Where did shops pull the most information from customers and their vehicles? It’s the customer intake and checkout processes that present windows into all this new data. Steve Barram, CEO of Integrated Services Inc., says that the capabilities of newer POS and shop management software can be leveraged in this new environment.
“To me, the future of POS is all about leveraging what the data elements are and moving them into a host of other systems in real-time ways,” he says.
The foundation is being laid now for more data-driven repair and maintenance, and that tie that binds a customer’s vehicle with a world of data may be that POS interface.
A Drop in the Cloud
On one end of the information transaction is a world of data. Information from all over the world about repairs on vehicles—when it happens on a particular make or model, repair costs and procedures and other details.
Put all that together (on the cloud, perhaps), and an artificial intelligence interface can let a customer know, based on real repairs, what’s most likely to happen to their vehicle based on their mileage, past service and a world of data.
“Then the AI assistant says, ‘By the way, based on all the information about your vehicle, we want to let you know what the next two most likely events that will happen to your car form a mechanical failure standpoint are x and y,’” Barram says. “‘And they're likely to occur within this mileage range.’”
One hurdle is how substantial that database can be. Many OEMs are pushing to keep their vehicles’ data proprietary, while the aftermarket is pushing for the same access.
“They will ultimately lose the battle,” Barram says of the automakers. “It’s the same fundamental principle. They develop the systems that the cars use, and therefore they say that they own that data.”
What it may come down to, he adds, is a system that allows each individual owner to opt into the release of data.
Like Herding Cars
What if there are much fewer individual car owners? In the SAE scale for automated, or self-driving, vehicles, level 5 represents full automation with no human input. In that world, more people might be hailing short taxi trips to move rather than drive themselves.
“When you get all the way to level 5, there’s an emergent category that by the year 2050, it’s estimated that one out of three miles driven could be out of this category of vehicle,” Barram says. “And that’s the shared fleet.”
Shops might be doing business more with fleet owners than individual drivers. To Barram, this presents an opportunity for software to handle scheduling based on more data: What the service predictions suggest, where vehicles are, when they have downtime and more.
In that sense, a shop owner might rely on convenience and availability. Quick lubes are especially well-placed to provide these services, he says. They're open more often, they're positioned in more places and they specialized in preventative maintenance.
“When you’re ready to have those services, just let us know,” Barram says. “So it begins to be a dialogue because that AI assistant has access to all the data.”
As Barram and his colleagues consider these data scenarios, he says that it’s important to lay the foundation for software groupthink. Ultimately, if an AI system is working as it’s envisioned, the data will propagate itself and it will be up to the software company to produce the interface for shop owners.
“We will become a consumer of our own API technology,” Barram says. “So we take in the information, we subscribe to it, and we then do the integration so that we’re helping produce and deliver the solution to the customer base.”