How AI can help lower repair costs, downtime

Matt Cole:

Trucking companies' repair and maintenance costs are soaring, but artificial intelligence and automation can help solve some of Fleet's challenges.

Jason Cannon:

Hey everybody. Welcome back. I'm Jason Cannon and my co-host is Matt Colt. The cost of maintaining and repairing a fleet just keeps going up. Parts prices, technician shortages, shop rates, and increasingly advanced equipment are all driving up costs. Telematics provider Motive recently published a report based on a fleet survey to help better understand how companies are dealing with those challenges.

Matt Cole:

The results reveal a gap in what companies know are their challenges and having the systems in place to solve them. Motive's report looks at how integrating AI-powered insights and particularly having those tools in one platform could be a game changer.

Sri Kolluri:

I think the report basically started with this whole, we keep talking to our customers and what we consistently hear is that maintenance is actually becoming one of the top most priorities for them, especially with the current economy with rising repair costs. And that made us also dig deeper to understand what are the gaps in the fleet maintenance world, both in terms of how are they managing their downtime risk and how are they operating across the technology stack and do they have the right level of visibility into their operating costs? So basically try to understand that entire piece of the world and in general, how well is this industry adopting AI? So that's basically where this whole research came up with.

Jason Cannon: 

While the cost of maintaining repair equipment continues to rise, maybe even the bigger issue is the cost of downtime caused by unexpected breakdown.

Sri Kolluri:

I think the biggest one is actually the cost of downtime and the cost of the unexpected breakdowns. To look at the stats, the cost of an unexpected repair is eight to nine times a planned maintenance. And there's also the other cost, the loss of productivity. So when you combine both of this, it's almost $760 per day is your cost of downtime. And that if you extrapolate for a fleet of thousand vehicles, that almost comes down to $4 million per year. That's a huge amount of cost that they're spending purely on downtime. And that's one major factor. The other major factor is in general, I think the technology stack today is scattered. So users use basically different solutions for their fleet maintenance, a different provider for their ELD and GPS. So a lot of time is basically spent in just reconciling data across platforms and that's time and money lost in just managing data.

And I think the third other piece here is basically just not having visibility into cost, which means you don't know what should I actually do to reduce my overall maintenance spend.

Matt Cole:

Fleets that have systems in place to obtain data for different parts of their operation are heading in the right direction, but that data being siloed and not connected means missed opportunities.

Sri Kolluri:

The biggest challenge comes down to the data silos. And when you actually dig deeper into the data, you'll find out that only 13% of the fleets actually have connected systems that talk to each other, which means almost seven in eight fleet completely manage their data manually. So what the leads is that your ability to respond to issues becomes very slow because you're still trying to crunch the data, identify what are my most problematic vehicles. Getting just the consolidated picture of the health of your fleet takes a lot of time manually. So that slows down your ability to identify problematic assets. And then in turn, converting that into a repair order, work order and then actually performing the repairs also slows down. So this cascading impact is basically what leads to that impact that we just talked about. The way you would look at it that is once you have all the data integrated, you essentially have all the right signals.

So by managing your fleet's health proactively, identifying which of my vehicles have critical faults, did my drivers report any major defects? Just having that information consolidated will help you to identify what are my most problematic vehicles. Now once you identified the problem, then the easier part is then converting that into a workflow that allows your maintenance teams to actually execute and operate on the vehicles. So once both these pieces come together, you are able to respond fast to issues, you're able to reduce the downtime and essentially both combined together reduces that huge maintenance cost that I talked about when it comes to the reactive cost.

Jason Cannon:

So where does AI and automation come into play and how can fleets benefit from using it?

Sri Kolluri:

When you look at AI, AI can be applied across the entire workflow. So you start with actually understanding, let's say the fault codes that are coming from the vehicle. Fault codes are typically cryptic in nature. They're just simple codes. Unless you're an experienced technician, you wouldn't understand what they actually mean. So that's one place where you can actually employ AI to understand what do this actually mean? What am I supposed to act on? Is this something that I can act on immediately or can I defer it for a planned maintenance later? I think that's one use case of AI. The other piece is actually using AI automations to convert these critical fault codes or these major defects into a work order. So that way you're bridging the gap between what's happening on the road with what's happening in the shop. So imagine that a vehicle faces a critical fault code.

The moment you have automations in place, you would have a work order set for your shop and the shop knows that this vehicle is coming in for repair and I need to plan my operations around it. That allows you to respond faster. And I think the other use case of AI is basically capturing your costs. This is one of the biggest blind spots. A lot of maintenance happens outside through vendors and there's no means for customers to be able to basically capture those costs. So that's where the ability to process invoices automatically through AI helps you to capture your in-depart and labor costs, which in turn helps you to basically identify what are systems that fail often, where am I spending the most, and which geographies am I spending the most and so on. So yeah, those are some of the use cases where AI is basically helpful.

Matt Cole: 

Motive recently released a new system, Motive Maintenance, that does all of what Sri has been talking about and integrates maintenance workflows directly inside the Motive platform.

Sri Kolluri:

I think the Motive Maintenance is essentially, think of it as a AI powered solution that basically connects the maintenance workflows and the maintenance spend into the Motor platform. Motive platform today all the talks to the vehicles, equipment, the workforce and spend. This basically adds another layer to it where it now brings the shop and the data around it into the motive platform. And to just give you some concrete examples of how this would help for a fleet is that let's say I have a critical fault quote that occurred on the road, the AI automations that was built inside Motive will convert that into prioritized work order for the shop so that the shop can basically start acting on it. Now let's say the shop does act on it, you have computer repairs, you've recorded the cost, all the consolidated cost data now basically gives you visibility into your total cost of operations.

And this is where your telematics also helps. You have your fuel data, the fuel consumption data that you understand from telematics, and then you have the maintenance data that's now coming from the work orders. Combine all of that and to get a holistic view of your cost of total cost of ownership. And that in turn allows you to identify, hey, what are my problematic assets which have a poor cost per mile? How am I trending versus other fleets out there? All of that basically.

Jason Cannon:

That's it for this week's 1044. You can read more on ccjdigital.com. While you're there, sign up for our newsletter and stay up to date on the latest in trucking industry news and trends. If you have any questions or feedback, please let us know in the comments below. Don't forget to subscribe and hit the bell for notifications so you can catch us again next week.