Hi, you're watching 365 Data Science Use Cases, a great initiative to highlight excellent
use cases for data across all different industries. Hi, I'm Jen. Today, I'm going to be sharing
one of my favorite data science use cases. I've worked in analytics for 15 years and
currently run my own analytics agency, as well as teaching other people about analytics
and how to get into analytics jobs. Check out my channel, The Career Force, which you'll
find up in the right corner or down in the description of this video.
There are so many great use cases for data. One of my personal favorites is predictive
vehicle maintenance. A lot of people don't realize just how much current vehicles are
driven by software and driven by data. And I'm not talking about just electric vehicles
or self-driving vehicles. Just the typical vehicle on the road that's been made even
in the last 10 years has hundreds of thousands of lines of code behind the scenes that are
helping power it, control its emissions and monitor what's happening with the vehicle
for safety, emissions and multiple other reasons. When it comes to commercial vehicles that
are transporting all of our goods behind the scenes, think those tractor trailers you might
see on the highway, a vehicle having a problem is a much bigger deal than if your car won't
start in the morning. That might be inconvenient for you, but a commercial vehicle, a tractor
trailer, that is broken down won't be able to deliver things. They might hold up multiple
stages of the delivery process. And we saw some of this when we had breakdowns earlier
this year when stores couldn't get supplies and many places were out of stock. There were
production issues, but there were also delivery issues and getting things moved quickly enough
to the right places. For many years, on both the consumer and the
commercial level, vehicles have come equipped with some sort of telematic systems. Telematic
systems or vehicle tracking devices that allow us to read, store and communicate telemetry
data. These telematic systems capture information like location, speed, fuel consumption, and
they also capture information about all the sensors on the vehicle and what sort of readings
they're having, if there is any sort of fault with a sensor and specifically what the fault
or the issue is. In the past this information was really treated
as a retroactive supporting diagnostic tool in a vehicle. And specifically, let's talk
about commercial vehicles. So, in the past, let's say 10 years ago, if a truck was having
an issue, a tractor was having an issue, they would go into their local dealership or a
dealership that's nearby where they are. They would wait in line. Sometimes they'd wait
multiple days to be seen. There would be diagnostics, which could take many hours if there is a
complicated issue. And the telemetry data was a supporting factor, it was one of the
clues that were used to help diagnose what was happening. Then, once that diagnostic
was made, the repair could be identified and the truck could be fixed and back on its way,
or it could be identified. And then parts need to be shipped because the dealership
doesn't have them in stock. They need to wait longer and maybe a week or two later – the
truck is fixed. Maybe this doesn't seem like that big of a
deal when you're talking about a big fleet of trucks, maybe somebody like FedEx who has
tens of thousands of vehicles operating at any time. But it's also a very carefully orchestrated
process to make sure that every vehicle is in use as much as it can be. And things are
organized in a way that the time frames flow well, everything gets delivered on time.
And many trucks are also operated by small fleets. They might have a dozen trucks or
less or individual owner operators where they have one vehicle and that one vehicle is their
entire livelihood. So not being able to do your job, not have any money coming in for
two weeks because there's an issue is a massive problem.
This doesn't have to be entirely reactive, though, and this is where that data use case
really starts to be impactful. Instead of just focusing on what's happened in the past
as a way to support a repair, right now we can read this information remotely off of
the vehicle. We can see what different issues there are, if the vehicle has been running
in conditions that may be creating an issue where there's a high engine speed or a high
load on the vehicle, that can typically point to earlier failure of a part than what a typical
usage would suggest should happen. By combining all of this remotely read telematics
data, along with other data we have about what vehicles have experienced issues and
how we've repaired them in the past, we can actually start predicting what's going to
happen and get ahead of it so that instead of the inconvenience of being broken down
on the side of the road, instead, someone is able to contact the driver, contact the
company and say, you're having an issue, you have a little bit of time, but we want to
get you scheduled to get this fixed. We use machine learning to be able to do this prediction.
What factors typically lead up to a specific type of failure and primarily focused on failures
that leave the truck inoperable that can't work or it won't run at an acceptable speed
until it gets repaired. Then, we can take that information. And here's almost an entirely
separate data use case in itself. We can look at all of the dealerships in the area that
the vehicle is or is traveling to. We can look at their stock of different parts and
identify how busy they are. So that not only is the driver contacted and
told you have this issue that needs to get fixed, but they proactively know what the
issue is. They're given an appointment at a dealership along the way that is available
to look at that truck right away. And they make sure that that dealership has the parts
necessary to do the work. So, a repair that in the past, could have taken several days
if the part wasn't in stock and the diagnostics were difficult, now can be done in the matter
of a few hours. That's still inconvenient, but it's a massive shift forward and it's
a much less inconvenient problem than in the past.
Predictive vehicle maintenance isn't just convenient. It can really save millions of
dollars, especially for larger companies over even relatively short time periods. And even
one truck with one problem can cost thousands of dollars beyond the actual repair cost because
most companies have delivery time frame agreements in place, where if the delivery window is
missed, there are penalties to pay. With data, these problems can be largely avoided and
anticipated and really make the process smoother for everyone. Thanks for sticking around to
the end of this video. Be sure to check out my channel for more analytics insights and
analytics career help. And please subscribe to 356 Data Science to get more great use
cases like this one.