Vehicle connectivity enables suppliers and manufacturers to gather new kinds of customer-specific information. See how automotive OEMs — and construction equipment makers like Caterpillar — use connected data systems and sensors to identify product flaws and improve future design efforts.
From this week’s Tech Briefs webcast presentation, Connected Vehicles & Jobsites, a reader asks: "Do you have any examples of OEMs that successfully used engineering-level data sent from end-user machines to help them resolve issues, set customer usage profiles (based on model type or geographic location, for example), or create better tests and design requirements?"
Chad Repp, Business Development Manager, CANect Telematics, HED: I've worked with some of the biggest automotive OEMs out there. OEMs are collecting vehicle-specific information to help them identify issues within their production vehicles, and then taking that information back into their system to analyze or R&D-verify that the parts in question aren't needing to be recalled or replaced.
Typically, the majority of OEM telematic solutions, from the automotive perspective, have been cellular-based in nature so that they can collect that information or have connectivity to those vehicles pretty much anywhere within the continent or country.
Some OEMs pass the cost on to the consumer, in packages that enable consumer accessibility or value. Think about your OnStar type application or your Chrysler or Dodge-type app that allows you to see where your vehicle is, and allows you to lock/unlock the doors. Many times, the OEMs will build those consumer-based packages in a way that provides a service to the consumer but also enables them to collect some data.
There are also some OEMs that are paying directly out of their pocket for this, because they see the value in providing a better stronger, longer-lasting car for future development.
Kjeld Jespersen, Global Product Manager, Digital Services, Construction Industries, Caterpillar: At Caterpillar, we are using these systems quite a lot, in two main ways. We have a large population of machines already connected. We take a fairly limited data set from each of those machines but aggregate the data over a large population that we use for specific design purposes, to try to understand how our customers are using the machines, to make sure we design future generations of machines with a use in mind, and to not overdesign or under-design.
In addition to that, in our testing phase, and for machines that end up in specific, severe applications, we do have the possibility of adding additional sensors, such as strain gages on the frames, or pressure sensors on the hydraulics, or temperature sensors to understand the machine’s environment. We can collect that data from those specific environments to understand failure and future design needs in further depth. Those are systems that we put in our machines extensively, and we would generally pay for.
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