OEM (Original Equipment Manufacturer) telematics systems combine hardware like sensors and GPS, and communication modules, with software to collect, analyse and transmit data related to the vehicle in question, such as its location and how well parts are performing. A common application for an OEM telematics system includes the real-time location tracking of the vehicle.
A market research report from Berg Insight released a couple of months ago reported that the number of two-wheeler OEM telematics systems are expected to increase from 11.1 million in 2023 to 53.8 million in 2028; representing a significant rise. And in 2022, subscribers of telematics services hit 200 million plus in 2022.
In the announcement, Martin Cederqvist, Senior Analyst at Berg Insight, said: “Most major motorcycle OEMs have started to consider including embedded telematics systems. Important drivers for telematics adoption include safety and security services such as emergency and roadside assistance services and stolen vehicle tracking solutions.”
The integration of IoT in OEM telematics systems, which have long been used in the automotive industry, has often meant vehicles are equipped with sensors to collect vast amounts of data from vehicle parts like the engine, brakes and tyres. This data is then sent to Cloud platforms to extract insights.
Predictive maintenance
One capability driving the growth of OEM telematics systems is the predictive maintenance aspect. Predictive maintenance uses a combination of data analytics, machine learning and AI to monitor vehicle parts and be able to, based on this information, predict when they may be likely to fail and consequently trigger a repair. This represents a shift from reactive maintenance where repairs are only carried out after a failure.
The advantages of predictive maintenance include reduced downtime, cost savings, extended vehicle lifespan and improved customer satisfaction. For obvious reasons, drivers who can be notified of a potential problem and are able to schedule in a timely repair will be far more satisfied with their level of service than a driver who encounters an issue and has to take it into a garage for repair.
Although spoken about in the context of smart factories, in a recent IoT Unplugged podcast episode, Rachel Johnson, Principal Product Manager at MathWorks shared more general insights about predictive maintenance that demonstrated the advantages of deploying it. “This [predictive maintenance] is all about preventing unexpected failures and being able to investigate problems earlier,” she said.
AI was spoken about as a tool to be deployed to analyse data. Johnson said: “There’s the issue of not having enough data, or not having collected it in the right way. [This] is easily the biggest blocker for companies on this path right now. Even if they have enough data, being able to understand what it means and how to define what anomalies look like is the problem they’re trying to solve.”
An example of an OEM telematics system in practice was demonstrated by Garmin Automotive when it showcased its OEM cabin solutions which included HMI customisation; linking wireless devices with and without Ultra-wideband (UWB); a voice assistant; and spatial audio technology.
Challenges involved in OEM telematics systems
Telematics systems collect large amounts of data which contain sensitive information about vehicle location and vehicle diagnostics. This can make them a target of cyber attacks, and the source of concern for consumers who are conscious of how their data is stored and used, an overall consciousness that could be attributed to the impact of the implementation of the GDPR Act which came into force in 2018.
Interoperability could be a consideration and subsequent challenge as vehicles from different manufacturers may not use the same telematics systems. For mixed fleet maintenance, this can make data sharing and communication challenging. Fleet operators using older vehicles or legacy management systems may struggle to integrate new telematics technology.
Other challenges include the initial investment involved in developing and deploying OEM telematics systems; the ongoing maintenance involved; user adoption and acceptance and the connectivity required for systems to function.
Closing comments
OEM telematics is anticipated to be a market that will continue to grow, thanks to the multiple capabilities it offers; whether this is for an individual driver or a fleet operator who will benefit majorly from the collection of data. Predictive maintenance is one advantage brought by OEM telematics systems that needs to be managed accordingly using tools like AI to draw meaningful insights from the data.
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