The rise of connected cars, autonomous vehicles and digital factories is rapidly evolving the automotive industry. As automakers strive to quickly incorporate those advances into their products, they’ve launched a technological “arms race” to avoid being left behind and eating the proverbial dust of those who secure the lead positions.
Data and software have become vital elements of manufacturing and extend well beyond production of the core car. There is data inside the car, outside the car, from drivers and passengers — it’s everywhere. Because of the importance of collecting data, applying artificial intelligence (AI), analyzing the data and then learning from it, analytics is playing a key role in shaping the next generation of automobiles. Yet how do we effectively manage it all, how do we extract the insights and how do we supply those insights to the AI?
Analytics is helping automakers answer those questions — and making a huge impact on the industry — in four important ways:
1. Autonomous vehicles
Today, most automakers are at autonomous driving levels 2 (partial automation) or 3 (conditional automation) and speeding toward level 5 (full automation).
As the vehicles become smarter, the challenge for designers is how to collect, store and process staggering amounts of data. To achieve these new capabilities, manufacturers should employ AI, data management and digital workflows while speeding up testing and simulation in the product development life cycle.
2. Predictive maintenance
Manufacturing shop floors are full of equipment with sensors generating data that can provide critical insights. Predictive maintenance is about predicting failure before it occurs based on the data collected from those sensors. Equipment maintenance is a very planned process, but unpredicted maintenance — something breaking when it’s not planned for — causes havoc up and down the supply chain. The ability to predict failures before they occur, and then proactively plan to address them, is a huge win for the manufacturer.
Predictive maintenance is also providing a key advantage for companies managing fleets of vehicles for ride- and car-sharing services by improving the overall performance of the fleet, reducing maintenance costs and improving the customer experience. Analytics, AI and machine learning are applied to vehicle telemetry data to detect changes in the vehicle’s subsystem, correlate that data, extract the intelligence and then predict and prevent failures. Acting on these insights can dramatically reduce vehicle downtime and keep operations running smoothly.
3. Location intelligence
In manufacturing, location intelligence gives automakers the ability to leverage location information off a part or a fully assembled product, then add intelligence to it. Cars are made from different sub-assemblies, which are made from different parts. It is important to know the physical location of these sub-assemblies and parts at any moment to inform the supply chain and make sure there’s a coherent feeding of the right sub-assemblies and the right parts to the right assembly line. Location intelligence lets staff monitor part locations in real time based on RFID and sensor data.
Location intelligence is also about ensuring visibility and consistency to diagnose and predict problems before they become major issues. For example, if there’s a disruption at a plant because of a tornado or other weather-related event, companies can make supply chain decisions in real time.
All of this intelligence helps keep the supply chain humming by preventing disruptions.
4. Customer analytics
There is also a ton of data out there in terms of the customer. Customer analytics is more than just understanding the preferences, sentiments and experiences the customer is having and has had. It spans across multiple channels — the customer could be interacting with the brand online, checking out new products or price shopping. Or customers could be visiting a dealership, doing test drives and interacting with service centers where they’re bringing in their cars for maintenance, repairs or warranty work. It is essential to be able to harness all of that data and leverage it in a meaningful manner.
Analytics can be used, for example, to measure whether or not a sales campaign is effective. It is important to know how customers are perceiving a campaign and then micro-target them with specific messages that would induce them to engage with the brand in a more meaningful way. Customer analytics can also be used to deal with warranty issues by detecting specific problems in a new model quickly, before people start bringing it into dealerships.
To make analytics work, auto companies should have an analytics platform in place that is focused on harnessing data and gaining insights. Standardization, agility and innovation in the production process are also key. By applying analytics successfully, companies can stay ahead of the curve and not be left behind.