Within the automotive industry, demand for metaverse solutions is rising at a compound annual growth rate of 40 percent. The sector has an estimated market value of $1.4 billion to $5 billion.
The automotive industry is already using the metaverse to optimize its supply chain from product design to procurement, from manufacturing to inventory and into the dealers' and auto shops' sales and aftersales service.
The metaverse is a network of shared interactive, immersive, digital twins of the physical world.
These new digital technologies are improving the design, research and development phases by sharing real-life customer-driven preference data from cars and the auto mechanics servicing and maintaining them.
All of it is weaved together using reliable, robust integration of multiple software and hardware systems. Costs are driven down, sustainability is rising, and customer engagement is being transformed.
From design the dealership
Today, companies such as BMW use 3D computer-aided design (CAD) drawings in scalable, real-time simulated worlds when styling a car.
AI-generated data about car use, driving behavior, and feature preferences are incorporated into the design.
Diagnostic and repair data uploaded by technicians from the body shop floor informs the R&D teams on how to re-design or improve parts repurposed from existing vehicles. (Ford uses the same chassis on sedans and trucks.)
New configurations, materials and components are analyzed in real-time and incorporated into the 3D CAD design, which will become the digital twin of the finished car.
Keeping cars on the road
Once the customer has bought a car, sensors tracking engine performance to battery performance provide data to support regular servicing, maintenance and repair.
Using historical service data, internet of things (IoT) data and technical manuals for repair and maintenance, auto shops can now use predictive maintenance to keep the car on the road with mechanics replacing parts before they break down.
The metaverse also enables repair data from dealerships and auto shops to be sent to the manufacturer's aftersales and development teams in real-time. This data can be quickly incorporated into the car's digital twin. The collective power of the data sent takes car maintenance, repair and aftersales development to a whole new level of integration.
Consider, for example, if an automaker finds tiny little bushings on the rear axle that are continually failing. Each time it's a different bushing but from the same car part.
Technicians report the fault and send images. As the data comes in, the manufacturer's development team can look at the different cracks and identify if there is a single core problem common to all the faults. A solution can be found and fed back to the technicians enabling them to fix the problem faster.
Data-driven insights
Consider also if the AI highlights that the fault is reported mainly from cities such as London, Sydney and Toronto. Car performance varies from urban to rural and mountainous areas and depending on ambient conditions. For example, there is significant variation in braking performance and wear and tear from the Middle East to Norway.
That insight leads the aftersales and development teams to realize the failure is probably caused by speed bumps and potholes on city roads, rather than the car being faulty. The development team can then test the theory and design a solution in the metaverse, using the new data in the 3D digital twin rather than physically testing vehicles on the road. Any insights gained can be used to design the next car model.
In a virtuous circle, the new diagnostic and repair data is sent to repair technicians' workpads. With passenger cars averaging 30,000 parts, and 2,000 different configurations of each model, the technicians need access to this real-time information on their laptops or computer pads as they repair the vehicles.
