Generative AI and digital twin technology are giving new meaning to the word innovation. They are changing how the automotive industry designs, develops, and launches vehicles. No, it’s no longer safe to upgrade a few machines and protocols. It’s time for a complete overhaul. Digital twins are exact copies of the physical product or environment but are virtual. These produce sophisticated simulations and are changing how people interact with automobiles.
This dynamic duo is driving the giants towards precision and unprecedented success. This is what drives Mercedes-Benz’s marketing strategies and Ford’s employee training programs. However, this is just the tip of the iceberg. The true potential of this technology extends beyond our expectations or experience. Sounds exciting? Fasten your seatbelt as you go through the lane of this powerful combination and how it’s leveling the playing field for others.
Digital Twins in Automotive Engineering
Through digital twin technology, vehicle manufacturers can create a simulation of what the end product might look like. This simulation helps through different stages of manufacturing, from early-stage design to manufacturing optimization. The virtual prototype helps designers and developers to test and refine the design and reduce costly mistakes.
After the initial screening, it’s time to test the design under different conditions or duress. The digital twin will show the engine’s performance and the vehicle’s fuel efficiency. Manufacturers can check other parameters, such as aerodynamics, etc., and if the design has some faults.
The virtual replica then undergoes crash tests to check design stability and maintain compliance with standard regulations. If the design completes all these levels successfully, the design is ready for launch. Using this technology is a genius move because it can significantly reduce costs and downtime.
Digital Twin In Vehicle Sales & Marketing
The combination of AI and digital twins plays a significant role in marketing and sales. A manufacturing company needs data before developing and launching a vehicle. This includes current market conditions, past models and their feedback, and relevant data. They can also run a survey and collect customer data to incorporate it during the development phase.
We already know that AI can forecast market trends accurately by analyzing historical data and social sentiments. This data can guide manufacturers to align their marketing efforts with consumer interests. Mercedes-Benz is a perfect case study for understanding this concept.
In 2023, Mercedez figured that people were interested in purchasing SUVs compared to other models. Equipped with this data, the team generated personalized marketing campaigns to target this audience. Big brands like Mercedez are seizing the market cap by using digital twins in the automotive industry.
AI can augment processes like lead scoring and sending personalized follow-ups after the initial conversation. These systems can send reminders to ensure no leads are ignored or overlooked. General Motors is doing exactly that. They have created digital twins of customer interactions to identify potential prospects for their brand-new automobile.
The cycle of production and marketing does not end here, and we need to perform post-launch marketing. This technology helps these companies understand the market’s pulse, and adjust accordingly. For example, if people are saying unfavorable things about a model, the company can focus on product improvements.
Digital Twin Technology In The Driver’s Seat
The application of AI-powered digital twins is not restricted to manufacturing or sales, it can help cab drivers too. This technology can create a digital replica of any data, including traffic, road conditions, and imminent weather. Using this data, they can optimize the route, avoid congestion, and make adjustments on the fly.
Similarly, digital twin technology can generate virtual profiles of customers, and AI can analyze their behavior. Cab drivers may use this data to offer a personalized experience to passengers. This includes playing their favorite music or setting a suitable temperature, i.e., personalizing the experience.
They can use the technology to perform predictive vehicle maintenance to prevent unfortunate breakdowns. This improves vehicle reliability, performance, and safety while reducing downtime.
Aside from these, this combination can be used for performance monitoring and providing employee training. Digital twins can capture data from sensors to understand insights into the vehicle’s production data and maintenance. If it is deployed from the beginning, they can avoid discrepancies and resolve them. In some cases, they can benefit the team during supply chain optimization.
Ford Motors is a first-mover when it comes to deploying different technologies to train its employees. The brand has used AI, digital twin technology, and VR during training programs to increase operational effectiveness and promote learning and growth.
Related Article: How Are M2M Payments Changing Automotive Finance
5 Steps to Create a Digital Twin For Customers
You have decided to create a digital twin to emulate consumer behavior. Congratulations, you are already a step ahead of your competitors. But you need a detailed blueprint to roll out this technology.
What is the scope of this project?
- Will it help with predictive maintenance?
- Do you need a digital twin for an assembly line or a specific model?
- What type of customer data do you need?
It is necessary to predetermine the objective of creating this twin; otherwise, you may get lost in the sea of information.
Data collection
You can get relevant data through the sensors present in the vehicle, connected devices, and other data collection channels. If the goal is to check car performance, understanding parameters like engine performance, and fuel efficiency might be beneficial. If you want to understand trends and historical data, you can try social listening to gather feedback about the current model.
It’s time for 3D
Using all the collected data, a 3D digital twin can now be created to simulate customer behavior. Using AI and ML, you can work on the specifications and test the design in real time. When you need to see the performance of a particular design, you can create a 3D model of the engines, transmission, and suspension.
Testing
After developing the digital twin, it is important to test it against actual customers to see if the replica is behaving accordingly. If the twin is not behaving as per the market trend or expectation, we need to modify the data or make other adjustments.
Implementation
Now it’s time to implement the digital twin technology, either on behalf of drivers’ or the company’s. If it is implemented for drivers, it could be used to customize vehicle settings. Companies can use the digital twin to personalize marketing and sales efforts and offer product recommendations based on consumer behavior and preferences.
Continuous updates
People’s expectations and behavior change with time, and so do market trends. A company can learn those trends by feeding real-time data to the digital twin. When it is continuously updated, it's more likely to mimic customers and their behavior.
Monitor
At last, it is important to monitor the performance of digital twin technology and see how it performs. Are people satisfied with your business’s service/ product quality? The twin is doing its job. Do they have any complaints? You can modify the town and adjust accordingly.
Challenges of Building A Digital Customer Twin
Expecting Human-like Behavior
Is this supposed to mimic real customer behavior? Yes. Should we expect the technology to possess human cognition and empathy? Maybe not for a couple of decades. To this day, AI is struggling to understand nuance and how to respond like a human. What seems humane to someone might not work for another customer. This gap can be frustrating, and businesses must dial down their expectations.
No control over data
The lack of control over data presents a significant challenge in building accurate digital customer twins. Data often resides in disparate systems, some of which may be outside the organization's direct control. This fragmentation can lead to incomplete or inconsistent information, hampering the twin's accuracy. Additionally, data privacy regulations and user consent issues can limit access to certain types of information.
Security Issues
These virtual representations contain sensitive personal information, making them attractive targets for cybercriminals. Moreover, the interconnected nature of digital twins with various systems increases the attack surface, potentially compromising not just customer data but also business operations.
Generative AI and digital twin technology create an array of opportunities for the automotive industry. This includes accelerated product designing, predictive vehicular maintenance, and personalizing marketing and sales strategies. The digital twin can be a replica of anything, from the design to the customer persona. Using the design copy, you can see if there are any defects in the model.
Also, you can test if it can withstand safety testing protocols and come out with flying colors. The digital customer twin is another jackpot if you know how to use it effectively. It can be used for social listening and understanding customer behavior. After understanding the behavior, you can develop personalized services.
Are you ready to embrace this digital transformation? At Chiprn IT, we employ a tailored and innovative approach to maximize ROI from this investment. We focus on developing impactful solutions that will only boost the industry’s operational excellence.
