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Top 5 Use Cases of Digital Twin in Automotive Industry in '24

Top 5 Use Cases of Digital Twin in Automotive Industry in '24Top 5 Use Cases of Digital Twin in Automotive Industry in '24

The rise of the fourth industrial revolution (Industry 4.0) and the increased adoption of big data is driving the need for data and data-driven manufacturing strategies. Digital twin is one of the top data-driven manufacturing concepts enabling businesses and manufacturers to simulate products to build faster, cost effective, and high quality products. Automotive sector is estimated to hold 15% of digital twin use case last year. 1

What are digital twins in automotive industry?

A digital twin in automotive industry is a virtual replica of an entire car, software, mechanics, electrics, and physical behavior of a vehicle. The digital twin holds all real-time performance, sensor and inspection data, as well as service history, configuration changes, parts replacement and warranty data.

What are the digital twin automotive use cases?

Some of the digital twin use cases in automotive industry include:

Product testing

Digital twin of a product helps in determining its quality and performance by virtually experimenting with different compounds and raw materials to improve the design, and optimize the performance of the product. For example, digital twin of a new tire can be virtually modeled and tested for different weather conditions and optimized according to the final result.

Adding manufacturing capacity

Before installing new machines for manufacturing in order to increase production, companies can leverage digital twins to simulate the impact and benefits of the new machine on the production capacity. The virtual model needs to consider features of the company’s product, material included, historical data of production time and required machinery, etc. and then provides insights about how the new machine can increase the production of this product.

Employee training

Companies can set up a factory’s infrastructure as a digital twin and train workers remotely without installing the equipment physically. For example, a manufacturing company in Europe can train their employees in Mexico on a digital twin of the factory even before final infrastructure installment in Mexico, in order to facilitate the hiring process and understand training needs of new hires.

Predictive maintenance

Digital twins of machines and manufacturing equipment can be used to determine the maintenance needs and enhance the health of production lines and factories. In this case, digital twins need to leverage real-time data extracted from IoT devices and sensors in the manufacturing process to detect fault recurrences and causes.

Sales 

One of the future implications of digital twin technology is with sales, where customers can give opinions about the products before they are released to the market. With a digital twin of the vehicle, a company can allow a potential buyer to check out the product, analyze the new features, and compare with older designs. Through 3-D visuals of cars, manufacturers can alter the features and ask feedback from their clients before producing the automobile.

Other operations

Another application of digital twin technology is to generate a digital twin of an organization (DTO) which is a digital replica of the company itself. DTO help companies understand their processes and optimize them. Since DTO leverages process data stored in documents and IT systems, it requires process mining to extract and analyze the event log data to generate a DTO.

An example of a digital twin in automotive industry
Figure 1: An application of digital twins in automotive industry, Source: MDPI 2

What are the benefits of digital twins in automotive?

Digital twins benefit overall automotive industry by:

Unifying data

Digital twin resolves the challenge of integrating data from several sources (e.g., Historical data of previous models, performance data, driver behaviors). The manufacturer can analyze and evaluate the data to derive any insight in a visual manner.

Easing verifications

The companies lose time while verifying new features or designs as they have to wait for production to determine the feasibility of their designs. A digital twin can provide a quick and reliable way of verifying design success and efficiency. It can source all the required data to run simulations that provide accurate results. 

Avoiding failures

Digital twin technology can use data to predict when downtimes of machines can occur by learn from past data, therefore, it can help businesses take steps to avoid such malfunction, which enables uninterrupted production with minimal financial loss. 

Predicting customer demands

Manufacturers collect customer experience data regarding which features are being mostly used by customers once the car is at the market. Using digital twins, manufacturers can leverage this customer experience data to get better at predicting customer demand, increasing customer experience.

The three challenges in automotive industry on vehicle concept, detailed design and design verification can be tackled down by digital twin technology.
Figure 2: How digital twins can tackle the challenges faced by automotive firms Source: TATA Whitepaper

What are the challenges of digital twins in automative industry?

Technology adoption

In the automotive industry, enormous amount of data is generated at each stage of the product life cycle of vehicles. Such big data enables building faster, cost-effective, and high quality products. Yet, automotive manufacturers have different levels of effective utilization of data, and it’s been estimated that companies analyze only 12% of the available data. Suppose an international automotive company that runs production of each stage of vehicles across the world. To generate a digital twin of the vehicle, the maturity of data adoption should be equal across different regions involved in the product life cycle.

Further reading

To learn more about digital twin technology and discover its use cases and applications in other industries, you can read our in-depth articles:

If you believe your business will benefit from a digital twin, feel free to check our data-driven list of digital twin software.

And let us help you choose the right tool for your business:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Hazal Şimşek
Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

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