BriefCASE: Twice as nice - The impact of digital twins in the automotive industry

In the era of Industry 4.0, smart manufacturing is transforming the automotive landscape through the integration of digital twins and AI. These virtual replicas of physical assets are accelerating vehicle development, enhancing operational efficiency and driving innovation, making them essential for manufacturers aiming to stay competitive in a rapidly evolving market.

By integrating real-time and historical data with engineering, simulation and machine learning models, digital twins (DTs) offer valuable insights into asset performance and behavior. As vehicle digitization accelerates, the adoption of DTs is poised to surge. In the third quarter of 2024, S&P Global Mobility engaged with key players in the DT market, including IBM, Ansys, ABB Robotics, rFpro, Digiflec, PTC and NVIDIA. Their insights highlight the market's dynamic nature and the potential for groundbreaking advancements.

Original equipment manufacturers are increasingly using DTs to enhance vehicle development and production. For example, Ford employs DTs to create virtual prototypes, optimizing aerodynamics and structural integrity during the design phase. BMW uses the technology in its manufacturing plants, leading to improved workflows and reduced downtime. Mercedes-Benz leverages NVIDIA Omniverse to enhance assembly design and operations through DTs. Collaborations such as between Siemens and Intel Corp. aim to advance digitalization and sustainability in microelectronics, while EDAG Engineering partners with Bosch Engineering to combine expertise in DTs and smart factories for customized engineering services.

DTs also facilitate predictive maintenance by monitoring vehicle component health in real time. General Motors uses DTs to track performance and anticipate maintenance needs, ultimately enhancing reliability and customer satisfaction. DTs also optimize supply chain logistics; for example, Toyota employs them to improve visibility and responsiveness to market changes.

DTs integrate data throughout the product development lifecycle, helping to avoid costly mistakes. They create precise replicas of plant assets and supply chain locations, enabling efficient modeling of deals and transportation paths. In autonomous vehicle development, DTs simulate real-world driving conditions. For instance, Waymo uses them to refine self-driving technology, while Valeo and Applied Intuition are developing a platform for advanced driver assistance systems.

In addition, DTs enhance customer insights by analyzing vehicle interactions, allowing manufacturers to tailor offerings and make informed decisions on product features. Simulating customer experiences helps identify potential issues early, enabling proactive solutions and boosting satisfaction. Ensuring functional safety and cybersecurity in the automotive sector is vital due to increasing vehicle complexity. With each new generation containing more code, DTs facilitate reliable testing of all components, from ignition timing to touchscreen interactions.

DTs also support sustainability in automotive manufacturing by enabling remote monitoring and predictive maintenance, reducing waste and optimizing resource use. They facilitate remote control of production processes, minimizing environmental impacts, and help identify inefficiencies for cost reduction. Continuous data analysis allows manufacturers to target improvements and implement changes that lower costs.

While DTs promise to seamlessly integrate the digital and physical worlds, challenges remain. Creating virtual representations can be time-consuming and requires careful planning. Although some view DTs as the Next Big Thing, they risk diverting focus from core workflows, necessitating constant verification to ensure simulations align with reality. Moreover, complex simulations demand substantial computing power and investment.

Michele Del Mondo, global adviser for automotive in the commercial excellence division at PTC, emphasizes that the primary challenges in implementing DT technology lie in aligning the fidelity and aspects of the twin with the appropriate use cases and expected business value, especially given the high initial investment required. Del Mondo said: "The common challenges often involve isolated and unconnected legacy systems and processes. Effective data management and integration, along with addressing security and privacy concerns, are crucial when implementing DT technology."

As DTs become integral to automotive manufacturing, they streamline operations, enhance customer experiences and support sustainability goals. Hans Windpassinger, principal client engagement at IBM Technology, said DTs deliver significant value when organizational silos are dismantled and relevant enterprise data is integrated. "Openness is the key ingredient to making DTs successful: an open collaboration across different disciplines; the ability to rapidly integrate new data sources; and the capability to merge various technologies.

"This might seem like a time-consuming exercise in enterprise transformation and system integration, but the advice is to start small and generate initial value first. Identify business areas where digital twins can provide insights or expedite processes, and combine previously isolated data."

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