Digital replication Digital Twins: Why German Industry Should Invest Now

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Digital twins are already demonstrating diverse potential in the industry. Read here why our author considers them indispensable, despite some hurdles in implementation.

*Frank Scheufens works as a Product Manager for Professional Visualization at PNY.

The technological revolution through digital twins offers endless possibilities, especially in the industrial sector. They are virtual replicas of physical assets, processes, or environments that are connected and synchronized in real time. This provides the potential to revolutionize all industries and significantly increase efficiency and productivity in the industrial sector.

Previously, data within companies was largely isolated, with proprietary formats that could not be easily utilized for other purposes. Technical innovations in this area, such as Open-USD and Nvidia Omniverse, now allow all users simultaneous access to data with different applications. This enables the creation of detailed digital twins incorporating all data, including sensors, robots, and even autonomous transport vehicles.

Efficiency redefined: The role of digital twins

Digital twins are physically accurate, virtual replicas of assets, processes, or environments—live, connected, perfectly synchronized, and with realistic physics, materials, lighting, rendering, and behavior. These models are updated and synchronized in real time via continuous data streams from sensors and other IoT devices. They are both AI-capable and AI-enabling. Moreover, they ensure that intelligent devices with advanced perception, reasoning, and recommendation capabilities interact with our physical world, offering advice and making autonomous decisions based on the laws of physics. In this context, AI-enabling means that digital twins can generate synthetic data to train or enhance AI models. This is a crucial point, as the necessary data are often unavailable or insufficient to achieve high AI accuracy.

The next decade will bring transformation for companies across all industries: they will leverage the capabilities of the metaverse and advanced simulations using the latest GPUs to develop products and services or enhance customer experiences. This will also increase their speed, agility, and operational efficiency.

There are already numerous examples of how companies in the energy, manufacturing, retail, telecommunications, and transportation sectors have significantly increased their efficiency and productivity using digital twins.

Why digital twins are indispensable for the future of industry

The use of digital twins offers numerous technological and operational benefits. Companies can continuously monitor their physical systems and production environments and make data-driven decisions. Virtual testing and simulations in a copy of the digital twin allow for optimizations to be made before changes are implemented in the real world. This reduces the risk of errors and downtime. For example, the motion sequences of autonomous transport vehicles can be tested and optimized in the digital twin's test environment without disrupting ongoing production. These precise and risk-free simulations lead to more efficient operations and cost savings.

Progress or Stagnation: Challenges in Implementation

Despite the clear advantages, there are also significant challenges in the development and implementation of digital twins. One of the biggest challenges is collecting and adapting the right data. Digital twins require extensive 3D data from various sources including CAD (Computer-Aided Design), CAE (Computer-Aided Engineering), PLM (Product Lifecycle Management), MRO (Maintenance, Repair, and Overhaul), GIS (Geographic Information Systems), BIM (Building Information Modeling), robotics, analytics, and IoT (Internet of Things) systems. Harmonizing and integrating these diverse data sources is a demanding task. Often these data are stored in different formats and standards, which complicates their consolidation. This integration process not only requires robust technological solutions but also a significant degree of coordination among different departments and external partners involved. Managing this complexity effectively is key to unlocking the full potential of digital twins.

Data collection and processing require specialized knowledge and technical expertise, which may not be available in smaller companies. Advanced skills in areas such as data modeling, simulation techniques, and system integration are often necessary. Furthermore, ensuring data quality and accuracy is crucial, as inaccurate or incomplete data can lead to faulty models and simulations. For smaller companies lacking in-house expertise, collaborating with external consultants or adopting user-friendly digital twin platforms that simplify the integration and analysis process can be effective strategies. These platforms often provide guidance and tools to help manage data complexity and maintain high standards of data integrity. In addition, ongoing training and the development of a data-savvy workforce are integral to leveraging the full potential of digital twins effectively. As this technology becomes more mainstream, the availability of more comprehensive educational resources and the widespread adoption of standards will likely reduce entry barriers for smaller enterprises.

Another obstacle is the necessary IT infrastructure required to process and store large amounts of data. Companies must invest in powerful servers, network infrastructures, and data security measures to ensure the integrity and availability of data. To fully leverage the benefits of digital twins, high-performance hardware is also essential. Professional graphics processors enable everything from detailed industrial design and advanced special effects to complex scientific visualization and the integration of artificial intelligence. The Nvidia RTX 6000 Ada Lovelace is an example of graphics cards specifically developed for these applications. With its third-generation RT cores, fourth-generation Tensor cores, and CUDA cores, the RTX 6000 provides the necessary performance to accelerate computation-heavy AI workloads. These investments not only increase the capacity to handle complex simulations but also enhance the reliability and speed at which these calculations can be performed, significantly boosting the overall productivity and effectiveness of digital twin technologies.

However, the high costs and complex setup of this infrastructure can be daunting, especially for small and medium-sized enterprises.

Indeed, the implementation of digital twins requires not only technological but also cultural and organizational changes. Employees need to be trained and ready to accept new technologies and workflows. This often involves a shift in company thinking and a willingness to invest in digital transformation despite the associated uncertainties and risks. Educating staff about the benefits and potential of digital twins to improve efficiency, reduce costs, and enhance product and service quality can help foster a supportive culture for change. Furthermore, leadership must actively promote innovation and provide the necessary resources and support structures. Including employees in the planning and implementation phases can also mitigate resistance and increase acceptance as they become active participants in the transformation process. Ultimately, the successful integration of digital twins into business operations relies on a balanced approach that combines technological adoption with strategic management and cultural adaptation. This way, companies not only adapt to new technologies but also thrive, leveraging these advanced tools to their fullest potential.

These challenges highlight that the successful deployment of digital twins is not only a technological task but also a strategic and organizational one. Companies that overcome these obstacles can achieve significant operational benefits and still gain a competitive edge.

The future is promising

The general interest in digital twins is tremendous, but currently, implementation is mainly occurring in large companies that already have the appropriate personnel and financial resources. After the USA, Europe is the second-largest region that is already using digital twins or working on their implementation.

Tools like Nvidia Omniverse, which was introduced three years ago, simplify the creation of digital twins. Generative AI also plays a significant role by enabling the automatic conversion of 2D plans into 3D models. This saves a lot of time and effort and makes it easier to get started with the technology.

The success stories from companies that have already implemented digital twins are making the adoption increasingly attractive for others, leading to a growing interest. Corresponding tools and applications are continuously improving, and the creation of a digital twin is becoming easier, which will also lead to more and more companies utilizing digital twins. Companies will not be able to ignore this technology if they want to remain competitive. In the future, tasks such as product development or process optimization will primarily occur virtually, as it can be more cost-effective and resource-efficient.