Digital Twins Keep Multiplying

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Digital twins—virtual representations of real-world entities and processes—continue to make an impact at enterprises. Digital twins have emerged as a transformative force across industries. The innovative pairing of the physical and digital realms enables organizations to gain deeper insights, make informed decisions, and optimize performance throughout the lifecycle of products, assets, or entire systems. 

By harnessing real-time data, advanced analytics, and simulation technologies, digital twins offer a comprehensive understanding of the present state and predictive capabilities for future behavior. From enhancing operational efficiencies and enabling predictive maintenance to facilitating design improvements and supporting complex decision-making, the potential applications of digital twins continue to evolve, promising groundbreaking advancements.

As this technology matures, its profound impact on revolutionizing industries and redefining conventional approaches to design, production, and maintenance becomes increasingly evident. However, if it were easy, everyone would have a digital twin. Big challenges still remain for enterprises building out digital twins for their assets, large and small. 

Challenges of Digital Twins

As anyone who’s worked on enterprise digital twins can tell you, they come with challenges, including:

Complexity and Interoperability

Representing intricate systems accurately, especially those with diverse components and behaviors, poses a challenge. But, ensuring compatibility and seamless communication between different systems, devices, and software tools is essential.

Real-Time Synchronization

Keeping digital twins synchronized with their physical counterparts in real-time is challenging, especially for systems with rapid changes or dynamic environments.

Security and Privacy

Safeguarding sensitive data used in digital twins against cyber threats and ensuring compliance with privacy regulations is vital.

Data Integration and Quality

Incorporating data from various sources (sensors, CAD models, historical records) into a unified model can be complex. But data quality and ensuring the accuracy, reliability, and consistency of data inputs is crucial for an effective digital twin.

This is perhaps one of the biggest challenges enterprises face. Some are turning to a multi-model approach where all types of data for an asset are kept in a digital twin but stakeholders can access different models within that digital twin. For example, accessing different levels of detail or specific areas of the system. Ansys, Siemens, Artec 3D, and others are working on multi-model solutions and infrastructure. 

Enterprises Making Digital Twins Happen

While these challenges exist, there are enterprises making digital twins happen—in a big way. 

On a Digital Twin Consortium webinar, Grant Epling, Lockheed Martin Digital Transformation, covered various Digital Thread and Digital Twin technologies being implemented at Lockheed Martin. Use cases include:

  • Design validation

  • Factory optimization and validation

  • Operational analysis

  • Fleet maintenance planning

  • Global logistics

  • And more

Lockheed Martin has the ARGUS (analytic recursive generation unified system) digital twin that serves as a single source of data and information from multiple sources of truth. Users don’t have to hunt through multiple tools to find what they’re looking for. 

From Bosch

At Bosch, they’re using digital twins themselves and encouraging the industry to share success stories and more. In building their digital twin infrastructure, they started with just one use case and one product group and scaled from there, now with digital twins across the enterprise. 

Bosch is one of 170 members of Catena-X, a European-wide initiative to create an open data-sharing platform designed to increase visibility in the automotive supply chain and improve the flow of parts and materials. It creates uniform standards for data and information exchange and is managed on a peer-to-peer system to ensure security in the exchange of supply chain data. 

So while there are challenges to implementing digital twins at the enterprise level, there are success stories to tell. Many of the enterprises with strong digital twin ecosystems know the importance of an open structure and encourage industry-wide and cross-industry collaboration. These steps will have digital twins multiplying even faster.