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A digital twin technology is one that creates a virtual replication of a real-world entity, like a plane, manufacturing plant, or supply chain.
Manufacturing companies have increasingly used digital twin technologies to accelerate digital transformation initiatives for product development, and the tech has grown in popularity over the past five years as legacy manufacturers look for ways to keep up with innovative startups like Tesla.
The idea has been around since 2002, when it was coined by Michael Grieves, then a professor at the University of Detroit, to describe a new way of thinking about coordinating product lifecycle management. The concept stumbled along for many years, owing to limits around integrating processes and data across engineering, manufacturing, and quality teams. But it has begun picking up steam, thanks to improvements in data integration, AI, and the internet of things, which extend the benefits of digital transformation efforts into the physical world.
In 2019, Gartner suggested that 75% of organizations would be implementing digital twins within the next year. This year, Accenture has positioned digital twins as one of the top five strategic technology trends to watch in 2021. The reason is that businesses are finally figuring out how to scale these projects across a fleet of projects, rather than a single one-off, Accenture Technology Labs managing director Michael Biltz said.
The promise of digital twins lies in improving collaboration and workflows across different types of groups — like product design, sales, and maintenance teams — and engineering disciplines. When it’s done well, it can deliver fantastic results. For example, the U.S. Airforce has made extensive use of digital twins to design and build a new aircraft prototype in a little over a year, a process that traditionally drags on for decades.
In other industries, the same principles can translate to accelerating vehicle electrification, lowering construction costs, and building smart cities. Chevron expects to save millions of dollars using digital twins to predict maintenance problems more quickly. Kaeser, which makes compressed air equipment, has been using digital twins to shift from a product model to a subscription model.
Accenture worked with Unilever to build a digital twin of one of its factories. The digital twin allowed different experts to analyze various trade-offs in fine-tuning the factory while minimizing the risk of new problems. They were able to reduce electricity costs and increase productivity. Despite these early gains, many of these successes have been within a limited domain constrained by the technology platforms or systems integrators.
The core idea behind digital twins emerged in the product lifecycle management for streamlining product development. But then other industries realized some of the same ideas were applicable. Gartner has characterized different types of digital twins for areas like product development, manufacturing, supply chains, organizations, and people.
Although the digital models themselves are getting better, figuring out how to share models across applications is a bit trickier.
Different types of applications optimize the data collection process and the data models for specific use cases. PLM vendors like Siemens, PTC, and Dassault have been buying up and building out rich ecosystems of tools that facilitate the exchange of digital twin data across the product lifecycle.
These kinds of tools work well when enterprises buy tools from one vendor, but passing digital twin models between apps from different vendors leads to less integration.
Various standards groups have been working to help streamline this process. The International Standards Organization has been working on developing a variety of standards for digital twin manufacturing, reducing data loss during exchanges, and promoting business collaboration.
Michael Finocchiaro, senior technologist at digital transformation consultancy Percall Group, said, “I think that there is a big dependency on the PLM vendors to implement these standards so that they are brought into the DNA of how we develop digital twins.” As the big PLM systems — such as Dassault’s 3DEXPERIENCE, PTC’s Windchill, and Siemen’s Teamcenter — adopt these standards, they will become easier to deploy in the real world.
But the jury is still out on how committed vendors are to ensuring interoperability in practice. For example, Finocchiaro said that integrating bill-of-material data across platforms often requires extensive customization despite the existence of standards.
“This exposes the gap between the rhetoric of openness of these platforms as they seek to maintain and expand their customer base occasionally by vendor lock-in,” Finocchiaro said. This natural tendency puts a bit of drag into the adoption of standards. Scaling these efforts will require better integration and improved communications across stakeholders about how digital twins are supposed to work in practice.
Industry collaborations like the Object Management Group’s (OMG) Digital Twin Consortium could help. Digital Twin Consortium CTO Dan Isaacs said, “While there is a lot more work to be done to enable digital twin interoperability, integration and standards that can support composability, sharing, and common practices will provide a foundation.”
The OMG has previously spearheaded widely adopted standards like CORBA for business architectures and BPMN for diagramming business processes. The Digital Twin Consortium includes industry leaders such as Microsoft, Dell, GE Digital, Autodesk, and Lendlease, one of the world’s largest land developers.
The group is focusing on creating consistency in the vocabulary, architecture, security, and interoperability of digital twin technology. It does not develop standards directly, but instead helps the different participants flesh out the requirements that will inform standards by organizations like ISO, the IEC, and the OMG.
For example, the Digital Twin Consortium recently announced an alliance with FIWARE, an open source community that curates various digital twin reference components for smart cities, industry, agriculture, and energy. The hope is that this partnership could jumpstart digital twin deployments in the same way the internet grew on the back of TCP/IP reference implementations. This will make it easier to connect multiple digital twins to help model cities, large businesses, or even the world.
“Digital transformation at full scale is still in early adoption,” Isaacs said. “Digital twins continue to gain momentum, but realizing their full potential will require seamless integration, alignment, and best practices for both software and hardware infrastructures.” This will require coordination across a wide range of technologies, such as AI/ML, modeling and simulation, IoT frameworks, and industry-specific data and communications protocols.
In practice, this might look like extending the success of geographical information system interoperability into other domains. These efforts are already extending the use of satellite imagery and point cloud scanning coupled with AI and ML to identify structures and anomalies that can then be tagged and associated to other assets or attributes. This helps enterprise teams improve pattern identification to unlock critical insights needed to gain a competitive edge.
Isaac expects to see the greatest adoption of digital twin technology in energy and utilities to accelerate the transition to renewables and achieve net-zero emissions. Other areas, like medical and health care, are also gaining momentum but face challenges harmonizing digital twins across a mishmash of different systems.
Visionary leaders who work out the kinks to scaling digital twins may see a significant competitive advantage. Accenture’s Technology Vision 2021 report predicted, “The businesses that start today, building intelligent twins of their assets and piecing together their first mirrored environments, will be the ones that push industries, and the world, toward a more agile and intelligent future.”
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