Digital twin technology has tantalized and beguiled enterprise companies interested in simulating real-life situations and their outcomes, ultimately allowing them to make better decisions. Now the concept has a new definition, and possibly new use, for enterprises.
The updated definitions of digital twin and digital thread technology by the Digital Twin Consortium (DTC) reflect the evolving landscape of digital engineering and are intended to foster a common understanding that “bridges multiple sectors and applications over the digital twin lifecycle,” Dan Isaacs, general manager and chief technology officer of the DTC, said in a statement.
“The revised digital twin definition emphasizes synchronization and data, with a model-based approach tied to engineering technology. Grounded in physics, it supports the full life cycle from simulation to decommissioning digital twins,” said Dr. David McKee, co-chair of the DTC Capabilities and Technology Working Group and Lead Author of the DTC Definition Team. “By refining this definition, we enable more accurate, real-time representations, leading to better decisions, improved efficiencies and deeper insights across industries.”
The definitions were updated to more closely reflect language from the National Academies of Sciences, Engineering and Medicine. More importantly, they clarify what has been a muddled interpretation of the technology that scared off some enterprises, according to Ron Westfall, a research director at The Futurum Group.
“A better understanding of what digital twin technology is should open up markets in smart city management, manufacturing and other future uses in the next few years,” Westfall said in an interview. “Remember, there was debate over what AI and machine learning was. Once it was clarified, more folks adopted the technology.”
For more than a decade, the industry struggled to understand digital twin and what it could and should do, Jason Massey, co-founder and CEO of Ndustrial, said in an interview. “We were banging our hands on the table,” he said, referring to slow adoption by supply chains and large refrigeration systems for food.
Indeed, the technology is expected to advance in leaps and bounds. “What you are going to see is digital representations of humans, predicted Gil Perry, chief executive of D-ID, in an interview. “In the near future, every business person who wants to stay efficient and up-to-date will have a digital representation of themself to recap meetings, do repetitive tasks and save time. And every company will have specific tools in customer support, sales representatives have digital twins.”
“It is going to happen faster than we can imagine,” Perry said.
Digital twins is a virtual replica of a physical object, person or process that can be used to simulate its behavior to better understand how it works in real life. The concept has been used to great effect for medical and health care use, sustainability, car sketches, factory modeling, supply chain efficiencies, environmental design and urban planning.
There are four types of twins: Product twins; data twins, which link real-time data on traffic to shorten a commute a la Google Maps; systems twins, which model the interaction between physical and digital processes in manufacturing, supply chain management and store operations; and infrastructure twins that represent physical infrastructure such as a highways, buildings, or even stadiums.
“Think of imagery of patient’s vital organs for preventive health care, modeling medical data or using the technology in virtual manufacturing,” Jim Kaskade, CEO of Conversica, said in an interview. “It has huge promise.”
It all adds up to a global market that will grow about 60% annually over the next five years, reaching $73.5 billion by 2027, according to McKinsey analysis. The firm’s research further found 70% of C-suite technology executives at large enterprises are already exploring and investing in digital twins, and for good reason: Digital twin technologies, they assert, are cutting product development times 20% to 50% and slashing costs.
Gartner analysts estimate the digital twin market will reach a value of approximately $183 billion by 2031.
IDC forecasts that by 2027, 35% of G2000 companies will employ supply chain orchestration tools featuring digital-twin capabilities.
Digital twins can integrate technologies such as AI and cloud to offer businesses a transparent summary of their assets and how they work together to improve business decisions and increase efficiency.
High-profile use cases abound in the fledgling field. Mayo Clinic has used digital twins to create custom patient models for diagnostics and treatment through data for digital imaging, genetics and wearable devices. Siemens is using a virtual version of a power plant digital twin to map out its infrastructure and components like solar panels and wind turbines. BMW is collaborating with SAP To create virtual models of its active factories.
“It (digital twins) will become part of normal operations everywhere,” said Gary Survis, operating partner at Insight Partners.
But perhaps the most useful digital twins’ outcomes are to come, as organizational leaders use the technology to weigh the impact of environmental laws, climate change and consumers’ views toward a brand’s sustainability record for buying decisions.
Of course, there are pitfalls like any technology.
Digital twins require powerful new data centers in their use of AI, which translates to hikes in energy, land and water use. The technology also poses security concerns, including the concept of an evil digital twin, a malicious virtual model used to support hacks and other criminal activity.
“We need ethical, responsible players,” Perry said.