What is a digital twin and why is it important to IoT? – Network World

The use of digital twins – digital representations that mimic the structure and behavior of physical objects or systems – is on the rise. Digital twin technology has moved beyond manufacturing, where it got its start, and into many other industries, driven by advances in sensor technologies, artificial intelligence and data analytics.
In the world of IT, enterprises can use digital twins to replicate their IT environments, including infrastructure, network equipment, and Internet of Things (IoT) devices, and then run simulations or what-if scenarios to test the impact of changes and to optimize performance. They can be used to validate the current state of a network, for example, and test configuration changes, firmware updates, or adjustments to security policies.
A digital twin is a digital representation of a physical object or system. In essence, a digital twin is a computer program that takes real-world data about a physical object or system as inputs and produces as outputs predictions or simulations of how that physical object or system will be affected by those inputs.
The digital twin concept first arose at NASA: full-scale mockups of early space capsules, used on the ground to mirror and diagnose problems in orbit, eventually gave way to fully digital simulations.
The technology behind digital twins has expanded to include buildings, factories and even cities, and some have argued that even people and processes can have digital twins, expanding the concept even further.
The term really took off after Gartner named digital twins as one of its top 10 strategic technology trends for 2017, saying that within three to five years, “billions of things will be represented by digital twins, a dynamic software model of a physical thing or system.” 
Today, digital twin technologies continue to gain traction because of their potential to bridge the gap between physical and virtual worlds, according to Grand View Research, which says the global digital-twin market is forecast to expand at a compound annual growth rate (CAGR) of 38% from 2023 to 2030. Incorporating technologies such as artificial intelligence (AI), cloud computing and IoT into digital twin systems is expected to boost market growth in the forecast period, Grand View says.
A digital twin begins its life being built by specialists, often experts in data science or applied mathematics. These developers research the physics that underlies the physical object or system being mimicked and use that data to develop a mathematical model that simulates the real-world original in digital space.
The twin is constructed so that it can receive input from sensors gathering data from a real-world counterpart. This allows the twin to simulate the physical object in real time, in the process offering insights into performance and potential problems. The twin could also be designed based on a prototype of its physical counterpart, in which case the twin can provide feedback as the product is refined; a twin could even serve as a prototype itself before any physical version is built.
The terms simulation and digital twin are often used interchangeably, but they are different things. A simulation is designed with a CAD system or similar platform, and can be put through its simulated paces, but may not have a one-to-one analog with a real physical object. A digital twin, by contrast, is built out of input from IoT sensors on real equipment, which means it replicates a real-world system and changes with that system over time. Simulations tend to be used during the design phase of a product’s lifecycle, trying to forecast how a future product will work, whereas a digital twin provides all parts of the business insight into how some product or system they’re already using is working now.
Potential use cases for digital twins are expansive. Objects such as aircraft engines, trains, offshore oil platforms, and turbines can be designed and tested digitally before being physically produced. These digital twins could also be used to help with maintenance operations. For example, technicians could use a digital twin to test that a proposed fix for a piece of equipment works before applying the fix.
Manufacturing is the area where rollouts of digital twins are probably the furthest along, with factories already using digital twins to simulate their processes. Automotive digital twins are made possible because cars are already fitted with telemetry sensors, but refining the technology will become more important as more autonomous vehicles hit the road. Healthcare is the sector that could produce digital twins of people; tiny sensors could send health information back to a digital twin used to monitor and predict a patient’s well-being.
Just as digital twins serve different purposes in different industries, the value of digital twins differs depending on the application.
In the world of manufacturing, for example, a digital twin can enable product designers to try out prototypes before settling on a final design. It’s a way to use digital resources to develop and refine products instead of tapping physical engineering resources. With a digital replica of a product that simulates the real thing in a virtual space, designers can rapidly generate new iterations, optimize their product designs, and improve product quality along the way.
In the semiconductor industry, digital twins can exist in the cloud and replace physical research models. In May 2024, the Biden Administration announced plans to fund up to $285 million to create a CHIPS Manufacturing USA institute focused on digital twins for the semiconductor industry: Digital twin-based research can leverage AI “to help accelerate the design of new U.S. chip development and manufacturing concepts and significantly reduce costs by improving capacity planning, production optimization, facility upgrades, and real-time process adjustments,” the US Department of Commerce said in the announcement.
Digital twins have had appeal in certain industries – manufacturing, oil and gas, utilities, mining – “basically physical, high-capital, asset-intensive verticals,” said Jonathan Lang, research director, worldwide IT/OT convergence strategies, at research firm IDC, in an interview with Network World.
In these settings, the rationale for digital twins has been clear, thanks to potential benefits that include better visibility into the health of assets, improved reliability, cost savings, and the ability to ensure stable operations, Lang says. “IT environments such as infrastructure, network equipment, connected devices, etc., have the same value drivers,” he says.
IBM offers a categorization scheme based not on specific industries but on the complexity of what’s being twinned. This provides a useful way to think about the needs in specific use cases and gives a look at the broad spectrum of what digital twins can do:
It’s worth noting that adding more components to the mix adds complexity. In particular, mixing and matching components from different manufacturers can be difficult because you’d need everyone’s intellectual property to play nice together within the world of your digital twin.
In the world of IT, digital twins that simulate IT infrastructure can:
The proportion of companies implementing a data center infrastructure sustainability program will rise from about 5% in 2022 all the way to 75% by 2027, as sustainability becomes an increasingly central consideration for cost optimization and risk management, according to data from Gartner Research. Industry watchers have made the case that a digital twin can aid in companies’ sustainability efforts. Some potential tie-ins between digital twins and sustainability in the IT arena include:
Dr. Mano Rao, IT director for global manufacturing at General Motors, wrote this in a blog about GM’s work with GE Digital to pursue model-based system engineering: “At GM, we developed a Virtual Factory Testbed to provide the tools and environment needed to test all manufacturing process variations that are necessary to support build-to-order manufacturing, as well as all permutations of outcomes that can result from each operation. We employ a process digital twin to mimic plant-floor behavior and test the integration of OT and IT systems—without requiring the physical lines to be deployed, and without requiring physical products flowing down the line. Not only does this help GM’s competitive advantage, but it brings us closer to our sustainability commitments.”
Digital twins can be deployed in many industries. Here are some examples:
The explosion of IoT sensors is part of what makes digital twins possible. And as IoT devices are refined, digital-twin scenarios can include smaller and less complex objects, giving additional benefits to companies.
Digital twins can be used to predict different outcomes based on variable data. With additional software and data analytics, digital twins can often optimize an IoT deployment for maximum efficiency, as well as help designers figure out where things should go or how they operate before they are physically deployed.
Building a digital twin is complex, and there is as yet no standardized platform for doing so. The Digital Twin Consortium is a global ecosystem of users who are driving best practices for digital twin usage and defining requirements for new digital twin standards.
One group that’s working to increase awareness, adoption, interoperability, and development of digital twin technology is the Digital Twin Consortium. It’s a global ecosystem of users – including industry, academia, and government members – who are driving best practices for digital twin usage and defining requirements for new digital twin standards.
In contrast with many emerging technologies that are driven by startups, commercial digital-twin offerings are coming from some of the largest companies in the field. For instance, GE, which developed digital-twin technology internally as part of its jet-engine manufacturing process, is now offering its expertise to customers, as is Siemens, another industrial giant heavily involved in manufacturing. Not to be outdone by these factory-floor suppliers, IBM is marketing digital twins as part of its IoT push, and Microsoft is offering its own digital-twin platform under the Azure umbrella.
Although digital twins have been around for some time, it’s still an early adopter technology. But the number of vendors that offer digital twin solution is growing, and recent upgrades to digital twin offerings in the enterprise IT industry include:
Digital twins offer a real-time look at what’s happening with physical assets, which can radically alleviate maintenance burdens. But keep in mind that that Gartner warns that digital twins aren’t always called for, and can unnecessarily increase complexity. “[Digital twins] could be technology overkill for a particular business problem. There are also concerns about cost, security, privacy, and integration.”
Potential challenges in developing and deploying a digital twin can include:
Interested in becoming a digital twin pro? The skill sets are demanding, and require specialized expertise in machine learning, artificial intelligence, predictive analytics, and other data-science capabilities. That’s part of the reason why big companies are hanging out their shingle: The little guy might find it more reasonable to hire a consultant team than to upskill their in-house workers. 

Josh Fruhlinger is a writer and editor who lives in Los Angeles.

The first gadget Keith Shaw ever wanted was the Merlin, a red plastic toy that beeped and played Tic-Tac-Toe and various other games. A child of the ’70s and teenager of the ’80s, Shaw has been a fan of computers, technology and video games right from the start. He won an award in 8th grade for programming a game on the school’s only computer, and saved his allowance to buy an Atari 2600.
Shaw has a bachelor’s degree in newspaper journalism from Syracuse University and has worked at a variety of newspapers in New York, Florida and Massachusetts, as well as Computerworld and Network World. He won an award from the American Society of Business Publication Editors for a 2003 article on anti-spam testing, and a Gold Award in their 2010 Digital Awards Competition for the “ABCs of IT” video series.
Shaw is also the co-creator of taquitos.net, the crunchiest site on the InterWeb, which has taste-tested and reviewed more than 4,000 varieties of snack foods.

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