What is a Digital Twin? Top Digital Twin Questions Answered

March 23, 2025

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Digital twins are transforming product development

Contrary to popular belief, digital twins have been in use for several decades. Engineers have long relied on virtual models to understand how to design, produce, and maintain complex physical equipment. This is especially true for high-stakes applications like airplanes, buildings, and power plants — where a single system failure can have significant monetary impact or even threaten lives.

Over the years, digital modeling technology has improved significantly. High-fidelity modeling tools are far more accessible, and organizations are increasingly looking at digital twins as a key part of product design and digital transformation strategies. When combined with simulators and emulators, digital twins enable engineering teams to predict, analyze, and validate how a design will function in the real world — completely within the virtual plane.

For research and development (R&D) teams, such flexibility is a welcome change. As product designs grow more complex and interconnected, design and test cycles have become a notable flashpoint in development. First-pass success is an elusive target. Without accurate simulation, emulation, and analysis, it’s difficult to produce prototypes that function as expected under test — causing lengthy design, test, and retest cycles until the team achieves their desired outcome.

Digital twins play a critical role as organizations look to solve these issues. Using emulators, product development teams can analyze their designs against a range of possible outcomes, use cases, and operating environments. Not only does this help R&D predict how their designs will fare under test and design test cases accordingly, it helps them identify potential problems and course correct early in the design process — without incurring expensive rework.

Let’s examine some top questions about digital twins and explore some of the technology’s use cases and applications.

What is a digital twin?

Multiple definitions of digital twins exist — with varying degrees of specificity, rigidity, and detail. For example, Gartner defines a digital twin “as a digital representation of a real-world entity or system. The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, protocol, person, or other abstraction. Data from multiple digital twins can be aggregated for a composite view across real-world entities, such as a power plant or city, and their related processes.”

In product development, digital twin tools comprise an array of technologies that create, interact with, or capture data to update digital twins. This includes virtual models of real-world objects — such as parts, assemblies, or environments — as well as the tools that interact with the model itself. These tools include replications of known-good sources or receivers, emulators that mimic signals or protocols, or even virtual recreations of human behaviors or interactions. Together, these technologies work in concert to help engineers ensure their designs’ real-world performance match their intended functionality.

What does a digital twin do?

Since digital twins are high-fidelity representations of real-world entities, product engineers can use them to analyze the safety, performance, and functionality of their designs through virtualized simulations and emulations. This stands in stark contrast to traditional R&D workflows — which gradually refine designs through a lengthy process of building, testing, and analyzing multiple iterations of physical prototypes. By shifting the bulk of that analysis to the virtual plane, digital twins help engineering teams test a wide range of scenarios that would otherwise be too time consuming or costly to perform in the real world.

For example, using digital twins in simulation helps R&D teams predict how their products and designs will function in the real world — enabling them to arrive at the best design faster. Whether it’s a model of a jet engine or an entire 5G network, simulations help designers and test engineers identify and account for potential limitations without lengthy “test / iterate / retest” cycles.

However, simulations aren’t without limitations. Environmental conditions, standards, and other external factors can have a significant impact on a model’s behavior — creating small, but significant, differences between virtual models and their real-world counterparts. That’s where emulators, another kind of digital twin, can help close the gap. Emulators act as a substitute for known-good sources, signals, standards, and more — helping R&D teams perfect their prototypes before going to market.

Granted, real-world accuracy doesn’t always conform to real-world constraints. As digital twins grow more detailed, sophisticated, and complex, organizations will need to prioritize artificial intelligence (AI) and machine learning (ML) as much as processing power. Where a model might tell a product engineer that thousands of tests are needed to validate a specific requirement, AI / ML tools can help select the most valuable hundred or so without devoting additional resources and spend to diminishing returns.

How do you create a digital twin?

Since a digital twin is a virtual representation of a signal, source, product, or system, it consists of three core elements.

A digital model
A physical twin (the model’s real-world counterpart)
Data connecting the physical twin to the digital twin

Digital twins are living simulations and emulations — constantly held in sync with their physical counterparts via data captured from the physical twin and transmitted back to the modeling software. These updates can reflect things like wear and tear, performance degradations, or environmental conditions. Traditionally, sensors capture this data from the physical twin in real time, but measurement data recorded from physical testing can also be used to update the model.

It’s this data-based feedback loop that elevates digital twins beyond simulation. Where a simulation is merely a program’s best guess or approximation, a digital twin reflects reality — in all its imperfections. For the product engineers who manipulate and analyze the model, this level of fidelity can make all the difference between an accurate or inaccurate conclusion, adjustment, or fix.

What are the benefits of digital twin technologies?

Adopting a digital twin-based engineering approach is not as simple as procuring a few new tools and training your team to use them. Therefore, like any transformational initiative, cost, schedule, and scope must be carefully considered. However, as the technology picks up steam, let’s examine some of the reasons why business leaders have started adding digital twins to their organizational roadmaps.

Digital twins enable product engineers to tailor test parameters to match the analysis done on virtual models. This helps ensure physical products function as expected under test — preventing lengthy rework and retest cycles. Using simulators and emulators helps R&D teams achieve first-pass success by making critical course corrections and modifications before prototyping.
Digital twins can help product design teams create shared repositories of reference data — making it easier to automate test processes and workflows. By deploying a process and data management (PDM) system, R&D teams can define, manage, and execute digital twin-based tests. The PDM’s process definitions are effectively reusable digital scripts — enabling engineering teams to orchestrate automate the test processes they define in the simulation.
Continuous feedback loops of test, measurement, and production data ensure that digital twins always reflect a product’s latest iteration or build. With easy access to the latest virtual model, R&D teams can quickly predict, analyze, and optimize how new standards, modifications, or updates will impact its real-world performance.
Digital twin-based workflows shift significant portions of traditional design / test / build / iterate processes to the virtual plane. This consolidates project metrics into a single source of design, emulation, and test data. Built on a feedback loop of real-world metrics and emulation-based test data, these centralized repositories help keep project teams, third parties, and stakeholders on the same page and reduce churn-related missteps.
As more R&D work moves to the virtual realm, digital twins can help organizations improve sustainability efforts by minimizing the environmental impact of repeated physical prototyping means. First-pass design successes reduce overall waste, while real-world simulations offer opportunities to improve sustainability, streamline processes, and reduce energy use.

How is a digital twin different from a model or 3D simulation?

A model is a representation of a physical system or product built with a specific function, such as analysis or design. A 3D simulation is a virtual model of a physical system, but it looks more broadly at the behavior and interactions.

The digital twin synthesizes the two to create something more comprehensive and dynamic. While a model or 3D simulation is limited and unable to reflect the entire lifecycle, a digital twin enables real-time monitoring, analysis, and control because it’s continuously linked to a physical counterpart.

When used in concert with emulators in a virtualized workflow, digital twins enable project teams to examine the viability of future designs by running complex, what-if scenarios with real-world applicability and insights. By contrast, models and 3D simulations would never get past a crude approximation.

Are digital twins only used for physical products?

No. Digital twins are commonly used across virtual and hybrid applications as well. For example, cybersecurity teams use digital twins of cyberattacks to test and assess their network defenses. A far cry from simple packet capture replays, these attack models are living emulations of complex network attacks — comprising multiple connections, endpoints, and variations. And, since they’re derived from real-world attacks, the models can continuously evolve to reflect cybercriminals’ newest techniques as analysts discover new scripts and code.

Digital twin use cases

As digital twin technology evolves, potential applications will continue to expand. Here are a few examples.

Communications and networking

A network digital twin is a simulation of a communication network integrated with its operating environment and the application traffic it carries. The virtual model reflects the communication protocols, topology, traffic, and physical environment — which can be enhanced by adding cyber vulnerabilities and defenses. By running simulated cyberattacks, network architects and information security teams can evaluate the network’s potential resiliency and take steps to strengthen security posture.

Aerospace & defense

NASA has used forms of digital twin technology since the Apollo 13 mission in 1970. Today, the software is deployed to simulate aircraft, missiles, battlefield communications, and other systems — reducing the cost of mission-critical testing and protecting the equipment and personnel that matter most. And in the space industry, where the risks and costs are vast, digital twin technology is used to design satellites, plot spaceflights, and model complex communication networks.

Manufacturing

A cornerstone of Industry 4.0, digital twins play a significant role in the digital transformation of manufacturing industries. Whether they’re helping identify supply chain-related process optimizations or hastening a product’s time-to-market, digital twins are helping manufacturers connect workflows, reduce downtime, and increase throughput.

Smart cities

Digital twins are used to model and simulate the behavior of buildings and transportation systems. This aids planners in reducing the risk of incidents while improving traffic and pedestrian flow. In addition, digital twins will play an increasingly significant role in making cities greener helping city planners reduce waste and meet sustainability directives.

Healthcare

Digital twins are mainstays in both medical research and personalized medicine, enabling researchers and practitioners to model patient conditions and potential treatment outcomes. By analyzing structured and unstructured data, digital twins can supply insights into how providers can optimize care and operations. These process models can help improve administrative aspects such as patient flow and determining the optimal time to send patients home.

Automotive

With lengthy supply chains, complex integrations, and substantial governmental oversight, automakers and suppliers have zero margin for error. That’s why they’ve been quick to embrace the flexibility and agility of digital twins. Take autonomous drive systems, electric vehicles, and vehicle-to-everything communications, for example. Digital twins have played a significant role in developing all three. Whether they’re evaluating the energy efficiency of electric batteries, emulating 5G connectivity, or honing autonomous driving algorithms with radar scene emulation, the next generation of automotive innovation will be powered by digital twins.

Digital twins aren’t a luxury — they’re a necessity

Digital transformation isn’t a buzzword. It’s a mission statement. For R&D teams, digital twins aren’t not a “new toy” or luxury — they’re a key piece of an organizational digital transformation strategy. So long as companies stake their reputation on delivering high-quality products on time and on budget, the use of digital twins will continue to grow.

At the same time, it’s safe to say that we’ve barely scratched the surface of the technology’s potential. Artificial intelligence, machine learning, and other breakthroughs will continue pushing the limits of digital twins beyond our wildest dreams. It’s a fair assumption that, before the end of the decade, every industry will use digital twins — in some role or another — optimize their design, build, and test lifecycles.

I’ll take that bet any day.