Unlocking the Potential: The Benefits of Digital Twins for Training and Simulation

Learn all about unlocking the potential of streaming digital twins for training and simulation across industries such as healthcare, utilities, aviation, manufacturing, defense, and more.

Unlocking the Potential: The Benefits of Digital Twins for Training and Simulation


Numbers don’t lie.

Digital twins have become one of the critical enabling technologies for asset-intensive industries. And despite increasingly broad adoption during the pandemic - where doing things virtually become our default state of being - digital twin technology is only now about to scale at a level that will redefine industries.

The global digital twin market is now projected to reach $73.5 billion (USD) by 2027, growing at a CAGR of 60.6% from 2022 to 2027 [1]. This is because 70 per cent of C-level technology executives at large enterprises are now exploring and investing in digital twins [2] and nearly 60% expect to incorporate them into their operations by 2028 [3].

In this whitepaper, we’ll explore the value and applications of digital twins for training and simulation. We’ll detail the key benefits, including cost-efficiency, risk mitigation, scalability, adaptability, and data-driven insights.

We’ll also look at real-world examples of digital twins across industries - specifically how they’re transforming training and simulation methodologies, fostering innovation, and driving superior outcomes.

And finally, we’ll outline some of the considerations and challenges of distributing digital twins at scale and how PureWeb can help you address them.

But first, we’ll start with the obvious question – what exactly are digital twins?

What are digital twins?

Digital twins are detailed and dynamic virtual replicas of physical assets or systems. They use sensors and other technologies to transmit real-time data from a physical object or environment to the virtual environment. Digital twins are effectively full virtual ecosystems that organizations can use for a myriad of purposes, including testing, optimization, decision-making – and training.

It’s worth noting that simulations are different from digital twins. Simulations are smaller in scale, often shorter in lifespan, and they typically don’t leverage real-time data. While a simulation is usually focused on one particular process, a digital twin can itself run many simulations in order to study multiple processes. The two are related, but true digital twins are a transformative playground for the enterprise.

In fact, digital twins now replicate everything from production factories, locomotives, and wind turbines to aircrafts, military battlefields, the human body – and even entire cities.

With the advent of IoT (the internet of things), XR (extended reality) and the exponential growth of computational power, digital twins can create virtual training and simulation environments for humans that are astonishingly accurate replications of their physical selves. These digital twins are remarkably rich and immersive, which drives deeper user engagement and helps organizations simulate complex, dangerous scenarios and dynamic interactions.

For example, the Canadian class-1 railway CPKC is now able to provide digital twin-enabled training for its new locomotive engineers that realistically replicates complex, often-dangerous scenarios on a stretch of distant rail track in the British Columbia Rocky Mountains – all without trainees ever having to leave their office in Calgary, Alberta [4].

As we’ll see, the benefits of that kind of digital twin are extensive.

Why leverage digital twins for training and simulation?

Here are four critical reasons.

  1. Digital twins drive cost efficiencies and expand accessibility.

One of the main factors driving the adoption of digital twins across industries are the efficiency benefits they provide. It’s night and day vs. traditional approaches, especially for industries with significant industrial assets.

With digital twins, organizations reduce their needed investments in physical resources and equipment. But there’s no trade off when it comes to value. At the same time as they’re limiting spending, they’re also expanding accessibility to their facilities and streamlining logistics.

For example, imagine an oil company with remote, offshore facilities. Training employees to work at these facilities previously required physically moving people to them, which takes time, drives up costs, pulls on-facility staff away from their work, and creates other downstream issues like safety, which we’ll discuss below. Each time new employees need training; the cycle starts all over again. And so do the costs. And inefficiencies.

It's far more efficient to train employees from a central location, especially since digital twins can effectively replicate the real-world environment with real-time data without losing any of the hands-on experience that’s so critical to human learning.

In an era when organizations across industries face growing international competition and pressure to drive operational efficiencies, digital twins are one of the enabling technologies they’ll need to deploy in order to maintain their competitive positions.

2. Digital twins eliminate risks created by dangerous training environments.

Aviation, military & defense, healthcare, and heavy manufacturing. What do those four sectors have in common? They’re high stakes. When mistakes are made in these sectors, people can die, and enormous economic and environmental damage can result. This is true not just in day-to-day operations, but in training as well.

Digital twins offer these industries (and others like them) a way to train employees and eliminate risk at the same time. They can simulate high-risk and crisis scenarios in controlled virtual environments so trainees can experiment, learn, and develop their decision-making skills without the downside of real-world consequences.

3. Scalability and adaptability are baked into digital twin experiences.

With digital twin-enabled experiences, replicating assets or systems for simultaneous training is, effectively, a push-button exercise. Once the digital twin is in place and populated with data, it becomes an endlessly recyclable environment for training purposes. That’s massively scalable for organizations that were previously limited by the number and location of their physical assets.

The digital twin environment is also highly adaptable. Organizations can build iterative, progressive learning experiences which help employees move from the simpler, early stages of their training into sophisticated and challenging experiences when they’re further along their learning curves. And if an organization adds new physical assets in the field or the warehouse or any other part of their property, digital twin technology does what it always does – replicate those assets with up-to-date data.

When it comes to new technology adoption, there are no sunk costs with digital twins. If an organization adds or revamps the software or operating systems that underpin its physical assets, the digital twin will mirror that. It’s an endlessly scalable training environment that doesn’t also require endlessly scaling costs.

4. Digital twins provide rich sources of real-time data and performance information.

In a world where data access and data insights are critical to operational and financial performance, digital twins create a data advantage for organizations that adopt them.

From a training perspective, they offer a wealth of information and insights into how individuals (and groups) learn, behave, and perform. That means organizations can personalize feedback and iteratively improve their training methodologies and simulations over time.

Of course, this dual improvement – of both individuals and training systems – isn’t tied just to an initial training phase for new hires. Digital twins can become ongoing tools to drive data-driven performance for the entire organization.

The state of digital twins: real-world examples and applications from across industries.

With a quickening adoption of digital twins across industries, there’s a growing array of use cases for the technology. Here are some of the industries where digital twins are making their current marks.


Over a three-year period, from 2021-2024, healthcare has been vastly expanding its use of digital twins.

An Accenture survey of healthcare executives indicated that 66% were increasing their investment in digital twins during this period [5]. By 2028, the market for digital twins in healthcare is expected to be worth over $21 billion [6]. None of this should be a surprise. Healthcare is one of the areas where digital twins can add the most value.

Digital twins have the potential to significantly reshape how doctors, nurses and other healthcare personnel are trained in the years to come. Medical students will eventually find themselves learning and analyzing virtual models of animal and human cadavers instead of the real thing. They’ll also be able to better learn complex surgical processes and understand the human body.

But that’s just the start.

Digital twins may yet transform health management and treatment for both medical professionals and patients alike.

Wearable health technology could provide a constant stream of data back to a virtual model of a patient so a physician would always have a real-time view into the health and vital signs of their patients. That could lead to earlier detection of diseases and more personalized treatment approaches for acute and chronic conditions – and provide the foundation for more effective clinical research.

And at the ground level, imagine what this ‘remote monitoring’ capability means for patients with mobility issues who struggle to make it in for appointments and for those who live in remote communities.

These personalized digital twins of individual patients will also aid in surgery preparation and training. Healthcare workers will effectively be able to perform a virtual surgery on a patient before ever doing the real thing. That will lead to more successful surgeries, with fewer complications and improved recuperation times.

At Mater Hospital Dublin, we’ve also seen another way digital twins can improve patient care. By developing digital twins of its ward operations, the hospital was able to drive significant reductions in wait times for CT and MRI scanning. Wait times fell by about four hours for each procedure, which increased overall capacity by 25% for CT scans and 32% for MRI scans [7]. These are remarkable numbers when considered in the context of overstressed healthcare systems around the globe.

Gartner predicts that by 2025, 25% of Healthcare Delivery Organizations will include formalized digital twin initiatives within their digital transformation strategy [8].

A prediction: that number will seem tiny a decade from now. This is just the beginning.


Manufacturing is an ideal environment for digital twins because any given factory – especially larger ones - contain a complex web of machinery, including system critical elements like turbines and electric motors, which must run smoothly to keep a factory humming. Production lines are also built around interrelated processes, which need to be optimized for efficiency and productivity.

With all of that in mind, manufacturers really can’t afford the training of employees to damage real-world equipment or reduce overall production. This is where digital twins are so valuable. Organizations are now creating digital versions of everything from specific equipment, which can be simulated, or virtual models of their entire factories.

In this no-risk virtual environment, employees can learn to operate software and heavy equipment, troubleshoot problems and maintenance issues, and make the mistakes that are core to learning anything – all without material consequence to the operation.

Unilever, the multinational consumer goods company headquartered in the U.K is one of the leaders in this space. Unilever operates eight full digital twin factories on four continents. The sophistication of its operation shows how digital twins amplify the power of other innovative technologies. Unilever gets real-time performance data it analyzes with machine learning (ML) algorithms in order to optimize its operations [9]. This is a good example of how digital twins will increasingly be interconnected with the broad deployment of AI and ML technologies in industrial settings.


In 2019, the digital twin market in the defense sector was $3.8 billion. By 2025 it’s estimated to reach $35.8 billion, nearly a ten-fold increase in just six years [10].

According to Capgemini, the international IT and consulting company, 80% of aerospace and defense organizations already have an operational digital twin program, with the remaining 30% planning to start one. These numbers are based on a survey of 150 companies [11].

One of the key drivers of this adoption is the need for enhanced vehicle maintenance. As you might expect, it can be trickier to source parts for military vehicles like warships and aircraft than a commercial-grade passenger car.  Especially as they age. The U.S. Department of Defense (DoD) is using digital twin technology to help address this. Because manufacturers can produce parts based on digital images, digital twins help DoD reduce times on part acquisition [12]. That keeps extraordinarily expensive vehicles reliably in service, and ensures force readiness, which is critical in the theatre of war and in the case of quick-arising global conflicts.

While exact details are tricky to come by given the military’s need for secrecy, we also know that digital twins are being used as part of training and for military mission planning. Digital twins provide realistic, immersive environments that help leaders and troops train in something approaching real-world conditions.

But digital twins go beyond supporting tactical execution. By incorporating masses of data – geographical, sensor, and other intelligence – digital twins are becoming strategic tools to help plan and model missions, all in order to make better decisions in battle. And before battle.


In 2017, KLM airlines created the first digital twin of a Boeing 787. Today, the airline has over 100 digital twins, including for each type of aircraft in its fleet. Originally, digital twinning was focused on training ground crews on how to efficiently clean the aircraft (which, of course, leads to quicker turnarounds at airports and better on-time departures). As part of that original initiative, the airline has reduced cleaning times by 30% [13]. Since then, KLM has found additional applications for digital twins.

Today, pilots use the technology as a reference during safety checks, and company employees train remotely via digital twins, which drives efficiency since it keeps real world resources like mechanics focused on their tasks, instead of being distracted by in-person visits from trainees.

Rolls-Royce uses digital twins to replicate the engines it manufactures for airlines around the world [14]. This has transformed engine maintenance in the sector, moving to a preventative model that reduces downtime and enhances overall performance and efficiency.

It also comes with sustainability benefits.

In a Capgemini report, Stuart Hughes, the Chief Information and Digital Officer at Rolls-Royce noted, “Since 2014, we’ve helped one of our airlines avoid 85 million kilograms of fuel and over 200 million kilograms of carbon dioxide. We did that by taking data on how the pilot is flying the plane, how the plane is operated, how they do the operational funding around that. We found data and insights that helped them to make better decisions.” [15]

Digital twins have also helped Boeing predict and avoid mechanical failures. The company reports an impressive 40 percent improvement rate in the first-time quality of parts by using a digital twin, which is one reason it plans to develop digital twins for all its engineering and development systems [16]


From oil & gas to nuclear and beyond, digital twins are increasingly a critical technology in every corner of the energy sector. As with aviation and manufacturing, predictive maintenance is a critical value driver in energy, particularly given the costs and downside risks of equipment failures or pipeline leaks in the oil & gas industry.

There are also major advantages when it comes to training employees. British Petroleum (BP) used digital twins to accelerate training on its offshore facilities. Employees were able to learn layout and isolation scenarios without having to be at the physical facilities. The result? BP saved an entire year on its training times [17].

Nuclear energy and oil & gas producers share a core focus on safety and sustainability. This is a major area where digital twins can help avoid catastrophic events like the Chernobyl disaster and major on-and-offshore oil spills. This goes beyond just predictive maintenance. Energy producers can now train their teams how to handle dangerous situations and scenarios (like bad weather conditions on offshore assets or major system failures that create safety issues) that better prepare them for real-world emergencies.


The COVID-19 pandemic disrupted education for millions of students and educators around the globe. For many, video calls proved insufficiently engaging substitutes, especially since the hands-on elements of learning were lost or poorly replicated.

Digital twins are helping to address that. While they haven’t yet remade K-12 education, in more niche areas, and in higher education, they’re increasingly becoming critical training tools.

At Wichita State University, the National Institute for Aviation Research partnered with the U.S. Air Force to create full-scale digital twins of the F-16 aircraft [18]. Students will use the models for immersive learning in digital engineering.

Digital Twins are also forming the foundation for virtual labs where students can learn and experiment with real data, in simulated environments.

The University of Iowa has taken all of this to the next level by creating a digital twin of its entire campus [19]. The digital twin is located in the Metaverse (another example of innovative new technologies and digital experiences being combined to greater effect).

This virtual campus completely changes the distance learning experience. Instead of being stuck on an endless Zoom call, a student in, for example, New Zealand could walk around the virtual campus and attend classes as though they were on the ground in Iowa. It’s not hard to imagine a future where not only could these students attend classes ‘in-person but virtually’ but also access the virtual laboratories noted above, collaborating with classmates from all over the world.

And, of course, pilot training remains one of the core areas where digital twins are fundamentally changing education. Specific environments and scenarios can be simulated for trainees, and they can navigate them while getting comfortable with the specific layouts of the cockpits of the planes they’ll eventually fly in the real world. It’s one of the safest, most effective ways to train pilots. And it’s only possible because of the advent and adoption of digital twins.

Digital twins: Adoption challenges & considerations.

As we noted at the outset, the numbers don’t lie. Digital twins are an increasingly critical technology across industries, especially when it comes to training and simulation. The spending is there. The value is there. The future of this technology seems assured.

But that doesn’t mean there aren’t real challenges and obstacles that need to be addressed to fully leverage digital twin technology. Here are three of the biggest ones.

Data privacy and security

In software and cybersecurity, the concept of an ‘attack surface’ is important. It’s the sum of the places in a digital environment that are potentially vulnerable to cybercriminals and other bad actors.

By its nature, a digital twin expands an organization’s attack surface area. Organizations understandably need to ensure robust security for their data, for all sorts of reasons, including competitive ones.

That’s only part of the story though. Think about healthcare. If real patient data is to be used as part of training programs, even if anonymized and aggregated, the security protocols around that data must be robust. A single breach of patient data as part of digital twins could create serious challenges for adoption.

Data privacy and security will need to be carefully managed by digital twin technology providers and end-clients over the next decade.

Training infrastructure and system integration

Organizations – especially at the enterprise level - that aspire to embed digital twins into their training programs have to consider how to integrate them with existing training systems and infrastructure. That includes technical integration and also considerations about the humans involved in the training.

This opens up a wide number of considerations, including:

  • Data synchronization: Real-time synchronization between physical systems and their digital twins is crucial, as any delays or inconsistencies can compromise training accuracy.
  • Interoperability: Integrating existing training systems with digital twins may require substantial modifications to address software, hardware, and data standards disparities.
  • Training content: Crafting high-quality training content and guides that effectively utilize digital twins can be challenging, and ensuring engaging and educational simulations is vital.
  • User proficiency: Proficiency in operating digital twin-based training systems is essential. Training programs must familiarize users with this technology.
  • Regulatory compliance: Meeting industry-specific regulations and training standards can complicate the integration process.
  • Cultural shifts: Employees must adapt to the use of digital twins in training and simulation. This may necessitate cultural changes within an organization.

Computational limitations

One reason digital twins have grown in popularity is the expansion of computational power and the digital transformation of all industries. However, pushing the boundaries of realism can strain computational resources and run up against the practical constraints of today's technology.

Organizations need to find the right balance between simulation realism and available resources on an ongoing basis. This will be a careful balancing act, and essential for making the most of the advantages offered by digital twins while pragmatically managing the inherent limitations of computing resources.

What’s next? The future of digital twins.

The realm of digital twin technology is continually evolving, shaped by emerging technologies and the digital transformation of industries. In fact, new use cases and industry applications pop up on a near-weekly basis.

Here are two of the key areas that will inform the future of the technology:

Technology innovation

The future of digital twins will be shaped heavily by the integration of AI and ML, allowing digital twins to not only replicate physical systems but also make real-time predictions and recommendations based on data analysis. These ‘cognitive digital twins’ will have the potential to further optimize – if not revolutionize - operations, maintenance, and decision-making.

Quantum computing also holds immense promise in enhancing the computational power available for digital twin technology. It can handle complex simulations and optimizations that were previously infeasible with classical computing.

Collaboration & interdisciplinary approaches

Collaboration is the key to pushing the boundaries of digital twin innovation. Interdisciplinary teams, including experts in engineering, data science, domain-specific professionals, and software developers, can pool their expertise to create more comprehensive and effective digital twin solutions.

Moreover, cross-industry collaborations have the potential to drive innovation further. Lessons learned and technologies developed in one sector can be applied creatively in another, accelerating the development and adoption of digital twins in new domains.

Industry and academia collaborations are also essential for research and development. Joint efforts help in exploring new possibilities, validating models, and disseminating best practices across various sectors.

A final word.

The inescapable conclusion to draw here is that digital twins hold immense potential for revolutionizing training and simulation methodologies across industries – even if we’re only just now scratching the surface of what the technology will do in the future.

By providing realistic, immersive, cost-effective, and scalable environments, digital twins offer unparalleled opportunities for enhanced learning, improved performance, and optimized outcomes.

As technology continues to evolve, embracing the power of digital twins will enable organizations to unlock innovation, bridge the skills gap, and empower individuals with the knowledge and expertise needed to thrive in a rapidly changing world.

PureWeb: Critical enabling technology for digital twin distribution.

Game engines like Unreal and Unity have dramatically simplified the creation of photorealistic 3D assets while streaming helps enterprises avoid having to purchase and manage the hardware.

However, one of the major challenges with digital twins is distribution. Delivering a digital twin that can enable real-time collaboration and training for users in far flung locations requires significant technological power. There are operational issues managing large file sizes, with hardware, and in ensuring data security.

PureWeb's managed service helps address these problems. The PureWeb publishing platform enables distribution to remote, off-site locations around the world including oil rigs, offsite wind farms, and military bases. Our SOC 2-compliant distribution platform offers enterprise-level security assurance. Additionally, rendering happens at the server level, preventing unauthorized access to source data.

PureWeb configures your chosen cloud distribution option, handles the coordination of streaming session connections, schedules user sessions to available servers, manages security and authentication, and more.

Ultimately, partnering with PureWeb can help your organization realize the full benefits of digital twin technology for training, simulation – and beyond.


[1] “Digital Twin Market Worth $73.5 Billion by 2027.” Markets and Markets.

[2] “Digital Twins: From one twin to the enterprise metaverse”. McKinsey Digital.

[3] “Digital Twins Market by Technology, Twinning Type, Cyber to Physical Solutions, Use Cases and Applications in Industry Verticals 2023-2028.” Research and Markets.

[4] “Innovation and Safety.” CPKC.

[5] “Mirrored World”. Accenture.

[6] “Digital Twins in Healthcare - A New Era of Innovation for Businesses.” Appinventiv.

[7] “Digital Twins in Healthcare - A New Era of Innovation for Businesses.” Appinventiv.

[8] “How Digital Twins Can Accelerate Healthcare Transformation”. Forbes.

[9] “What is a Digital Twin?” Tomorrow’s World Today.

[10] “The Impact of Digital Twins on Defense Industry Transformation”. Markets and Markets.

[11] “Digital Twins in Aerospace and Defense.” Capgemini.

[12] “How Digital Twins Are Shaping The Future Of Defense System Design”. Benchmark.

[13] “KLM Royal Dutch Airlines Adopts Matterport Digital Twins to Transform Ground Operations and Engage Passengers”. Matterport.

[14] “How Digital Twin Technology is Transforming Airline Operations and Safety”. Appinventiv.

[15] “Mirroring Reality.” Capgemini.

[16] “Digital Twins In Aerospace - A Paradigm Shift”. SP’s Airbuz.

[17] “3t Transform Webinar - The Power of Digital Twin & VR Technology for Safety in the Energy Sector”. Oil Review Middle East.

[18] “Air Force to develop F-16 ‘digital twin’ with help from Wichita State NIAR”.

[19] “University of Iowa unveils digital twin campus.” Radio Iowa.