In brief

In brief

  • Digital twins allow organizations to model, analyze, and optimize our physical world’s often chaotic interactions.
  • Digital twins are increasingly accessible and can help federal agencies experiment, collaborate, and operate faster and with more confidence.
  • 89% of federal executives say they need a “central intelligence hub” to understand the complexities of their agency’s processes, workers and assets.
  • Artificial intelligence and machine learning are critical to unlocking new insight and making real-world models possible.

The growth and convergence of technologies like the cloud, AI, machine learning, 5G, and IoT are propelling digital twins to the forefront as a critical tool for managing the enterprise. At their core, digital twins replicate the performance of individuals, physical assets, and processes in a virtual environment to help us understand how these objects might behave under a variety of circumstances. Digital twins can even simulate complex scenarios in countless new and unimagined ways — using machine learning — to capture insights and present options and possibilities that might otherwise be missed.

This network effect creates a mirrored world where our physical world's often chaotic interactions can be digitally modeled, analyzed, and optimized. Leaders are starting to interconnect massive networks of intelligent twins to create living models of entire workplaces, warehouses, product lifecycles, supply chains, ports, mission spaces, and even cities. As enterprises build out these digital reflections of our working world, these capabilities will grow exponentially. Leaders will be able to make data and intelligence the primary orchestrators of the agency's business, increasing real-time agility at scale, and overhauling their innovation processes and potential.

Digital twins are already upending the way federal agencies plan, operate, and make decisions.

As with many of today’s innovations, the federal government has historically fostered many of the concepts underlying today’s intelligent twins. This includes fueling much of the early growth in computer simulation technology by modeling nuclear reactions, weather forecasting, car crash assessments, drug interactions, and flight simulators. But it was NASA’s innovative use of simulators in 1970 to diagnose and repair the damaged Apollo 13 spacecraft from 200,000 miles away that served as the most salient precursor to today’s digital twins.

The digital twin concept was first introduced in 2002, but the technologies needed to make the concept widely accessible have only recently reached a tipping point. Key enabling technologies — data storage, computing power, cloud-based interoperability, wireless networks, machine intelligence, and miniaturized sensors — have now reached the level of maturity and price points needed to support the use of digital twins for enterprise applications. While computer simulation was once the primary domain of supercomputers, today’s digital twins are accessible to any enterprise thanks to the scalable, more cost-effective compute capabilities of the cloud.

Digital twins build upon the computer-assisted modeling capabilities that have become staples of modern product development and systems engineering centers for two decades. But today’s advances in AI, ML, and real-time data connectivity have advanced upon that concept by creating virtual models that are seamlessly and continually updated across a product's entire life cycle. The virtual model can now support the physical product's operation through direct linkage and representation of its more readily captured operational data. Changes experienced by the physical object now are reflected in the digital model —and the insights mined from the digital model now support decisions for optimizing the physical object.

Accenture’s Chris Copeland and Bill Marion discuss Federal Technology Vision’s Trend 2: Mirrored World.

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Commercial companies utilize digital twin technologies to improve planning and decision-making in many sectors, including oil and gas, retail, logistics, manufacturing, infrastructure and transportation, and life sciences and healthcare, among others. But many federal agencies are employing digital twins in a variety of ways, including:

A future replete with digital twins is now being built, and today’s initiatives signal that it isn’t far off. The mirrored world will soon be the foundation on which enterprises form and test new strategies, collaborate with partners, operate faster and with more confidence, and more — in short, it’s becoming their new mission control.


of federal executives report that their organization is innovating with an urgency and call to action this year.

The federal imperative for digital twins

Three things make digital twins highly compelling as a decision-support tool, whether in government or industry.

The first, as discussed, is that they mimic the real world with unprecedented precision and accuracy due to their ability to rely upon real-world, real-time data. “It’s not a new idea to construct a model of a physical system and run a simulation that emulates how it actually works,” said then-U.S. Air Force Chief Scientist Richard Joseph. “With the development of computer technology, we’ve been able to do a better and better job to get closer and closer fidelity to the actual performance of the system.”

The second is that they operate as mathematical models — and models carry considerable power because they allow the decision-maker to change any number of variables and conduct unlimited ‘what-if’ analyses to model likely outcomes and effectiveness. Because they employ models, digital twins can modify an overall process with any number of variables — such as various resource allocations; the inclusion of automation, data analytics, or AI/ML; business process re-engineering; and policy changes — to see which combinations produce the most optimal outcomes.

A third feature that makes digital twins helpful to decision-makers is that they can remove blind spots. They identify potential points of contention, points of failure, early indicators of bottlenecks or subpar performance, vulnerabilities, inefficiencies, and spot where there’s room for improvement. By layering machine learning algorithms onto a digital twin, the tool can analyze countless variables to model a vastly expanded range of potential scenarios that can impact the business or mission — including many scenarios that decision-makers may not be considering. In this way, digital twins can help agencies pivot to a more proactive posture on risk awareness and mitigation.

These many features help explain why digital twins are becoming pervasive across so many industry sectors. This has forced many federal agencies to take notice and learn how they work and the many ways they can be applied. For example, General Electric and Boeing were early pioneers in using digital twins to develop aerospace products, and this has prompted one of their biggest customers, the Defense Department, to aggressively explore their potential for improved mission readiness, product development, supply chain integrity, and more.

Another example of this can be found in the health sciences arena. Commercial medical device manufacturers are increasingly using digital twins to model both devices and patients to better design devices for people with specific conditions. Some companies are using CT scans and MRI images to create three-dimensional computational models of individual patients that will help doctors decide on and prepare for surgeries or other procedures. And this is prompting the Food & Drug Administration to examine how to regulate these products. The agency is even exploring how digital twins might play a role in its own regulatory processes. The FDA, for example, is collaborating with the French company Dassault Systèmes to conduct an in silico (computer simulation) clinical trial to evaluate whether a simulated three-dimensional heart can be used to test and evaluate new devices for the heart. “Modeling and simulation can help to inform clinical trial designs, support evidence of effectiveness, identify the most relevant patients to study, and assess product safety. In some cases, in silico clinical trials have already been shown to produce similar results as human clinical trials,” said Tina Morrison, deputy director of Applied Mechanics at the FDA’s Office of Science and Engineering Labs.

The Interagency Modeling and Analysis Group (IMAG), which has members from roughly a dozen federal agencies, has acknowledged the considerable impact of digital twins on health sciences and is exploring the implications for federal healthcare and science agencies. “The healthcare industry is currently being disrupted by digital twin technology, where digital twins can represent diverse elements of the treatment process, ranging from medical devices to patients to healthcare delivery systems and other aspects of patient care,” notes the IMAG website. “Tailoring treatment options based on the response of each individual patient is expected to be one of the biggest benefits. Another is the ability to detect and warn of an impending health issue before it occurs. Digital twin technology may also transform how treatments are deployed, unifying existing monitoring technologies into an integrated platform that can rapidly diagnose an individual’s disease state and then evaluate treatment options based on knowledge of not only characteristics of the various therapeutic options, but also estimates of the patients current and future pathological condition. Therefore, digital twins will not only result in faster, safer, and more efficient healthcare delivery to patients, but also improve our definition and image of a healthy patient.”

Federal use cases for digital twins

Digital twins deliver value to an enterprise in many ways. Common use cases include:

With digital twins, leaders can subject a product, a system, or a business process to various modifications — such as the inclusion of an automated or AI-enabled component, for example — to see which delivers the best outcomes. It can inform managers how they can reduce their energy footprints, improve productivity, and reduce risk in their supply chains.

Digital twins help visualize and analyze the status of physical assets that are not easily accessible, such as a satellite, a military asset on the battlefield, or a wind turbine.

Employing analytics and machine learning, digital twins can suggest probable root causes of problems and run countless simulations to help select a plan of attack for repairing a problem.

The likely future state of a product or system, such as an aircraft component or an industrial facility, can be predicted based on innumerable scenarios. This capability helps ensure components that are at risk are inspected and replaced before they fail, improving maintenance and reliability. It also enables maintenance operations to shift from calendar-based, prescriptive inspection regimes to more data-informed, condition-based inspection models.

Whether it is optimizing the flow of ground vehicle and aircraft traffic at an airport, vehicle traffic at a border station, maritime ships out at sea, car traffic in a smart city or military installation, or an agency’s fleet or mail or delivery vehicles, digital twins can help agencies achieve greater efficiency and safety.

Many federal agencies are already employing digital twins to provide invaluable decision support for all of these use cases. For example, the U.S. is Navy employing digital twins for asset optimization on a large scale as it embarks upon a 20-year, $21 billion effort to modernize its four aging public shipyards, a program called the Shipyard Infrastructure Optimization Program, or SIOP. “This really is an … industrial manufacturing optimization program with a focus on productivity in the shipyards and how that affects the overall national defense,” said Steve Lagana, SIOP program manager. “How do we get submarines in and out of shipyards as efficiently as possible, so the fleet commanders have the assets they need to do their mission?” Stephanie Douglas, executive director for logistics, maintenance, and industrial operations at Naval Sea Systems Command, said digital twins “allows us the opportunity to figure out how to optimize flow, not only within the shops, but around the yards to provide the most efficient and productive layout for operations within the shipyard.”

This is a large-scale example, but agencies can apply a similar approach to individual facilities or business processes. For example, agencies can use digital twins to model and optimize their facilities’ carbon footprints or model options for rationalizing physical office space for the post-pandemic era. The Office of Management and Budget (OMB) released guidance in March 2021 directing agencies to develop annual performance goals and track their progress to improve the delivery of government services and programs in key priority areas. With digital twins, agencies can accomplish this with greater speed, precision, and confidence.

Supply chain optimization and resiliency is another growing use case for digital twins. The shocks of the COVID-19 pandemic and the March 2021 maritime interruption at the Suez Canal underscored the importance of resilient supply chains for both commercial and government enterprises. Commercial companies have been the pacesetters here, but federal agencies are following suit. A February 2021 executive order directs agencies to prioritize identifying and shoring up vulnerabilities in their critical supply chains and making them more resilient to potential shocks. By creating virtual replicas of their supply chains — consisting of hundreds of assets, warehouses, logistics, and inventory positions — agencies can use advanced analytics and machine intelligence to identify areas where real or potential value loss, risk, volatility, and uncertainty reside and where optimization is possible. Digital twins can inform logistics managers of potential scenarios and equip them to be more proactive, risk-aware, and evidence-based in their decision-making.

By creating virtual replicas of their supply chains, agencies can use advanced analytics and machine intelligence to identify areas where real or potential value loss, risk, volatility, and uncertainty reside and where optimization is possible.

Along these lines, the Air Force is looking to digital twins to help secure the semiconductors and microelectronics that supply the military. The Air Force Research Laboratory (AFRL) is working with BRIDG, a Florida-based public-private-partnership, to develop a secure digital twin for semiconductor (SDTS) capability that will enable end-users to validate the integrity of a chip or assembly of multiple chips. The effort will apply digital twin manufacturing concepts to develop data-driven, quantifiable security standards and methodologies for the fabrication of microelectronics. This should better protect the military's microelectronic components from malicious function insertion, fraudulent products, intellectual property theft, and reliability failures.

Many agencies rely on distributed field operations that collect and generate large volumes of data, whether it’s mail being processed, customs transactions at ports of entry, federal building operations, or depot maintenance activities. Digital twins can provide a framework for that data that can then be used to improve the effectiveness and efficiency of those operations dramatically.

Intelligent digital twins are driving a step-change in how federal agencies operate, collaborate, and innovate. And enterprises that get left behind will struggle to remain relevant in their mission areas as the industry sectors they oversee and collaborate with evolve technologically. Government agencies that start today, building intelligent twins of their assets and ecosystems, piecing together their first mirrored environments, will be far better positioned to succeed in a more agile and intelligent future.


of federal executives believe their organization requires a central intelligence hub to gain insights into complexities and model their organization’s processes, people and assets.

Explore further

1. Fortify

Unleash the power of data

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To gain the organizational insights and greater agility the mirrored world promises, you first need a comprehensive and robust data foundation for your twins. When intelligent twins are connected in mirror environments, they are a powerful way to turn data into actionable, big-picture insights. But incomplete or incorrect data will lead to false conclusions.

High-quality historical data is critical for intelligent twins — it’s what the twin uses to monitor real-time machine performance, build models of business processes and high-value assets, and more. But COVID-19 has made historic data increasingly unreliable. Everything from traffic and shopping patterns to energy consumption and international travel changed abruptly due to the pandemic. These anomalous changes in behavior and activity patterns have sent many machine learning models that have been trained on “normal” behavior off course, impacting supply chains, inventory management, marketing, and more. Going forward, enterprises cannot rely on historic data blindly — they need to check and correct their models as the world changes.

On top of historical data, federal agencies need a strategy for real-time data collection, or they'll miss out on the real-time analytics intelligent twins can provide. There are two sides to this: investment in sensors and IoT devices to collect data and the tools to prepare, analyze, and visualize the massive amounts of information gathered. Today, many agencies are already investing in IoT devices and sensors, but some struggle to fully utilize the data these devices generate. New cloud-based services and platforms are being developed to bridge this gap and help enterprises achieve real-time insights. Snowflake, for instance, which Barron’s recently described as a “growth juggernaut,” offers clients data warehousing as a service, which can load continuously generated real-time data, requires no manual effort, and can even digest semi-structured data.

From there, intelligent twins can make real-time data actionable in the moment, as many of the examples above illustrate. Going even further, some enterprises are starting to explore how multiple intelligent twins, connected in mirror environments, can use real-time data to safely increase autonomy. GEMINA (Generating Electricity Managed by Intelligent Nuclear Assets) is a U.S. Department of Energy program funding research projects that use AI and digital twin technology to increase the flexibility and autonomy of nuclear reactor systems and reduce operation and maintenance costs. Two of the projects to receive funding are tied to GE Hitachi’s BWRX-300 boiling water reactor design. GE Research intends to move from time-based to condition-based predictive maintenance, which will lead to significant savings. To make this possible, they will develop an array of digital twins for continuous monitoring, diagnostics, prognostics, and early warnings for the reactors. They will also develop a "Humble AI" framework that defaults to a safe operation mode when confronted with situations the algorithm does not recognize. In doing so, the system ensures the secure handling of uncertainties and increases the feasibility of more autonomous operations.

As they continue building out their mirrored worlds, agencies will also need to think about data integration across multiple twins or multiple sub-components that feed into a single twin. API connections can help achieve that data synchronization, enabling different twins or components to connect and interact.


of federal executives report their organization is experimenting with digital twins this year.


of federal executives report their organization is scaling up digital twins this year.

When built on comprehensive, compatible, and trusted data, intelligent twins and mirrored environments can help enterprises optimize operations, detect and predict anomalies, pivot to prevent unplanned downtime, enable greater autonomy, and dynamically adjust their designs and strategies with every new piece of data they collect or new test that they run. While each of these capabilities can save money and increase efficiency, their true value lies in what they represent together: a new way of understanding the agency’s business and running it.

2. Extend

A risk-free playground for innovation

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Intelligent twins have powerful simulation capabilities, and with your data foundation in place, they will let you reimagine your innovation process. They are, essentially, a low-risk playground to explore new product ideas, strategize for many possible futures, and explore limitless "what-if" scenarios. While the adoption of digital twins is gaining steam in sectors such as energy, manufacturing, healthcare, defense, and logistics, many examples still tend to be more experimental and small in scale. But the capabilities they are demonstrating will only become more valuable when enterprises can tap into multiple twins in fully mirrored environments.

For instance, intelligent twins can completely transform product development. They enable AI-driven generative design, where human workers and AI systems iteratively work together, shrinking design and manufacturing timelines significantly. And they allow enterprises to complete more product testing in simulation, meaning they can put off physical manufacturing for much longer, saving time and money.

And this is precisely what the Air Force has in mind. The service successfully used digital twins to design, prototype, and conduct initial testing on its latest jet trainer aircraft, the eT-7 Red Hawk, thereby avoiding the time and expense of building a prototype. Former Air Force Secretary Barbara Barrett even boasted that the plane had flown "thousands of hours before it [took] off," and was "assembled hundreds of times before any metal [was] even cut." The Air Force now intends to use digital twins to develop and test weapons and is building an online “Colosseum” in which vendors can show off their virtual weapons. Col. Garry Haase, head of the Munitions Directorate at the Air Force Research Lab, said AFRL plans to stage regular competition events in the Colosseum, each dealing with a different technology area.

For the Air Force, this isn’t just a new, better way to build and acquire weapons systems. It amounts to a total transformation of the military’s entire approach to modernization, says Will Roper, the Air Force’s recent assistant secretary for acquisition, technology and logistics. Digital twins will play a central role in what Roper is calling his "Digital Century Series" concept for developing future combat aircraft. "The idea of the 'Digital Century Series' is not about building aircraft that are different, but about building aircraft differently," he said. "The key tenet is a new 'holy trinity of technologies that would flip the pace of building new things and the price we pay for them." Those technologies include agile software development; modular, open-systems architecture; and digital engineering, including the use of digital twin technology.

When all aspects of a new weapon system — such as the aircraft design, all the components, the assembly line, the tooling — are digitally modeled, they can be easily optimized. “You can get expensive tooling out if you can find a better substitute. You can change a process from requiring an artisan with years of training to one requiring a lower skill level. The idea is to find a better way of assembling things, and raise the learning curve in the digital space, before you ever build the first aircraft,” Roper said. “The ambition — which I think is completely achievable — is building the first airplane as if it was the hundredth.”


of federal executives expect their organization’s investment in intelligent digital twins to increase over the next three years.

This new concept, he said, aims to overhaul the decades-long approach the Air Force has used to acquire weapons. "With the Digital Century Series, we want to give profit in design, keep production rates low, never go to 'full rate' production, not by hundreds or thousands of things so that we can keep upgrading and modernizing, and re-competing who builds the next aircraft every few years. If we do this well, and digital tools become common industry practice, you don't have to be a producer of thousands to be a competitor. You can be a competitor as a great design company. And if this sounds like science fiction, it's already happened in the automotive industry. If we do it, we can start building cutting-edge aircraft every few years, and we can build satellites this way, as well.”

From generative design to personalization to security, intelligent twin simulation is about bringing the right data and the right AI models together and exploring various possibilities, futures, and strategies from the safety of a twin. Soon, the mirrored world will bring this future-focused intelligence and agility to bigger stages, with more significant impact.

3. Reinvent

Build the big picture

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Today’s federal agencies are not self-contained; they rely on partnerships, co-experimentation, and collaboration with other agencies, academia, industry partners, and stakeholder groups, and this must be reflected in the mirrored world. It’s not always enough to have a real-time view of what’s happening within your own organization. The full picture includes what’s happening with the supply chains, vendors, research organizations, and interagency partners that you rely on too.

Europe is demonstrating one way that the mirrored world can increase big-picture visibility with its DigiTwins initiative, which aims to revolutionize healthcare by creating digital twins of individual patients that will enable healthcare decision-makers to identify optimal therapies, preventions, and health maintenance programs. The idea is to use digital twins of individual patients to safely simulate many treatments and outcomes, cheaply, and quickly before critical decisions are made. To do this, the DigiTwins initiative — which is supported by more than 200 partners from 118 academic and clinical research institutions and companies in 32 countries — is leveraging the vast knowledge base of its participating subject matter experts and organizations.

Here in the U.S., the Department of Energy’s National Renewable Energy Laboratory (NREL) has developed a modeling and simulation toolkit that can create a digital twin of an urban area to assist researchers and city planners in quantifying the advantages and disadvantages of various transportation options. The Automated Mobility District (AMD) toolkit can create digital twins of the transportation systems in selected urban districts with which it can assess the mobility and energy impacts of various transportation options. “The AMD Toolkit moves past the basic analysis of connecting point A to point B,” said NREL researcher Stan Young. “We are looking at accessibility of resources in the district — such as food, healthcare, entertainment, and employment — to its inhabitants and to outside visitors.” In one example, the toolkit analyzed the impact of deploying a half-dozen shared automated vehicles (SAVs) at Clemson University’s International Center for Automotive Research in Greenville County. The study found that adding the electric-powered SAVs would result in fuel savings of between 11 percent and 38 percent. But it also found that the addition of SAVs didn’t improve the vehicle miles traveled, occupant-free miles traveled, or travel time.


of federal executives agree digital twins are becoming essential to their organization’s ability to collaborate in strategic ecosystem partnerships.

As more organizations digitize their physical operations and systems with intelligent twins, they will be able to share designs, information, and insights easily across silos and across ecosystems, virtually test how future products might work together, and conduct business in ways that were not possible before. How will your agency evolve when the power of comprehensive visibility, unlimited simulation, and safe experimentation is at your — and your partners’ — fingertips?

Decision points

Fortify: Is your business prepared for the mirrored world?

  • Audit your data practices. Evaluate what tools and technologies are being used and where data is being warehoused to deconstruct data silos. Identify where COVID-19 may have impacted historical data and its ability to drive accurate insights.
  • Prioritize building streaming analytics capabilities. Digital twins will need a healthy data “supply chain” to be effective. Embed sensors in physical products and spaces, and invest in solutions that deliver rapid ingestion, preparation, and analysis of the data generated.

Extend: How can digital twins transform your innovation process?

  • Develop a list of key use cases for where digital twins will generate the most impact in your enterprise. Reimagine how modernization planning, business process reengineering, resource optimization, supply chain management, and product development cycles would look with digital twins at the center.
  • Integrate intelligence capabilities with digital twin efforts. Pilot generative design or synthetic data solutions to explore how they enhance design, testing, and product development.

Reinvent: How will your enterprise engage wider ecosystems of digital twins?

  • Design twins from the outset with the intent to connect them across the agency enterprise or ecosystem. Make API strategy a priority when developing digital twins. This includes evaluating and including external (or open) sources of data and ensuring the construction of an API for the twin itself.
  • Have ecosystem-scale thinking lead digital twin strategies. Target large systems as the long-term target digital twins. Think entire offices, supply chains, and more. Use individual twins to gain greater visibility into larger collaborations.
  • Short-list potential digital twin-driven partnerships. This could be collaboratively building a new twin or tapping into an already established network of digital twins.

Ian McCulloh, Ph.D

Chief Data Scientist – Accenture Federal Services

Bill Marion

Managing Director – Accenture Federal Services, Defense Portfolio Growth & Strategy Lead and Air & Space Force Lead

Paul Ott

Managing Director – Accenture Federal Services, Supply Chain Operations Lead

Viveca Pavon-Harr, Ph.D.

Senior Manager – Accenture Federal Services, Applied Intelligence Discovery Lab Lead


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