Digital Twins: Transforming Government Operations with Interoperability

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Digital twin technology can replicate almost everything about a physical environment.

Over the past decade, the prevalence of digital twins has skyrocketed within government.  

As defined by the Digital Twin Consortium, a digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. Powered by domain knowledge and real time data, digital twins provide a holistic, end-to-end view of data across an entire organization.

With digital twins, agencies can simulate operations far more efficiently and cost effectively than building physical representations. Using generative artificial intelligence and machine learning, the technology can replicate everything about a physical environment or asset. These virtual representations, combined with sensors to collect real time data and live feedback, allow users to dive deep into real world operating scenarios.

For example, when operational equipment needs to be deployed, it’s essential to understand how it’s operating, the potential for outages and what can be done to prevent such events.

A digital twin gives agencies the ability to track all of that not only in real-time, but also in the future through predictive data analysis—allowing them to predict problems and prescribe fixes before they occur.

The Power of Digital Twins in Government

Implementation of digital twins has expanded across government and is already making a major impact. The GAO’s 2023 Science & Tech Spotlight: Digital Twins details how the technology is used for capabilities, from supply chain management and spacecraft manufacturing to healthcare and climate forecasting.

We’ve seen widespread use of digital twins within numerous agencies, such as the Department of Defense, Defense Advanced Research Projects Agency, Department of Homeland Security, the National Science Foundation and more.

For example, NASA is building a digital twin of its Michoud Assembly Facility in New Orleans, where the agency manufactures critical hardware components for rockets under development. By utilizing digital twins, the space agency aims to improve the efficiency and quality of facility maintenance and manage the needs of NASA programs, which will ultimately lead to decreased downtimes, improved communication and lower costs.

Digital twin technology is also playing a pivotal role within government’s manufacturing process, fueled by the Department of Commerce’s goal to innovate the federal approach to manufacturing through Manufacturing USA.

As agencies see how digital twins can streamline service delivery and optimize decision-making and much more, their use cases are expanding, leading to a need for interoperability. 

Increased interoperability between digital twins and across larger ecosystems will allow digital twins to make an even greater impact on government operations and help meet the evolving demands of today’s digital-first environment.

Interoperability is Key to Digital Twin Success

While digital twins continue to gain momentum in the public sector, a key factor in maximizing the technology’s benefits will be advanced interoperability.

Data is the foundation of any digital twin. Within larger agency ecosystems, there can be numerous vendor—each with their own digital twin—working on their component of a complex project simultaneously. This means data is coming from many different sources within an agency or across multiple agencies.

It’s imperative for these digital twins to effectively communicate with one another and exchange critical data to ensure actions are aligned and platforms are compatible.

Without this capability, there is a higher cost to entry for digital twins and a lower ROI as each digital twin is practically working in a silo. This can create complexity in the deployment and management of a digital twin, potentially resulting in costly missteps that can delay production and hinder mission success.

A lack of interoperability also diminishes trust—the degree of confidence that a system performs as expected regarding safety, security, privacy, reliability and resilience — across the organization.

Enhanced digital twin interoperability allows for a successful industrial metaverse, allowing agencies with multiple vendors and dispersed facility sites the ability to successfully interact with one another to complete a specific project.

The ability to effectively consume and react to information from another system allows the digital twin to be an additive to the collective intelligence of an organization, as opposed to a hinderance. Ultimately, this leads to the holistic understanding of information exchanged between any components.

Additionally, interoperability provides data provenance — the ability to identify the place of origin for a specific piece of data—which is critical to validate any conclusion made from analyzing project information.  

Digital twins are applicable to virtually all industries, however, agencies looking to adopt digital twin technology must consider a platform with robust interoperability capabilities. By leveraging an organization’s existing application programming interfaces (APIs) and other connection methods, an interoperable digital twin platform can eliminate the need to purchase additional proprietary software suites or undergo costly migrations of existing data models into new systems. This approach allows for the successful integration of a digital twin without significant disruptions.

For example, if an organization already has a digital twin of their product, instead of recreating it for another organization’s digital twin, it should be able to directly communicate with their digital twin from within theirs to save on time and money.

With strategic collaboration with industry partners, the government will continue to realize the full potential of digital twins—enabling faster decision-making, advanced data analytics, robust employee training and more—ensuring agencies can stay agile and responsive to the demands of the federal landscape.