What Is Digital Twin Technology & What Is It Used For?

Digital Twin technology is rapidly redefining how we design, monitor and optimise the world around us, enabling real-time insight, predictive maintenance and smarter decision-making across industries from aerospace to healthcare. 

With market growth accelerating at unprecedented rates, digital twins are no longer concepts but practical tools shaping innovation, and the very future of technology as we know it. In this article, we will look at this new and unprecedented technology, and understand what it is used for. Keep reading to learn more…

What is a digital twin?

A digital twin is a dynamic virtual counterpart of a physical product, process, or system, continuously updated to mirror its behaviour and condition in real-time. 

This capability allows organisations to simulate scenarios, predict outcomes, and refine operations without interrupting the physical environment.

The concept of digital twins has moved rapidly from research into mainstream adoption, and forecasts suggest that by 2027 more than 40% of large enterprises will integrate them into projects to boost efficiency and revenue. 

The sector was valued at around $8 billion in 2022, and is projected to grow at a compound annual growth rate over 25% throughout the next decade.

Every digital twin has a number of key components that make up its structure:

1. The physical asset that will be monitored, analysed, or simulated in a virtual environment
2. The virtual model that acts as a digital representative of the real-world object
3. Data sources (such as Sensors and Internet of Things (IoT) devices) that continuously record relevant metrics
4. The data pipeline that transmits data from the physical asset to the virtual model
5. Analytics engines that process this data, while dashboards and visualisation interfaces present insights to users. 
6. The feedback loop that allows adjustments in the digital twin to inform changes in the physical system, creating a cycle of continuous improvement.

The process typically unfolds in stages:

• Data acquisition - sensors capture operational data such as temperature, vibration, pressure or throughput.
• Data modelling - the information is used to construct or update the virtual model, ensuring it mirrors the physical asset’s current state.
• Data application - simulation and analysis are performed, enabling predictive maintenance, performance optimisation or scenario testing.

A digital twin is sometimes described as a digital mirror or digital mapping, but the defining feature is its ability to replicate not only the structure, but also the behaviour of its real-world counterpart. 

This combination of physical and virtual domains allows engineers to test scenarios, predict outcomes and make decisions with far greater confidence than traditional models permit.

This capability is particularly valuable in maritime environments, where vessels and offshore structures operate under rapidly-changing conditions that demand precise, real‑time insight.

The history of digital twin technology

Although the term “digital twin” is relatively new, the idea of creating a virtual counterpart to a physical system is one that stretches back decades. 

One of the earliest and most dramatic demonstrations occurred during the Apollo 13 mission in 1970. When an oxygen tank exploded on board, NASA engineers relied on simulators, essentially digital twins of the command module and its electrical systems, to replicate conditions in real time. These virtual counterparts allowed them to test solutions safely on the ground before instructing the crew, ultimately saving the mission.

The formalisation of the concept came in 2002, when Professor Michael Grieves of the University of Michigan introduced a framework for what he described as a “conceptual digital representation of a physical product.” His work laid the foundation for the terminology and structure that would become standard. 

By 2010, NASA’s John Vickers coined the phrase “digital twin” in a technical roadmap, building directly on Grieves’ ideas, and marking the point at which the concept moved into recognised engineering practice.

Digital twin technology has steadily evolved over the years. The rise of the Internet of Things, cloud computing and advanced analytics in the 2010s provided the infrastructure needed for widespread adoption.

The maritime sector began exploring similar concepts as sensor technologies matured, using early modelling tools to understand hull behaviour and structural fatigue long before the term “digital twin” became widespread.

By the end of that decade, analysts such as Gartner were reporting that digital twins were entering mainstream use, particularly in manufacturing and aerospace. 

What began as a life-saving improvisation in space exploration has now become a cornerstone of modern industry.

What are the different types of digital twin?

Digital twin technology is not a single, uniform concept, but rather a spectrum of approaches that vary in scope, integration, and focus, each of which determines the level of insight available and the complexity of implementation:

• Digital Model - this most basic level is a static representation of a physical product or process, similar to a CAD drawing or simulation, but without continuous data exchange. 
• Digital Shadow - this next step is where data flows one way from the physical asset to the digital model. This allows monitoring but does not permit feedback or control. 
• Digital Twin - the most advanced form is characterised by bidirectional data flow. Here, the virtual model not only receives real-time information but can also influence the physical system, creating a closed loop of monitoring, analysis and adjustment.

Digital twins can also be classified by their role in the product lifecycle. 

• A Digital Twin Prototype (DTP) exists before the physical product is built, allowing engineers to test designs and processes virtually. 
• Once the product is manufactured, each unit can be paired with a Digital Twin Instance (DTI), which remains linked to its physical counterpart throughout its life. 
• Over time, data from multiple DTIs can be aggregated into a Digital Twin Aggregate (DTA), providing a broader dataset for diagnostics, fleet management, and learning across product lines.

Another way to classify digital twins is by the level of detail they replicate:

• Part Twins model individual elements such as a motor or pump, allowing precise monitoring of performance at the smallest scale. 
• Asset Twins combine several components into a functional unit, such as a wind turbine drivetrain, showing how parts interact in real time. 
• Unit Twins expand the view to multiple assets working together, such as a section of an oil pipeline. 
• Process Twins replicate entire workflows, such as a production line or supply chain, enabling holistic optimisation.

Beyond industrial assets, digital twins can represent infrastructure and even data itself:

• Infrastructure Twins model buildings, roads or utilities, supporting urban planning and facility management.
• Data Twins focus on operational and customer information, offering visibility into profitability, logistics, or user behaviour. Google Maps, for example, functions as a form of data twin by continuously reflecting traffic flows and geographic information.

These classifications showcase the flexibility of digital twin technology. Whether applied to a single component or an entire city, a digital counterpart that evolves alongside its physical twin, enabling better decisions through continuous insight.

The applications of digital twin technology

With its ability to replicate, monitor, and optimise physical systems in real time, digital twin technology has quickly become one of the most valuable and versatile tools in modern industry. 

It is now embedded across sectors as diverse as shipping, manufacturing, healthcare, construction, transport, energy, and agriculture:

Maritime and Shipping

Digital twin technology is influencing the maritime sector, where the operational demands on vessels, ports and offshore assets make real‑time insight particularly valuable. 

Digital twins are being used to model hull integrity, machinery performance, and fuel consumption under varying sea states and loading conditions. By monitoring structural stresses, corrosion progression and engine behaviour, digital twins allow for more accurate maintenance planning, thus reducing the risk of unexpected failures.

It is here that The Lab utilises digital twin technology, through our CMAP full corrosion assessment. Using advanced Pulsed Eddy Current Array (PECA) inspection techniques, we capture detailed data on steel thickness and corrosion across a vessel’s structure, even through coatings and protective layers. 

This information is modelled within a sophisticated 3D digital twin of the vessel, creating a visual, data-rich representation of its material condition. This provides an understanding of areas requiring immediate attention, the extent of material loss, and the likely repair scope before entering dry dock. 

Port authorities are also using digital twins to greatly improve berth allocation, vessel movements and cargo handling. By simulating tidal patterns, traffic flows and equipment availability, ports can improve throughput and reduce congestion. 

Offshore energy operators benefit too, using digital replicas of platforms, subsea structures and mooring systems to assess operational fatigue and predict potential failures.

Additionally, as environmental regulations tighten, digital twins aid with compliance by optimising voyage planning and improving energy efficiency. For an industry under pressure to decarbonise, digital twins offer a powerful tool for more sustainable maritime operations while maintaining reliability.

Manufacturing and industry

Manufacturing has been the proving ground for digital twins, particularly within the framework of Industry 4.0. 

Engineers can now rely on virtual prototypes to test designs before committing to costly physical models, thus reducing development cycles and expenses, while also allowing more radical experimentation with form and function.

On the shop floor, digital twins can simulate production lines under varying conditions, allowing for the identification of bottlenecks and inefficiencies before they can cause disruption. 

Predictive maintenance is perhaps the most celebrated application. By modelling equipment behaviour, manufacturers can forecast when repairs are needed, thus avoiding unexpected breakdowns and extending machine life.

Supply chains can benefit too. By modelling packaging performance, fleet management and route efficiency, digital twins provide visibility across logistics networks. This predictive capability allows firms to move from reactive to proactive planning, reducing costs and improving reliability.

Utilities and power generation

Utilities and power generation companies use digital twins to optimise assets, plan production and strengthen grid resilience. 

Wind farms, for instance, employ digital twins of turbines to monitor performance and predict maintenance needs. Grid operators use system-level twins to simulate demand fluctuations and integrate renewable energy sources more effectively.

In nuclear power, digital twins are being explored to model reactor behaviour under different scenarios, enhancing safety and efficiency. Hydroelectric plants use them to optimise water flow and turbine performance. 

Across the energy sector, the emphasis is on reducing downtime, improving efficiency and supporting the transition to cleaner energy sources.

Other sectors  

Beyond these core industries, digital twins are also transforming healthcare, construction, urban planning, automotive and aerospace engineering, agriculture, and mining. 

From personalised medicine and hospital optimisation, to safer construction sites, smarter cities, more efficient vehicles and aircraft, precision farming, and environmentally responsible mining operations, digital twins provide a powerful means of modelling complex systems. They are improving decision‑making and driving greater sustainability across the global economy.

What are the benefits of digital twin technology?

The rapid adoption of digital twin technology is driven by myriad benefits that organisations are already realising:

Accelerated research - digital twins allow for experimentation in a risk-free environment. This affords engineers and researchers the opportunity to test multiple design iterations virtually, exploring performance under different conditions without the expense of building physical prototypes. Operating in this way accelerates product development cycles and reduces time to market. 

Operational efficiency - by simulating processes and monitoring assets in real time, companies can identify inefficiencies and implement corrective measures before they escalate.

Predictability - by continuously analysing sensor data, digital twins can forecast when equipment is likely to fail, enabling maintenance before breakdowns occur. This in turn reduces downtime, extends asset life and improves safety. In energy generation, predictive maintenance of turbines has saved millions in avoided outages and repairs.

Risk management - digital twins allow for continuous communication with physical systems, detecting early warning signs such as abnormal temperature spikes or vibration patterns, allowing organisations to anticipate failures and mitigate risks. In industries where safety is paramount, such as aerospace, nuclear power, healthcare, this foresight is invaluable.

Sustainability - digital twins contribute by reducing waste and optimising resource use. In product design, they help minimise material consumption, while in manufacturing they improve traceability and reduce scrap.

Customer experience - digital twins can also model customer behaviour. By creating digital replicas of customer interactions, companies can personalise experiences, predict preferences and improve satisfaction.

Scalability - organisations can test new configurations virtually before deploying them universally, reducing risk and accelerating implementation. This scalability is particularly valuable in complex systems, where changes must be carefully managed to avoid disruption.

The future of digital twin technology

Despite the advancements and widespread adoption, digital twin technology is still in its relative infancy. The next decade will be important for this technology, as greater integration with emerging digital tools and frameworks allow it to further evolve its power and ubiquity.

Integration with advanced technologies 

Digital twins are increasingly being combined with augmented reality (AR), virtual reality (VR) and mixed reality (MR) to create immersive environments where users can interact directly with digital replicas. This allows users to “step inside” a twin, observing performance metrics and testing interventions in an intuitive way. 

AI will continue to enhance analytical capabilities, enabling twins to simulate complex systems, predict outcomes and even recommend corrective actions autonomously. Generative AI will certainly play a role in structuring inputs, synthesising outputs and even creating code for prototype twins, accelerating development cycles further.

Digital Twin as a Service (DTaaS) 

Cloud-based delivery models are making digital twins more accessible, putting this innovative technology into the hands of more people. 

DTaaS allows organisations to implement and scale twins quickly without the heavy upfront investments associated with the necessary infrastructure. This service model is particularly attractive for small and medium-sized enterprises, which can leverage sophisticated twin capabilities without the burden of maintaining extensive IT systems.

Digital doppelgängers 

Developers are exploring digital twins that replicate human behaviours and cognition. These “digital doppelgängers” could be used to train employees, simulate customer responses to new products, or model workforce dynamics. 

However, while promising, digital doppelgängers raise myriad ethical questions about privacy, consent, and representation that will need careful regulation. Expect this front to face criticisms and regulations as the technology improves.

Global initiatives 

Governments are already starting to recognise the strategic importance of digital twin technology. 

In the UK, the National Digital Twin Programme (NDTP) is working to establish standards, processes, and tools to build a functioning market in digital twins. Its emphasis is on ensuring that twins are safe, secure, trustworthy, and ethical. 

Similar initiatives are emerging globally, aiming to create practices and interoperable frameworks that will allow data sharing across sectors and borders in a safe, secure manner.

An interconnected ecosystem 

The long-term vision is of interconnected twins forming a digital ecosystem. 

This development represents a substantial shift in the scope of digital twin technology. Rather than focusing on a single asset or process, linking multiple twins across scales can generate insights that no individual twin could provide. 

Research from the Alan Turing Institute shows that these ecosystems are naturally hierarchical, combining twins at the component, asset, system and process levels into integrated networks capable of delivering richer analytics and decision support.

The potential of such ecosystems is evident in urban infrastructure. Studies show that when digital twins of transport networks, energy grids, buildings, and social systems are connected, they provide a comprehensive view of how a city or region functions. This integration supports predictive evaluation of policies, resilience planning, and disaster management. 

But the benefits extend beyond urban contexts. In industrial settings, ecosystems of twins can connect production lines, supply chains and logistics networks. By sharing data across these domains, companies gain visibility into how changes in one area affect performance elsewhere.

Despite the promise, the challenges remain. Establishing interoperability between different twins requires common standards, secure data-sharing protocols and governance frameworks. Without these, ecosystems risk fragmentation or vulnerability to cyber threats.

What began as a conceptual framework is now a cornerstone of digital transformation. As digital twins evolve into interconnected ecosystems, not only will they mirror the physical world but actively help in shaping it, guiding decisions that improve efficiency and sustainability across society.

CMAP assessments at The Lab

Accepted by Lloyd’s Register and proven in demanding environments, Brookes Bell’s CMAP full corrosion assessment harnesses advanced digital twin technology to deliver expert insight into vessel condition.

If you’d like to find out more about how The Lab’s full corrosion assessment services can help your business, contact us today for a friendly, no-obligation consultation.

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