What is a digital twin?

Digital twins enable continuous monitoring, simulation and analysis of an object, product or system over the course of its lifecycle, from design and production to maintenance and decommissioning. They can also incorporate external processes and critical variables that affect an asset’s performance.

A key feature is real-time, two-way data exchange between the object and its virtual replica, helping ensure that simulated conditions accurately reflect the physical world. Enterprises can also connect multiple digital twins to model more complex systems in service of a larger digital transformation or Industry 4.0 strategy.

By providing insight into how an object functions in the present—and projecting how it might behave in future scenarios—digital twins help organizations improve efficiency, accelerate innovation and make data-driven, informed decisions. Common use cases include process optimization, predictive maintenance, supply chain optimization and product development.

Many modern digital twin providers, including Siemens, General Electric, Nvidia, IBM, Bentley and Microsoft, offer a full suite of services. Packages might include hardware layers (such as sensor kits), data processors, synchronization services, simulation engines, analytics platforms and visualization dashboards. But enterprises with more specialized applications might instead take a modular approach, choosing several services to match their needs.

Digital twins can represent virtually any object, from buildings and bridges to cars, airplanes, historical artifacts and even the earth. They might also model complex systems such as traffic patterns, weather events, healthcare treatment plans and factory operations. Finally, in more experimental contexts, digital twins might be based on real or imagined people, complete with modeled voice, appearance and personality traits.

Digital twins are now widely used across industries: A 2023 Strategic Market Research study found that roughly 75% of businesses employ them in some capacity. These initiatives can be costly and resource-intensive. But for many enterprises, they are worth the investment: 92% of companies who deploy digital twins report returns above 10%, while over half report at least 20% return on investment, according to a 2025 Hexagon survey.

• A physical asset that an enterprise aims to monitor, analyze or simulate in a virtual environment
  
  
• A virtual model that acts as a digital representation of the real-world object or system
  
  
• Data sources such as sensors or Internet of Things (IoT) devices that continuously record relevant metrics like temperature, pressure or motion
  
  
• A data pipeline that transmits sensor data to the virtual model, keeping it synchronized with its associated physical asset in real time
  
  
• A feedback loop that sends insights or control signals from the digital twin back to the physical asset to optimize performance, efficiency and decision-making
  
  
• An analytics engine—often powered by machine learning or artificial intelligence—that can detect data patterns, simulate future scenarios, perform predictive analysis and assist with asset tracking
  
  
• Visualization interfaces and dashboards that enable teams to interact with a 2D or 3D representation of assets or systems

While digital twin workflows vary widely across industries and applications, most include these fundamental steps:

An enterprise might begin by equipping a physical object with an array of sensors, which capture its performance, condition and operating environment. In IoT contexts, an organization might deploy “smart objects,” which often come preinstalled with built-in sensors that can continually collect and share data. In IT settings, teams can build digital representations of applications, software and computers (virtual machines) using virtualization technologies. They can then deploy software agents to collect data at or near the digital asset for monitoring and analysis.

A virtual model is a digital replica of an object or system, built using the data gathered from its real-life counterpart. It is embedded with key attributes that help it realistically react to variables such as environmental conditions and interactions with related systems.

For example, a digital twin of an airplane turbine not only simulates wear and failure at the same rate as it’s real-life counterpart but also accounts for aerodynamic forces during flight and the influence of connected engine and hydraulic components. This detailed modeling helps ensure that the digital twin can reliably simulate how its real-life counterpart might respond under a range of conditions.

Live data integration enables continuous, real-time communication between the digital twin and its physical counterpart. This dynamic feedback loop can help organizations optimize performance, enhance system reliability and implement predictive maintenance—when teams anticipate issues ahead of time, reducing downtime and extending asset lifecycles. Enterprises often automate the data exchange process, freeing them up to tackle higher-level strategic tasks.

Digital twins enable teams to run safe, cost-effective experiments within a virtual environment. For example, in a manufacturing context, a team can simulate how an assembly line upgrade might affect performance and efficiency. Or it might test whether a more affordable packaging option can withstand the rigors of shipping and distribution. By exploring a range of “what-if” scenarios, digital twin platforms help teams improve operational efficiency and enhance product quality without the risks and costs associated with real-world testing.

Analytics engines can suggest certain operational changes—such as scaling cloud capacity, production volume or team budgets—to help teams optimize performance and spending. They might also integrate with customer relationship management (CRM) platforms and enterprise resource planning (ERP) tools to streamline production workflows and customer funnels.

Both simulations and digital twins help teams replicate and test scenarios in a digital environment. But while digital twins mirror a real-life object and its specific traits, simulations often exist entirely in the virtual world without an immediate connection to real-world systems.

Put another way, simulations are static; they run predefined scenarios with no built-in mechanism to transmit their findings to a physical system. In contrast, digital twins can dynamically reflect real-time conditions while at the same time sending information to the physical systems they represent.

Another distinction is that digital twin solutions can connect multiple assets and systems, instead of evaluating them in isolation. Teams can seamlessly add or eliminate components to mirror real-life scenarios, determining how changes to one asset might impact the wider ecosystem.

3D models are static representations of an object at a single point in time. Organizations can use them to understand what an object looks like, but not how it behaves. On its own, a 3D model can’t assess future scenarios or represent real-time conditions. However, 3D models often serve as a foundational component of both digital twins and simulations, providing accurate visual and spatial representations of physical assets or processes.

Although teams can build connected twins (networks made up of linked digital twins) to capture a wider view of system performance, these networks are typically used to optimize asset lifecycles within a single production environment. 

Digital threads, meanwhile, are often broader in scope, connecting data across multiple departments, processes and environments to capture an organization-wide view of assets and systems. Digital threads can centralize data from multiple production environments so it’s accessible to stakeholders across the organization.

Ultimately, digital threads are ideal for gaining a holistic view of the organization’s interlocking systems, while digital twins are better suited for fine-tuning individual assets and processes.

It’s common for several types of digital twins, each offering a different layer of magnification, to co-exist within a single production environment. The four main types include:

Component twins, also called part twins, replicate individual components, offering a granular level of insight into specific parts. For example, a component twin might use an array of sensors to mirror a valve in an oil pipeline, a motor in a wind turbine or a turbocharger in a car.

Asset twins replicate complete functional units, often made up of two or more components, and show how these components interact in real time. Asset twins might replicate an oil pipeline valve system (made up of multiple valves and pipes), a wind turbine drive train (made up of a motor, gearbox and shaft) or a car’s turbocharging system (made up of a turbocharger, intercooler and compressor).

System or unit twins enable enterprises to understand how assets fit together to form a larger, integrated system. They provide visibility into asset interactions while identifying opportunities for performance enhancements at the system level. System twins might mirror a segment of an oil pipeline (made up of multiple valve and pump systems), a wind turbine (made up of motors, blades and control systems) or a vehicle powertrain system (including the engine, transmission and driveshaft).

Process twins provide the broadest view, revealing how systems work together across a production facility, supply chain or operational workflow. Process twins can help ensure that the entire production environment, not just specific components, is operating at optimal efficiency. Process twins might replicate an end-to-end oil distribution network, an energy-generating wind farm or an automotive manufacturing process.

Digital twins give enterprises greater visibility into complex systems along with the flexibility to explore multiple operational configurations before committing real-world resources to them. Major benefits include:

Digital twin solutions help enterprises experiment with different product designs, workflows and manufacturing processes within a virtual testing environment, accelerating innovation and reducing time to market.

For example, aerospace engineers can build digital twins of experimental aircraft, each with different wing and propulsion designs, to determine which iteration shows promise for further development. This approach is far more cost-effective, and safer, than building and testing physical aircraft prototypes for each proposed design.

After a new product has gone into production, digital twins can help mirror and monitor systems to achieve and maintain peak efficiency throughout the manufacturing process. Teams can also identify cost-cutting opportunities without interfering with current workflows.

For example, an enterprise can test out a more affordable material or manufacturing process in the virtual environment—and determine whether it can maintain performance and emissions standards—before rolling it out on a wider scale. Digital twins can also use historical data for predictive maintenance (forecasting which assets are likely to fail before an error occurs).

In complex modern systems, a single malfunction or asset failure can cause widespread disruptions, especially if teams struggle to identify the root cause. For example, a small circuit that controls cooling fans in a data center might fail, triggering overheating that takes an entire server rack offline.

Digital twins can address this problem by reflecting the real-time condition of individual components, including sensors, circuits and capacitors. By continuously communicating with the physical system, a digital twin can detect early warning signs, such as abnormal temperature spikes, and anticipate imminent failures. This capability helps teams act early, avoiding downtime and costly errors.

To remain competitive, enterprises must quickly scale operations to accommodate shifting product demand, economic conditions and strategic priorities. Traditionally, scaling up or down is a slow, arduous process, requiring teams to carefully validate new systems before rolling them out across the organization. Digital twins make this process faster and less risky by providing a virtual environment where teams can safely adjust parameters and test configurations ahead of universal deployment.

Digital twins can also connect to live systems, enabling them to continuously transmit scaling adjustments to their physical counterparts in real time. For example, digital twin platforms might use algorithms to automatically add or remove cloud nodes during usage spikes to reduce traffic bottlenecks and maintain stable performance.

Many industries rely on digital models to make sense of complex systems, spur innovation, maintain equipment and optimize efficiency. Digital twins are used extensively in the following industries and applications:

Organizations can use digital twins to model jet engines, locomotive engines, electricity-generating turbines, utility assets and other power-generating systems. Digital twin platforms can establish time frames for regularly scheduled maintenance, detect hardware irregularities and enable testing of new components. They can also facilitate the transition to renewable energy by monitoring grid demand, simulating new asset configurations and forecasting grid trajectories.

Physics-based digital twin systems can help engineers design durable, safe and cost-effective structures, including buildings, drilling platforms, canals, dams and bridges. They can, for example, determine whether a particular bridge can withstand heavy wind, rain and traffic, giving engineers the opportunity to alter their design before construction begins.

Digital twins can also provide visibility into already-built structures, for example by revealing how key systems—such as plumbing, HVAC, electrical and security—interact inside an office building. These insights can help inform building information modeling (BIM) systems, which use digital representations of a structure to manage its construction and maintenance.

In manufacturing, digital twins (often equipped with AI capabilities) can enhance quality control, supply chain management and error detection by providing oversight across a product’s end-to-end lifecycle. For example, an electronics manufacturer can build a digital replica of a factory floor, reflecting the real-world location’s inventory levels, production schedules, equipment statuses and other operational data.

Digital twins can generate key health insights through disease progression forecasting, which predicts how patients might respond to various treatment options, and through enhanced diagnosis, which uses highly detailed modeling to pinpoint how interactions between organs and body systems can impact health.

They can also help hospitals optimize their operations—including staffing, scheduling and equipment maintenance—and can facilitate the transition to personalized healthcare, where treatments are customized to match individual patients’ needs.

Digital twins are used extensively in auto design, both to improve vehicle performance and to increase production efficiency. For example, vehicle designers can conduct extensive safety and emissions testing with virtual replicas before benchmarking real-life vehicles.

Civil engineers and urban planning experts use digital twins to simulate how pedestrians and vehicles move through cities. City models often incorporate 3D and 4D spatial data, IoT object data and AI-powered analytics to simulate how new policies, infrastructure upgrades or transportation systems might impact the built environment. Digital twins play a key role in smart cities, which use IoT-connected devices to continuously collect and share data that can be harnessed to improve quality of life and sustainability.

The concept behind digital twin technology dates to the 1960s, when NASA built physical replicas of its spacecraft to study how they might respond to different conditions before sending their real-life counterparts into orbit. In 1970, when an onboard explosion threatened the lives of the Apollo 13 crew, NASA relied on these models to explore different rescue scenarios from the ground, according to the administration’s Technical Reports Server. While these early efforts used physical copies instead of virtual ones, they paved the way for what would eventually become known as “digital twins.”

In 2002, scientist and business executive Michael Grieves conceptualized a product lifecycle management (PLM) framework that links a physical product with its virtual counterpart through continuous data exchange. Eight years later, NASA’s John Vickers officially coined the term “digital twin” in a NASA technical roadmap, building from Grieves’ “mirrored spaces” concept.

The digital twin market is rapidly expanding, according to a Fortune Business Insights report. It’s expected to grow from USD 24.5 billion in 2025 to USD 259.3 billion by 2032, with industries such as smart cities, aerospace, healthcare and manufacturing driving growth. New and emerging digital twin capabilities include:

Generative AI can predict how systems might react in the future based on both historical and real-time datasets. This capability empowers teams to make better-informed operational decisions and investments. AI technologies can also help digital twin systems optimally scale and provision resources without human intervention.

Instead of automating only rote, repetitive tasks, AI models can use digital twins to make longer-term, multi-step decisions. For example, they can anticipate how a component failure might cascade through the network, affecting neighboring assets and systems; alert relevant teams each time a component needs maintenance; recommend network enhancements so that failures are less likely to occur; and in some cases, implement operational changes entirely on their own.

Like software as a service (SaaS), digital twin as a service (DTaaS) is becoming a popular choice for enterprises. The delivery method enables organizations to quickly implement and scale digital twins through the cloud, without having to program them from scratch or maintain the servers they live on.

Developers are designing digital twins capable of mirroring human behaviors and cognition. Digital doppelgängers can be used for both personal applications (such as legacy preservation or audience engagement) and professional ones (such as training employees or automating repetitive tasks).

They can also be useful in research contexts. For example, researchers can perform experiments with synthetic users to simulate how real-life humans might respond to new products and features. Enterprises can then aggregate these findings to project population-level trends.