What is a Digital Twin?

August 21, 2025

A digital twin is a virtual replica of a physical object, system, or process, used to simulate, monitor, and optimize real-world performance in real time.

A digital twin is a digital representation of a system or object designed to mirror its features and behavior using real-time data. This is a digital replica that permits analyzing, monitoring, and optimizing the physical counterpart.

Businesses use them by automating holistic understanding, decision-making, incessant augmentation, and interventions using some of the most effective actions, and these actions help them in making better decisions. The usage of digital twins is inevitable and it is felt by the fact that by 2032, they are predicted to rise to $259.32 billion from $24.48 billion in 2025.

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Different Types of Digital Twins

A digital twin can model everything from the whole system to individual components. Though all types of digital twins perform the same function, their scope and purposes vary greatly.

In this section, let us explore all of them one by one.

Asset Twins

Also acknowledged as product twins, asset twins are virtual representations of physical products in place of their parts. These twins can technically be composed of several component twins. Their chief purpose is to have a basic understanding of how their parts work together within one real-world product. For instance, a wind turbine might possess an associated asset twin that can keep a tab on its performance. Again, it can also recognize the probable parts failure because of wear and tear.

Component Twins

A component twin is the digital representation of a part of a product or system, like a screw or a gear. Component twins help in modeling integral parts like those under heat or stress. Engineers and designers model all these parts digitally and subject them to dynamic simulation so that they can enhance them to ensure their integrity in a given situation.

System Twins

System twins are digital representations of how several assets or products interact in a big system. These twins are also known as unit twins and they give an overview of the plant or factory. When you understand how these assets interact with each other, you can increase productivity and enhance their relationship.

Process Twins

A process twin is the digital representation of various systems that work together. System twins model a manufacturing line, but process twins model the whole factory and even the employees who operate the machines on the floor of the factory.

How Does a Digital Twin Work?

Digital twins connect real-world data and real-world assets so that they can be visualized better. They enable cross-functional teams to build, test, operate, design, and deploy complex systems in an immersive and interactive manner.

With time, smart cities are embracing digital twin-support solutions; hence, by 2028, they are supposed to reach €4.8 billion. Companies use digital twins to view the present conditions, have a good understanding of the past, and avoid future issues.

Digital twins can be very simple or complex as you need, and they have varying amounts of data that determine how finely a model simulates the real-world physical variation.

Developers who create digital twins ensure that the virtual computer model has been receiving feedback from sensors that gather data from the real-world version. You can use digital twins with a prototype so that they offer feedback on a product when it is developed. They can also work as a prototype to model what might happen with the physical version when it is developed.

Applications of Digital Twins

Digital twins are used for designing products, predictive maintenance, improving customer experience, and process optimization, among many others. According to data, organizations that use digital twins witness an average of a 15% enhancement in sales, and their system performance gains surpass 25%. Take a quick view of the different applications of digital twins:

Healthcare

To form virtual replicas of the organs of patients, digital twins are used. They allow surgeons to augment surgical outcomes and practice complicated processes seamlessly. Digital twins work to hasten the development procedure and lower the need for time-taking clinical trials.

Manufacturing

Digital twins seem inevitable in the field of manufacturing too, as they can enable virtual testing and prototyping of new products. They also accelerate development cycles; thus, they allow for customization. Digital twins also help in identifying bottlenecks and optimizing the allocation of resources. A digital twin provides real-time visibility into the whole supply chain, and this way, it enables better inventory management, logistics optimization, and planning.

Transportation

A digital twin can model the flow of traffic and enables intelligent traffic management systems that help in improving traffic flow, improving safety, and decreasing congestion. It can also design and examine vehicles virtually and optimize fuel efficiency, safety, and performance. Digital twins monitor the performance of vehicles and forecast potential maintenance requirements. This way, they ensure that the vehicles are working efficiently and safely.

Energy

Digital twins can enable efficient resource management, simulate the operations of power plants, and contribute to the integration of renewable energy sources. They can also model as well as optimize the energy grid and facilitate the addition of different distributed energy resources. Digital twins can monitor the performance of equipment and predict potential failures. Therefore, they can ensure a dependable supply of energy and minimize downtime.

Challenges of Digital Twins

Though digital twins are transformative, they come across various challenges, and they hamper their effective implementation in various fields and widespread adoption. A few of these challenges are explained below.

Technological Challenges

Integrating a digital twin with the existing systems, data sources, and infrastructure turns out to be both expensive and complex. As digital twins are supposed to be scalable for accommodating the evolving needs of a business, they become a barrier to their adoption at large. Developing comprehensive and precise models of physical systems and assets, particularly the complex ones, becomes a big challenge.

Ethical Challenges

As digital twins collect, store, and process sensitive data, they raise remarkable privacy issues and concerns, particularly regarding data ownership and consent. Again, the automation capacities of digital twins also result in the displacement of jobs in some sectors that need careful consideration as well as planning for a workforce transition.

Data-connected Challenges

To ensure the ideal working of digital twins, they need correct, dependable, and high-quality data, and most often, sensor errors, data anomalies, and other issues compromise the quality of data. Managing a huge chunk of data that digital twins generate ensures their usability and accessibility. This way, they present remarkable challenges.

Cybersecurity Threats

A digital twin, at times, becomes prone to cyberattacks and malware infections, and this factor runs the risk of exposing sensitive data and the integrity of the whole system. This is especially critical since a digital twin is a real-time replica of a physical asset, system, or process. A successful breach could not only compromise the virtual model but also be used to manipulate or cause physical damage to the real-world counterpart.

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