Oct 2, 2025
Digital twins are virtual replicas of physical assets that allow for monitoring, simulating, and optimizing processes to improve efficiency and make decisions.
Immersive technology is advancing at a rapid pace, driven by innovations in the Internet of Things (IoT), Artificial Intelligence (AI), and real-time data analytics. This convergence has given rise to digital twins, one of the most transformative tools of the digital era.
Digital twins have become fundamental pillars of digital transformation within Industry 4.0. Their ability to create accurate virtual replicas enables organizations to improve operational efficiency, reduce costs, and gain valuable strategic insights through a comprehensive digital view of their physical processes and assets.
Today, multiple industries are adopting this technology as a strategic investment to boost efficiency, advance sustainability, and strengthen their competitive position in the market.
The origin of the “digital twin” and its historical evolution
Although the modern term “digital twin” gained popularity in recent decades, its essence dates back to space exploration, with NASA being a pioneer in applying similar concepts during the Apollo 13 mission in 1970.
After the explosion of one of the spacecraft’s oxygen tanks, NASA engineers used ground simulators and exact replicas of the vehicle to virtually recreate the conditions of the damaged spacecraft. This allowed them to analyze the cause of the failure and develop and validate real-time solutions, which proved crucial to ensuring the astronauts’ safe return. This intervention is widely recognized as the first practical application of what we now call a digital twin.
Decades later, in 2002, Michael Grieves formalized the theoretical concept within the context of Product Lifecycle Management (PLM) during a presentation at the University of Michigan. Later, in 2010, John Vickers of NASA officially coined the term digital twin to describe, more concretely and operationally, this dynamic connection between the physical world and its virtual replica, continuously fed with real-time data.
Definition of a Digital Twin
A digital twin is a virtual replica of a physical object, system, or process that simulates its behavior in real time using data. This technology enables monitoring, visual analytics, and performance optimization of the replicated element through accurate simulations and data gathered from the real system in operation.
In essence, a digital twin is not a static representation, but a dynamic tool that adapts and learns over time, enabling companies to anticipate failures, explore improvements, and iteratively adjust operations. Imagine a manufacturing plant where every machine and process is monitored through its digital twin: with each new data input, the system can predict anomalies, adjust parameters, and maintain uninterrupted workflow continuity.
Digital twins empower companies to act in the present while planning for the future.
As SAP Signavio points out, digital twins provide organizations with the visibility and understanding needed to plan, test ideas, prioritize initiatives, forecast risks, and manage complexity. In doing so, they help reduce uncertainty in strategy execution and support better-informed decision-making.
Digital Twin vs. Metaverse: Are They the Same?
The concepts of digital twin and metaverse are often mixed up and confused. This is largely because both are linked to immersive technologies such as VR, AR, or XR. However, their goals and architectures are different.
The main differences between digital twins and the metaverse lie in their focus: a digital twin centers on simulation, monitoring, prediction, and process optimization through real-time data. It functions as a tool to improve performance, anticipate failures, and test changes without impacting the real system. This is why digital twins are most commonly used in industrial environments.
In contrast, the metaverse is an immersive digital space where users interact socially via avatars in virtual worlds, with a strong emphasis on entertainment.
An important nuance is that the metaverse does not replace the digital twin, nor the other way around, they are two distinct technologies that can complement each other.
Types of Digital Twins: Application Examples
When we talk about “types of digital twins”, we refer to different categories used to organize them by scale, level of detail, and scope of application. These classifications have been shaped by user needs and help determine what level of digital twin is right for each project. The most common categories are component, product, system, and process.
Digital twins of components
Focus on the individual units: a piece or element that has operational relevance on its own. For example, a bearing, valve, gear, or an isolated motor within a larger system. These twins allow for greater precision when diagnosing localized failures, estimating lifespan, setting just-in-time replacement policies, or detecting deviations.
Digital twins of assets
These involve multiple components that function together as an operating unit (e.g., a complete machine, a piece of equipment, or a vehicle). Instead of focusing on isolated parts, this type incorporates interacting components, offering a more holistic view of the asset’s behavior.
Digital twins of systems
Group together several interrelated assets acting as a subsystem, for example, a production line, an entire plant, or a functional module. This type of twin shows how assets interact with each other, helps identify bottlenecks, and analyzes dependencies.
Digital twins of processes
Model entire operational flows or large-scale business processes (supply chain, end-to-end manufacturing, logistics operations). Rather than focusing on a physical asset, they capture the sequence, interdependencies, and overall performance of the process. They enable simulations, predictive scenarios, and improvements across the entire cycle.
Benefits of Digital Twins in Industry 4.0
Digital twins have proven to be a key enabler of industrial digital transformation, delivering benefits that impact both operational efficiency and business strategy:
Real-time simulation and optimization: Enable simulation of different scenarios with updated data to optimize processes, anticipate problems, and support informed decision-making.
Predictive maintenance: Through predictive models powered by machine learning, they enable early detection of failures or wear, reducing downtime and unplanned repair costs.
Safe innovation: By validating new ideas and adjustments in a virtual environment, risks of physical implementation are minimized, allowing for a faster, safer innovation cycle.
Cost savings and ROI growth: Reduced downtime, fewer errors, and minimized waste, combined with resource optimization, directly improve operational profitability.
Sustainability: Minimize environmental impact by using materials and energy more efficiently and reducing waste, aligning with corporate sustainability goals.
Enhanced strategic decision-making: Provide business leaders with a comprehensive, detailed view of processes and assets, improving planning accuracy and governance of operational complexity.
Applications of Digital Twins
Digital twins have demonstrated value across multiple sectors, from manufacturing to the design of smart cities. Below are some examples of how this technology is transforming industries:
Digital twins in Manufacturing: Enable the simulation and optimization of production lines, helping identify bottlenecks and maximize productivity. They support predictive maintenance by anticipating failures and reducing downtime. According to McKinsey, some factories have reduced operating costs by 5–7% by optimizing planning and eliminating bottlenecks with digital twins.
Digital twins in AECO: Optimize building design using AR, improve planning, efficiency, and on-site safety. They allow BIM/CAD data to be visualized in situ and even enable infrastructure layers to be displayed separately for better understanding. Once construction is complete, the twin continues to monitor and support predictive maintenance, becoming, as Innowise defines it, “the brain of the building’s entire lifecycle.”
Digital twins in Smart Cities: Urban digital replicas help cities manage key systems such as mobility, utilities, energy and water consumption, and waste management. As PwC highlights, in urban contexts, digital twins continuously capture information about the built environment via sensors and drones, ensuring constantly updated representations.
Digital twins in Energy: Allow for the virtual replication of entire energy infrastructures, from generation plants (wind, solar, offshore) to distribution networks. This enables advanced, real-time management of assets and resources, identifying improvement areas in distribution and reducing energy losses.
Digital twins in Education: Enable gamification of tasks into challenges and achievements, making learning more dynamic and motivating. They allow students to practice skills in safe, controlled environments and provide detailed progress tracking for more personalized education.
Digital twins in Telecommunications: Facilitate visual monitoring of antennas, towers, nodes, and distributed data centers. They provide real-time, detailed information about asset status, allowing for rapid detection of failures or anomalies through graphical interfaces and 3D models.
Examples of Digital Twins
Digital twin for production line simulation and optimization
DIMECO specializes in the manufacturing and supply of automated production lines for metal processing.
In the case study of DIMECO, we explain how we applied our digital twin software TOKII to provide a graphical representation of the HMI of their production lines and to calculate loop control and production speed.
Digitalto twin to centralize and monitor real-time data
VICINAY MARINE is a leader in the design, innovation, production, and supply of chains and mooring systems for the offshore wind and naval industries. They faced the challenge of integrating and organizing more than 20 years of stored data, scattered across different platforms.
In the case study of VICINAY, we detail how we used TOKII to centralize that data within an interactive interface, enabling real-time monitoring.
Digital twin for asset maintenance
TEICON is an engineering and construction company focused on innovation and Industry 4.0. They use our digital twin platform to centralize the technical information of all machinery in a single control point and manage maintenance more efficiently. In the case study of TEICON, we explain the full process in detail.
FAQs – Everything You Need to Know About Digital Twins
Are digital twins just 3D models?
Not necessarily. While they often include visual representations or 3D models, their real value lies in the ability to replicate the dynamic behavior of the asset. What makes a digital twin powerful is the intelligent layer: real-time data connectivity, data analysis, predictive insights, and process optimization.
Are digital twins only for large companies?
No. While large enterprises may gain more advantages due to bigger budgets, digital twins also create significant value for small and medium-sized businesses. They are especially useful for organizations without dedicated data analytics teams who prefer to manage information internally.
The main advantage is that they are scalable: companies can start with a single asset, machine, or process, and expand progressively to entire plants or multiple projects.
See our success stories for examples of SMEs already using digital twins for predictive maintenance and operational optimization, achieving tangible benefits from early stages.
Are digital twins difficult to use?
It depends on the solution. Our digital twin platform, TOKII, for example, has been designed to be as intuitive as possible, even for non-technical users. The interface is user-friendly, 3D environments and KPIs can be configured with simple clicks, and AI-powered predictions are completely no-code, no programming required.
What infrastructure is needed to implement digital twins?
Ideally, you need sensors and smart devices to feed the system in real time, the more complete the sensor layer, the more accurate the replica will be. Historical data is also highly valuable, as it can train predictive models and improve simulation reliability.
In cases where sufficient data is unavailable, it is possible to generate simulated scenarios as a starting point, which can then be refined with real data over time.
What are the disadvantages of digital twins?
The main drawback is the initial investment required. The cost of a digital twin depends on multiple factors. For very sophisticated projects, the investment can be high, but when aligned with a clear business need, it translates into savings, efficiency, and an ROI that outweighs the initial cost.
At IMMERSIA, we provide training and continuous support to ensure our clients always get the most value from TOKII.
For more advice or information about our digital twin software, book a personalized demo, share your needs, and we’ll help you define the best fit for your project.
