Digital Twin

A Digital twin is exactly what it sounds like, basically a virtual model that have been designed to be able to in an accurate way reflect a physical object or system

Digital twin


What is a Digital twin?

A digital twin is a virtual copy of an object or system that reflects its lifecycle. The digital twin is updated from real-time data (often through sensors), and uses simulation, machine learning and logical reasoning to make good decisions.

How does a digital twin work?

A Digital twin is exactly what it sounds like, basically a virtual model that have been designed to be able to in an accurate way reflect a physical object or system. This is possible through studying the object, take a wind turbine as an example: The turbine will be outfitted with various sensors that mirrors vital areas of its functionality in the physical world. The sensors produce data on different aspects of the objects performance and report data such as for example energy output and weather conditions back to the digital copy through the processing system.

When the data is reported, the virtual model of the object will be able to run simulations and study the objects performance in order to pick up valuable insights and possible improvements that can applied back to the physical object.

The digital twin vs. simulations

Digital twins are often mixed up with simulations. Even though they both make use of digital models replicate various processes of a system there is a big difference. Since the digital twin is built to copy the virtual environment its data is considerably richer for study, and at a much larger scale. A simulation can be used to study one particular process, while a digital twin can itself run multiple simulation in order to study a number of different useful processes.

And there is more. For instance, while simulations normally don’t benefit from access to real time data digital twins are the exact opposite. They are designed to operate around a two-way flow of information, where the sensors first provide relevant data to the system processor, and secondly when the processor share the insights and improvements back with the original source object.

The conclusion is pretty clear. A digital twin has the advantage to be able to analyze more issues from far more angles compared to standard simulations, which increases its odds and ultimate potential to improve products and processes

Different types of digital twins

Depending on the level of product magnification, a number of different types of digital twins are being used. It is not unusual to have several different types of digital twins that co-exists within the same system or process. Read more about the different types of digital twins and in which areas they are being used below.

Component twins & part twins

Component twins, is defined as the basic unit of a digital twin and the smallest functioning component. Part twins are more or less the same thing as a component twin, but are normally part of less important components

Asset twins

An asset is formed when two or more components work together. An asset twin allows you to track and study the interaction in between those components, and create performance data that can be processed and used as actionable insights.

Unit or System twins

Unit or System twins provides the next level of magnification which enables you to see how different parts and assets come together in order to form a functional system. System twins provides insights of the interaction in between the assets and may suggest improvements on the performance.

Process twins

Process twins is used to reveal how systems work together as a production facility by providing a macro level of magnification. Process twins will be able to provide data on systems synchronized performances and how one system may effect others. Precise timing schemes can be determined with data provided by the process twins, in order to be able to reach overall effectiveness of the systems.

The beginning of digital twin technology

David Gelernter was the person who voiced the idea of digital twin technology back in 1991 with his publication “Mirror Worlds”. Dr. Michael Grieves (who was a faculty at the University of Michigan) was however credited for being the first, 2002 when actually applying the concept of digital twins to manufacturing when he announced the digital twin software concept. Many years later, in 2010, John Vickers of NASA officially introduced the new term called “digital twin”.

NASA may very well also have been the ones that pioneered the use of digital twins already back in the 1960’s when working on the Apollo-projects. Each spacecraft had an exact replicated earthbound version that was used by NASA to study simulate scenarios that may accord during space travel.

Advantages and benefits of digital twins

Improved research and development

A digital twin makes it possible to more efficiently research and design product due to access of massive amount of data based on likely performance outcomes. With the insights from this information, companies can make necessary tweaks and improvements before even starting the production.

Better efficiency

Although a new product has gon in to production, digital twins can assist to monitor and mirror the production systems, with a goal to achieve and maintain highest possible efficiency throughout the process of manufacturing.

Product end-of-lifecycle

Through simulating the lifecycle of a product, digital twins can also help manufacturers to decide what to do with their products in the end of the journey. Digital twins can determine which product materials that can be harvested and potentially recycled.

Digital twin industries and markets

Although digital twins are ideal for creating and designing many objects and systems, it’s just not viable for the manufacturer to necessarily use it for every product. Simply put, not all objects are complex enough to provide the digital twins with the sensor data required. It could also be costly to create a digital twin, have in mind it’s an exact copy of the physical object and it does not make financial sense for all objects.

But there are some projects that specifically benefit from using digital models:

  • Buildings, bridges and other large projects with complex structures bound by strict engineering rules
  • In manufacturing projects where digital twins can help to streamline the efficiency of the process in industrial environments when using co-functioning machine systems
  • Power equipment used for generating and transmitting power.
  • In jet turbines, aircraft, automobiles and other mechanically complex projects where a digital twin can assist in providing efficiency for mammoth engines and complicated machinery.

For those reasons, industries that benefit the most from digital twin technology are those dealing with large-scale projects or products:

  • Engineering
  • Manufacturing of automobile
  • Production of aircrafts
  • Design of railways
  • Building construction
  • Manufacturing
  • Power utilities

The digital twin market is poised for growth, and even though digital twins are already being used across many industries the demand for the digital twin technology just keep growing with an estimated market value of USD 3.1 billion in 2020. Analysts in the industry speculate that that it could climb as high as an estimated market value of USD 48.2 billion by 2026.

The digital twin future

We already see a fundamental change to current operating models, where the the digital reinvention is taking place in asset-intensive industries, changing and disrupting when integrating new technology to work along the machines. Digital twins are already playing a massive part in this realignment.

There is no limit for the demand of digital twins in the future, since they continue learning new skills and capabilities in order to generate the insights needed to make better products and more efficient processes.

Carl Wanngård, M.Sc.

Account manager & business developer
+46 731 473 320


Lukas Johansson, M.Sc.

Account manager & business developer
+46 704 466 297


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