Digital twin refers to a digital replica of physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of things device operates and lives throughout its life cycle. Definitions of digital twin technology used in prior research emphasize two important characteristics. Firstly, each definition emphasizes the connection between the physical model and the corresponding virtual model. Secondly, this connection is established by generating real time data using sensors.
|“A Digital Twin is an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin”||Glaessgen & Stargel, (2012)|
|“Coupled model of the real machine that operates in the cloud platform and simulates the health condition with an integrated knowledge from both data driven analytical algorithms as well as other available physical knowledge”||Lee, Lapira, Bagheri, an Kao, (2013)|
|“digital twin is a real mapping of all components in the product life cycle using physical data, virtual data and interaction data between them”||Tao, Sui, Liu, Qi, Zhang, Song, Guo, Lu & Nee, (2018)|
|“a dynamic virtual representation of a physical object or system across its lifecycle, using real-time data to enable understanding, learning and reasoning”||Bolton, McColl-Kennedy, Cheung, Gallen, Orsingher, Witell & Zaki, (2018)|
|“Using a digital copy of the physical system to perform real-time optimization”||Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017)|
|"A digital twin is a real time digital replica of a physical device"||Bacchiega (2017)|
Digital twins integrate artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. A digital twin continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position. This learning system, learns from itself, using sensor data that conveys various aspects of its operating condition; from human experts, such as engineers with deep and relevant industry domain knowledge; from other similar machines; from other similar fleets of machines; and from the larger systems and environment in which it may be a part of. A digital twin also integrates historical data from past machine usage to factor into its digital model.
In various industrial sectors, twins are being used to optimize the operation and maintenance of physical assets, systems and manufacturing processes. They are a formative technology for the Industrial Internet of things, where physical objects can live and interact with other machines and people virtually. In the context of the Internet of things, they are also referred as "cyberobjects", or "digital avatars".
- 1 Examples
- 2 Manufacturing industry
- 3 Healthcare industry
- 4 Automotive Industry
- 5 The characteristics of digital twin technology
- 6 Future directions for new players
- 7 Related technologies
- 8 References
An example of how digital twins are used to optimize machines is with the maintenance of power generation equipment such as power generation turbines, jet engines and locomotives.
Another example of digital twins is the use of 3D modeling to create digital companions for the physical objects. It can be used to view the status of the actual physical object, which provides a way to project physical objects into the digital world. For example, when sensors collect data from a connected device, the sensor data can be used to update a "digital twin" copy of the device's state in real time. The term "device shadow" is also used for the concept of a digital twin. The digital twin is meant to be an up-to-date and accurate copy of the physical object's properties and states, including shape, position, gesture, status and motion.
A digital twin also can be used for monitoring, diagnostics and prognostics to optimize asset performance and utilization. In this field, sensory data can be combined with historical data, human expertise and fleet and simulation learning to improve the outcome of prognostics. Therefore, complex prognostics and intelligent maintenance system platforms can use digital twins in finding the root cause of issues and improve productivity.
Examples of industry applications:
- Aircraft engines
- Wind turbines
- Large structures e.g. offshore platforms, offshore vessels etc.
- HVAC control systems
- Utilities (Electric, Gas, Water, Waste Water Networks)
The digital twin is disrupting the entire Product Lifecycle Management (PLM), from manufacturing to service and operations. Nowadays, PLM is very time consuming in terms of efficiency, manufacturing, intelligence, service phases and sustainability in product design. A digital twin can merge the product physical and virtual space. Besides, the digital twin enables companies to have a digital footprint of all of their products, from design to development and throughout the entire product life cycle. More specifically, industries that are being highly disrupted by digital twins are industries with manufacturing as an important component. In the manufacturing process, the digital twin is like a virtual replica of the near-time occurrences in the factory. Thousands of sensors are being placed throughout the physical manufacturing process, all collecting data from different dimensions, such as environmental conditions, behavioural characteristics of the machine and work that is being performed. All this data is continuously communicating and collected by the digital twin.
Due to the Internet of Things, digital twins have become more affordable and could drive the future of the manufacturing industry. A benefit for engineers lays in real-world usage of products that are virtually being designed by the digital twin. Advanced ways of product and asset maintenance and management come within reach as there is a digital twin of the real ‘thing’ with real-time capabilities.
Digital twins offer a great amount of business potential by predicting the future instead of analyzing the past of the manufacturing process. The representation of reality created by digital twins allows manufacturers to evolve towards ex-ante business practices. The future of manufacturing drives on the following four aspects: modularity, autonomy, connectivity and digital twin. As there is an increasing digitalization in the stages of a manufacturing process, opportunities are opening up to achieve a higher productivity. This starts with modularity and leading to higher effectiveness in the production system. Furthermore, autonomy enables the production system to respond to unexpected events in an efficient and intelligent way. Lastly, connectivity like the Internet of Things, makes the closing of the digitalization loop possible, by then allowing the following cycle of product design and promotion to be optimized for higher performance. This may lead to increase in customer satisfaction and loyalty when products can determine a problem before actually breaking down. Furthermore, as storage and computing costs are becoming less expensive, the ways in which digital twins are used are expanding.
When looking more specifically on a firm-level, several incumbent firms are investing in a digital twin increase effectiveness. Some of these firms are: General Electric, Arctic Wind, and Mechanical Solutions Inc.
Firstly, General Electric has a system based on digital twins and uses this software to administer and analyze data from wind turbines, oil rigs and aircraft they produce. The system they use for aircraft collects, per engine, all data of a flight between London and Paris. The data is transferred to a data centre, where it generates a real-time digital twin of each engine. In this way, General Electric is able to detect potential defects or faults already during the flight. So, if a part of the engine is causing a fault, the personnel that is responsible for maintenance can have the replacement part ready at the airport where the aircraft will land.
Secondly, Arctic Wind, a firm that owns and operates multiple wind power plants in Norway, wanted a solution to keep track of the health of their wind turbines they produce. These turbines are costly, and all parts require constant monitoring. The maintenance of these turbines is challenging because of long periods of darkness and low temperatures. To find a solution against the elements, they have installed sensors on all their wind turbines and the data that comes from these sensors is transported over 1,000 miles away to the office. This provides the digital twin real-time data of the wind turbines, therefore employees are able to visualize any problems as they happen. In addition, the digital twin provides the firm with future prognoses, so they can run simulations of how the turbines will perform under different extreme circumstances. In this way, Arctic Wind knows if they have to shut down operations during an extreme storm or not.
Lastly, Mechanical Solutions Inc. (MSI), a company which specializes in turbomachines, used Siemens STAR-CMM+ software. This software enables product development organizations to make use of a digital twin. MSI successfully implemented this software in their process chain as a troubleshooting and design tool. This enabled the cost-efficient engineering process to solve very complex problems, they could not have solved without the digital twin.
Embedded digital twin
Remebering that a definition of digital twin is a real time digital replica of a physical device, manufacturer are embedding digital twin in their device. An example di I.R.S. srl that is using this technology in HVAC testing field. Proven advantages are improved quality, earlier fault detection and better feedback on product usage to product designer.
Healthcare is recognized as an industry being disrupted by the digital twin technology. The concept of digital twin in the healthcare industry was originally proposed and first used in product or equipment prognostics. With a digital twin, lives can be improved in terms of medical health, sports and education by implying healthcare in a more data-driven way. The availability of technologies makes it possible to build personalized models for patients, continuously adjustable based on tracked health and lifestyle parameters. This can ultimately lead to a virtual patient, with detailed description of the healthy state of an individual patient and not only on previous records. Furthermore, the digital twin enables individual’s records to be compared to the population in order to easier find patterns with great detail. The biggest benefit of the digital twin on the healthcare industry is the fact that healthcare can be tailored to anticipate on the responses of individual patients. Digital twins will not only lead to better resolutions when defining the health of an individual patient but also change the expected image of a healthy patient. Previously, ‘healthy’ was seen as the absence of disease indications. Now, ‘healthy’ patients can be compared to the rest of the population in order to really define healthy. However, the emergence of the digital twin in healthcare also brings some downsides. The digital twin may lead to inequality, as the technology might not be accessible for everyone by widening the gap between the rich and poor. Furthermore, the digital twin will identify patterns in a population which may lead to discrimination.
Looking more specifically on a firm-level, several incumbent firms are investing and developing healthcare solution with the digital twin. Firstly, Philips came up with the idea of a digital version of yourself. Digital twins offer so much opportunities to track the status of physical systems and devices, so why not manage your own health by tracking the status and create the ability to act preventive instead of reactive.
Secondly, ‘The Living Heart”, a collaboration between Stanford University and HPE where multi-scale 3D models of the heart were created to monitor circulation and to virtually test medications, which are still in development in order to ultimately prevent harmful side effects. Lastly, Siemens has developed a similar digital health twin. By making use of artificial intelligence, doctors can make more precise diagnoses.
The costs of developing a digital twin are seen as a considerable investment. By this, nowadays only incumbent firms have the resources to develop this. However, by making use of a cloud-based platform and a modular organization, it may also be possible for smaller organizations to contribute to a certain module. One of those organizations is Sim & Cure, which is the first company to market a patient-based simulation model for treatment of aneurysms. This treatment allows prediction of deployment of medical devices. Their product Sim&Size is an implant composed of three applications used to heal patients of neurovascular disorders such as aneurysms.
Another industry that has been disrupted by digital twin technology is the automobile industry. Digital twins in the automobile industry are implemented by using existing data in order to facilitate processes and reduce marginal costs. Currently, automobile designers expand the existing physical materiality by incorporating software-based digital abilities. A specific example of digital twin technology in the automotive industry is where automotive engineers use digital twin technology in combination with the firm’s analytical tool in order to analyze how a specific car is driven. In doing so, they can suggest incorporating new features in the car that can reduce car accidents on the road, which was previously not possible in such a short time frame 
Firm-level Dynamics (Volkswagen & Tesla)
One of the incumbent automobile firms that is incorporating digital twin technology in their business processes is Volkswagen. The use of this technology, which they refer to as a “virtual twin”, has allowed Volkswagen to create digital 3D prototypes of their different car models, such as the Golf. The Pre-Series Center in Wolfsburg is the specialized department of the Virtual Prototype team, where they put together digital representations of the vehicles, that are used from the point of assembly and throughout the lifecycle of the cars. The digital twins support the production process and development of the cars, by providing all employees worldwide with detailed and real-time data of the model. Leingang, one of the leaders of the Virtual Prototype team, describes how the implementation of digital twins helps Volkswagen to optimize their product lifecycle management. "Our work helps people in design, quality assurance, body construction, and assembly. (...) This is because the ‘digital twin’ lets our colleagues know early on what exactly needs to be done when mounting a particular component.”. At another innovative department in Wolfsburg, Volkswagen’s Virtual Engineering Lab, further develops the use of digital representations and digital tools in combination with Augmented Reality. Here they make use of the Microsoft Hololens, which enables engineers and designers to view and modify digital twins, assisted by other technologies such as gesture control and voice commands.
Unlike incumbents in the automobile industry that have been wrapping digital technologies around their traditional products in the last couple of years, relatively new player Tesla has been involved with (digitally) innovating the industry from the moment the firm entered the market. Besides stimulating the switch towards the adoption and use of electric vehicles by the mainstream, Tesla has been innovating vehicles by implementing software-based tools in the physical product, including digital twin technology. Tesla creates a digital twin for every electric car they manufacture, which provides the firm with a constant stream of data going from the vehicle to the manufacturing plant and vice versa, allowing Tesla to increase the reliability of the car by predicting any kind of maintenance from a distance. The digital nature of Tesla vehicles, enables the firm to resolve most maintenance issues remotely, by using the received data of the digital twin, for example, “if a driver has a rattle in a door, it can be fixed by downloading software that tweaks the hydraulics of that particular door”. Tesla continues to develop and update their software and other digital technologies, in order to sustain their status of a successful innovator.
Comparing the strategies of these two well-known automotive firms, it seems that Volkswagen has implemented digital twin technology as an offensive reaction to Tesla’s innovative approach to the automotive industry. By transitioning to the new technology, Volkswagen is showing that it prefers to ‘rise to the challenge” and frame this threat as an opportunity by creating a new, specialised department for virtual prototyping rather than fleeing to either a new market or a niche.
The characteristics of digital twin technology
According to Tuertscher (2018), digital technologies have certain characteristics that distinguish them from other technologies. These characteristics, in turn, have certain consequences. Digital twins possess some of these characteristics, namely connectivity, homogenization, they are reprogrammable and smart, modular, and they leave digital traces.
One of the main characteristics of the digital twin technology is its connectivity. The recent development of the Internet of Things (IoT) brings forward numerous new technologies. The development of IoT also brings forward the development of digital twin technology. This technology shows many characteristics that have similarities with the characteristic of the IoT, namely its connective nature. First and foremost, the technology enables connectivity between the psychical component and its digital counterpart. The basis of digital twins is based on this connection, without it, digital twin technology would not exist. Like described in the previous section, this connectivity is created by sensors on the physical product which obtain data and integrate and communicate this data through various integration technologies. Besides this obvious connection, digital twin technology enables increased connectivity between organizations, products, and customers. For example, connectivity between partners in a supply chain can be increased by enabling members of this supply chain to check the digital twin of a product or asset. These partners can then check the status of this product by simply checking the digital twin.
Also, connectivity with customers can be increased, for example, an organization which manufactures engines for its customers can check the digital twin of its engines to see if it still working properly and offer maintenance to its customers if needed. This last example is an example of a consequence of the connectivity of the technology, which is called servitization.
Servitization is the process of organizations that are adding value to their core corporate offerings through services. In the case of the example of engines, the manufacturing of the engine is the core offering of this organization, they then add value by providing a service of checking the engine and offering maintenance.
Digital twins can be further characterized as a digital technology that is both the consequence and an enabler of the homogenization of data. Due to the fact that any type of information or content can now be stored and transmitted in the same digital form, it can be used to create a virtual representation of the product (in the form of a digital twin), thus decoupling the information from its physical form. Therefore, the homogenization of data and the decoupling of the information from its physical artifact, have allowed digital twins to come into existence. However, digital twins also enable increasingly more information on physical products to be stored digitally and become decoupled from the product itself.
As data is increasingly digitized, it can be transmitted, stored and computed in fast and low-cost ways. According to Moore’s law, computing power will continue to increase exponentially over the coming years, while the cost of computing decreases significantly. This would, therefore, lead to lower marginal costs of developing digital twins and make it comparatively much cheaper to test, predict, and solve problems on virtual representations rather than testing on physical models and waiting for physical products to break before intervening.
Another consequence of the homogenization and decoupling of information is that the user experience converges. As information from physical objects is digitized, a single artifact can have multiple new affordances. Digital twin technology allows detailed information about a physical object to be shared with a larger number of agents, unconstrained by physical location or time. In his White Paper on digital twin technology in the manufacturing industry, Dr. Michael Grieves  notes the following about the consequences of homogenization enabled by digital twins:
“In the past, factory managers had their office overlooking the factory so that they could get a feel for what was happening on the factory floor. With the digital twin, not only the factory manager, but everyone associated with factory production could have that same virtual window to not only a single factory, but to all the factories across the globe.” (Grieves, 2014, p. 5)
Reprogrammability and Smart
Another important characteristic of the digital twin technology is its reprogrammable nature. As stated earlier, a digital twin makes it possible to make remote adjustments through the digital component of a twin. It enables a physical product to be reprogrammable in a certain way. Furthermore, the digital twin is also reprogrammable in an automatic manner. Through the sensors on the physical product, artificial intelligence technologies, and predictive analytics, digital twins can adjust themselves automatically in order let the physical product be up to date and work as good as possible. A consequence of this reprogrammable nature is the emergence of functionalities. If we take the example of an engine again, digital twins can be used to collect data about the performance of the engine and if needed adjust the engine, creating a newer version of the product. Also, servitization can be seen as a consequence of the reprogrammable nature as well. Manufactures can be responsible for observing the digital twin, making adjustments, or reprogramming the digital twin when needed and they can offer this as an extra service.
Another characteristic that can be observed, is the fact that digital twin technologies leave digital traces. These traces can be used by engineers for example, when a machine malfunctions to go back and check the traces of the digital twin, to diagnose where the problem occurred. These diagnoses can in the future also be used by the manufacturer of these machines, to improve their designs so that these same malfunctions will occur less often in the future.
The final characteristic that we can define, is the characteristic of modularity. Modularity is particularly important in the manufacturing industry. In the sense of the manufacturing industry, modularity can be described as the design and customization of products and production modules. By adding modularity to the manufacturing models, manufacturers gain the ability to tweak models and machines. Digital twin technology enables manufacturers to track the machines that are used and notice possible areas of improvement in the machines. When these machines are made modular, by using digital twin technology, manufacturers can see which components make the machine perform poorly and replace these with better fitting components to improve the manufacturing process.
Future directions for new players
The technology of a digital twin creates several opportunities for new players that want to tap into this emerging market. Since the technology can be brought into a more operational framework, not only the incumbents can exploit the opportunities that arise. In the following part we discuss two start-ups that try to exploit the opportunities that emerge around this new technology.
A key challenge that arises around digital twin is how firms can implement the technology easily, given the fact that most firms have investments in legacy systems. Some questions that arise are: who will operate and manage the digital twin? How can we assure that the digital twin communicates with the existing ERP software and other applications? For example, SWIM claims that their technology (EDX) focusses on overcoming these challenges by using a lot of data to create a digital twin that can learn from the real world. Furthermore, SWIM mentions that implementing the technology does not require a new infrastructure within the company and there is no need for new skills. SWIM seems to have created a solution for companies who do not have enough resources to create a digital twin themselves with EDX. Unlike the big players as Microsoft, Dell, and Google who are part of a vendor ecosystem, EDX is not part of such a platform, which could be a disadvantage for SWIM.
Another opportunity that is exploited by a new player is the reduce of risks associated with cost and schedule overruns in the industry of capital projects. By creating a digital twin of a construction site, VEERUM enables project teams to be more sure in making decisions about design and construction of the project. VEERUM enables project teams to see the progress of the construction of a building on a working site. Due to the fact that they have a virtual version of the working site, they can predict and solve issues in the virtual environment before they occur in the real world. These issues could be noted before they will actually happen, and a lot of costs and issues with schedule overruns could be prevented.
Lastly, a digital twin can also be used to optimize urban sustainability by capturing the temporal and spatial implications. As a virtual replica of a certain city, a digital twin allows city operators to develop different strategies to deal with problems beforehand by implementing the best possible solution. Several cities such as Singapore and Jaipur are already implementing the digital twin. However, it is expected that many more cities will make use of this technology in the future.
- Finite element method
- Discrete event simulation
- Internet of things
- Industry 4.0
- Health and usage monitoring systems
- Integrated vehicle health management
- Digital workplace
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