Mikkel Stein Knudsen and Jari Kaivo-oja:
With Digital Twins, organisations can not only create mirrors of real-world objects and processes but integrate physical and virtual worlds through bidirectional flows of information. With real-time simulations and intelligent algorithms, Digital Twins shifts the focus of data-driven operations from ex-post monitoring to ex-ante predictions and optimization in increasingly complex environments. Digital Twins are said to revolutionize the manufacturing industry, but it may also have major impacts on future studies and the general ways organisations anticipate the future. A Digital Twin development can be a new advanced form of scenario planning.
Finland Futures Research Centre takes part in the project Manufacturing 4.0 (2018–2020) for the Strategic Research Council at the Academy of Finland. Our contribution includes technological foresight related to the identification of promising technologies for the future manufacturing landscape in Finland. The concept of digital twins has featured heavily in our early scanning, as one of the most enterprising new advanced manufacturing technologies.
As the embodiment of Cyber-Physical Systems, Digital Twins has become one of the most hyped technologies of the so-called Industry 4.0. According to a comment in Nature in September 2019, “Digital twins – precise, virtual copies of machines or systems – are revolutionizing industry” (Tao & Qi, 2019). Many major companies already use digital twins, while half of all corporations may use them by 2021 (Gartner). For example, in Finland, Nokia is focusing strongly on Digital Twin challenges.
Digital twins are part of a vast shift in the world’s economy towards ‘mirror worlds’ as a new dimension of human life based on and fuelled by data. The emergence of these mirror worlds will bring about a distinct economy, and require new markets, infrastructure, institutions, businesses, and geopolitical arrangements, according to a recent special report in The Economist.
What are Digital Twins?
In its original form, a Digital Twin is the virtual model of a process, product or service, or ‘a digital representation that mirrors a real-life object, process or system’ (Panetta, 2018). While no general and precise definition of the features and scopes of Digital Twins has been reached (Cimino et al., 2019), a consensus appears of certain characteristics distinguishing ‘real’ Digital Twins from related virtual replications.
Kritzinger et al., 2018 operate with concepts of Digital Models, Digital Shadows, and Digital Twins (see figure 1). What sets Digital Twins apart is the real-time automatic dataflow in both directions between the physical and digital objects.
Talkhestani et al., 2019 elaborates this further including four necessary features in their definition of a Digital Twin: (1) A Digital Twin has to be a digital representation of a physical asset, including as realistic as possible models and all available data on the physical asset. (2) The data has to contain all process data, acquired during operation as well as organizational and technical information created during the development of the asset. (3) A Digital Twin has to always be in sync with the physical asset. (4) It has to be possible to simulate the Digital Twin of the behavior of the physical asset.
Fig 2. The conceptualisation of Digital Twin.
Summarily, as seen in Figure 2, we conceptualize digital twins as possessing five constitutive features separating them from other virtual models: They need to have counterparts or equivalents in the physical world (be it a person, an organ, a product, a machine, a traffic system, or an organisational environment/infrastructure). They need to provide a fair (= as precise as possible) representation of its equivalent characteristics. They must be updated automatically and continuously (in real-time, or close-to-real-time). It must be possible to simulate the environment of the real-world counterpart on the digital twin (achieving what Qi et al. deem ‘integration between entity DT and scenario DT’). Finally, there must be synchronization directly from the digital twin to its counterpart, so that the physical asset can also mirror new directions or alterations happening virtually to the digital twin.
Digital twins in systems theory and modeling
Until now, digital twins can roughly be separated into two categories (Zhidchenko et al., 2018). Either, they assist with the analysis of very complex systems (like transportation systems), or they provide real-time analysis of relatively small systems (like a vehicle). For complex systems, digital twins are means of providing a safe space for simulation of various potential developments and impacts. The idea of digital twins is thus born out of being a (much cheaper) virtual replacement of the identical twin spacecraft NASA always produced to have suitable test spaces. By using digital twins, it becomes possible to perform simulations using the real-time characteristics of their physical counterparts, even while these are in operation. This has enormous potential to limit operational risks, as well as for optimization issues.
However, the distinction between the two categories of digital twins is increasingly blurred. Digital twins for real-time analysis is being applied to more and more complex systems. If future digital twins, as seems likely, acquire functionalities – enabled by artificial intelligence and machine learning – which allow them to interact with other digital twins in a ubiquitous environment, even digital twins of simple systems will themselves be entities forming complex systems.
Personal Digital Twins
Digital twins are also moving out of the manufacturing halls and on its way into, well, you. Signals regarding digital twins for health are already appearing manifold. Last month, The Economist reported on ambitious cardiac-research plans to create digital twins of human patients’ hearts. Digital twins are also potentially important enablers for personalised medicine through “high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient” (Björnsson et al., 2020).
In Finland, novel ideas of personal digital twins also appear in the context of the national AuroraAI-programme, which aims for Finland to ‘enter the AI age in a human-centric and ethically sustainable way’. A YouTube-video from the projects illustrates how you can ‘Let your digital twin empower you’. Futurist Osmo Kuusi has been a key contributor to development (cf. Kaivo-oja et al., 2019). Various technologies in the EU Foresight report “100 Radical Innovation Breakthroughs for the Future – The Radical Innovation Breakthrough Inquirer” indicate the high technical feasibility of digital twins.
Finding Finland’s niches
Manufacturing 4.0 believes Finland has the potential to be a leader in the Digital Twin-revolution, e.g. by developing and applying Digital Twins in strong niche markets. Department of Futures Technologies at the University of Turku has developed world-class digital models utilizing virtual reality/augmented reality, now commercialised through the spin-off company CTRL Reality. Researchers demonstrated their model of a virtual forest at the Finland Futures Research Centre’s Futures Fair in December 2017; this model was recently highlighted in the journal Nature calling for the world to Make more digital twins.
Other projects in Finland related to national strengths involve the environmental impacts of mining and mobile cranes. Usage of Digital Twins is also high on the agenda for the development and modernizations of ports in Pori and Rauma and as an element of the further take-off of the Robocoast-cluster. In Turku, digital twins are integral to the vision of creating a Smart and Wise Turku. Smart City Digital Twins is a major global research and investment area, and Finnish pioneer cities like Turku lead the way. Relying on a massive amount of data collected at high-speed from millions of sensors, there are finally also clear links between the rollout of digital twins and new demands for 5G and 6G networks.
What Digital Twins mean for futures studies?
The rapid trajectory of Digital Twins links with the disciplines of foresight and futures studies in several ways. First, it is in itself a trend to follow, analyse and speculate about the consequences of. It is also an embodiment of megatrends such as digitalisation, personalisation, and altered human-machine interactions.
At the same time, the very idea and definition of a digital twin as a vehicle of simulating the future makes digital twins an inherently futures-related technology. Futures studies-researchers and foresight practitioners must learn to utilize this as an important new tool in their toolbox. As one of its founding ideas, digital twins can provide a “safe simulation environment” to test future novelties, new products, new services, new organizational structures, new medicines, and smart infrastructures. This safety-oriented futures approach may be the most desirable way to use and apply digital twins systems thinking, and it could develop into a great futurist tool. How digital twins can supplement the identification of weak signals would seem like another fruitful avenue of investigation for future futures research.
Simultaneously, the theoretical baggage of futurists and system thinkers may be useful in shaping the field of digital twins. At present, the field seems primarily occupied by engineers and technology optimists, who may not always be aware of potential blind spots in their simulation models. A push might be needed to functionally integrate weak signals and wild cards into the simulations of digital twins. This will be another great topic for futures researchers in the years to come.
Mikkel Stein Knudsen
Project Researcher (M.Sc., Pol. Science), Finland Futures Research Centre, Turku School of Economics, University of Turku
Research Director, Finland Futures Research Centre, Turku School of Economics, University of Turku.
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The project ‘Manufacturing 4.0’ has received funding from the Finnish Strategic Research Council [grant number 313395]. The project “Platforms of Big Data Foresight (PLATBIDAFO)” has received funding from the European Regional Development Fund (project No 01.2.2-LMT-K-718-02-0019) under a grant agreement with the Research Council of Lithuania (LMTLT).
References and additional information:
Cimino, Chiara et al. (2019). “Review of digital twin applications in manufacturing”. Computers in Industry, 113. DOI.
Grieves, Michael & Vickers, John (2017). “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems”. In Kahlen, FJ. – Flumerfelt, S. & Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems. Springer, Cham. DOI.
Kaivo-oja, Jari et al. (2019). ”Digital Twins Approach and Future Knowledge Management Challenges: Where We Shall Need System Integration, Synergy Analyses and Synergy Measurements?”. In Uden, L. – Tinh, IH. & Corchado, J. (eds.) Knowledge Management in Organizations. KMO 2019. Communications in Computer and Information Science, 1027. Springer, Cham. DOI.
Kritzinger, W. et al. (2018). ”Digital Twin in manufacturing: A categorical literature review and classification.”. IFAC-PapersOnLine, 51(11), 1016–1022. DOI.
Lu, Y. et al. (2020). “Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues.” Robotics and Computer-Integrated Manufacturing, 61. DOI.
Qi, Q. et al. (2019). “Enabling technologies and tools for digital twin”. Journal of Manufacturing Systems, in press. DOI.
Saracco, Roberto (2019). “Digital Twins: Bridging Physical Space and Cyberspace”. Computer, 52(12), 58–64. DOI.
Talkhestani, B.A. et al. (2019). ”An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System.” at – Automatisierungstechnik, 67(9), 762-782. DOI.
Tao, Fei & Qi, Qinglin (2019). ”Make more digital twins”. Nature. DOI.
Zhidchenko, V. et al. (2018). ”Faster than real-time simulation of mobile crane dynamics using digital twin concept”. Journal of Physics: Conference Series, 1096.
Warnke, Philine – Cuhls, Kerstin – Schmoch, Ultich – Daniel, Lea – Andreescu, Liviu – Dragomir, Bianca – Gheorghiu, Radu – Baboschi, Catalina – Curaj, Adrian – Parkkinen, Marjukka & Kuusi, Osmo (2019). 100 Radical Innovation Breakthroughs for the Future – The Radical Innovation Breakthrough Inquirer. Foresight-report. European Commission. Brussels. DOI.
Cover picture: Pixabay.com