An Emerging Technology Challenge: Digital Twins

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.

Fig. 1. From Digital Model to Digital Twin (own representation, after Kritzinger et al., 2018)

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    

Jari Kaivo-oja
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

Are we in the midst of a fourth industrial revolution? New Industry 4.0 insights from future technology analysis professionals

Mikkel Stein Knudsen and Jari Kaivo-oja:

The recent July 2018-issue of the highly influential futures studies journal Technological Forecasting & Social Change contained a special section dedicated to Industry 4.0. The issue is relevant to an increased understanding of the current trends and transformations of the manufacturing sector. Finland Futures Research Centre works with this theme in the project Manufacturing 4.0 supported by Academy of Finland’s Strategic Research Council. Discussion about Industry 4.0 is part of larger technological transformation process (Kaivo-oja et al. 2017). “Industry 4.0” was first coined at the Hannover Fair in 2011, seven years ago. All over the world, the term “Industry 4.0” has drawn great public attention from practitioners, academics, government officials and politicians. Some scientist as Reischauer (2018) see Industry 4.0 as policy-driven discourse to institutionalise particular innovation systems in manufacturing.

For use in the MFG4.0-project, and due to its general relevance, this blog post contains a summarizing review of the TFSC-special issue combined with other recent research on Industry 4.0. We hope this blog will be informative both to those already working with these themes and to those curious about the field. Awareness about Industry 4.0-strategy is an important development driver for both progressive SMEs and large corporations. Discussion in the TFSC Special Issues of Industry 4.0 underlines the idea that Industry 4.0 challenges do not hit only large corporations, and that the role of progressive SMEs and start-ups needs more scientific attention in the global Industry 4.0-process. Orchestration of innovation eco-systems requires broad networks and new dynamic capabilities in organizations.

Compared to previous Industry 1.0-3.0 revolutions Industry 4.0 revolution will include a novel and global dynamic element: The BRICS-countries will be now more active players in Industry 4.0 transformations than these countries were in previous industrial transformations. Especially the role of China in Industry 4.0-era will be a big political and economic issue (see Kaivo-oja & Lauraeus 2017a, 2017b). Industry 1.0 phase was founded on mechanisation, Industry 2.0 phase was based on electricity and Industry 3.0 phase was founded on information technology (IT) to human manufacturing. New Industry 4.0 era is expected to be founded on Cyber-Physical Systems (CPS) and the Internet of Things (IOT). Other key technologies are Cloud computing, Big Data analytics and Extended ICT.

The expected changes will lead to new integrated systems, where sensors, actuators, machines, robots, conveyors, etc. are connected to and exchange information automatically. Factories are expected to become conscious and intelligent enough to predict and maintain the machines and control the production process. Business models of Industry 4.0 imply complete communication network(s) between various companies, factories, suppliers, logistics, resources and customers. This kind of highly integrated and transparent industrial approach probably allows more efficient circular economy in the future (see de Sousa Jabbor 2018).

Both smart production and smart consumption are key benefits of Industry 4.0 approach. Industry 4.0 includes a new research agenda for sustainable business models, business model innovation and re-organization process of old supply chains of companies. Lean Industry 4.0 is expected to be a key challenge for SMEs and corporations. From this technology foresight analysis perspective, the reported technology roadmap in the computer and electronic product manufacturing industry is highly relevant reading for Industry 4.0 policy discussion (Lu & Weng 2018).

The difficult task of defining Industry 4.0

As noted, the term Industry 4.0 was coined in Germany by a government advisory council at the beginning of this decade. This origin does not seem disputed, but otherwise the definition of Industry 4.0 remains up for debate. It is notable, for example, that all articles in the TFSC-special section provide their own slightly different explanations of the term. One article (Sung, 2018) even argues that the inclusion of “4.0” in the umbrella-term refers to the fourth industrial upheaval post-WW2, while others follow the more widely used definition of 4.0 being the fourth industrial age after the age of steam, the age of electricity and the information age (Müller et al., 2018).

While exact definitions differ, common themes in the understanding of Industry 4.0 are easily distinguished. It revolves about new technologies, new digital possibilities, new modes of inter-connectivity etc. Jabbour et al. (2018) captures this by denoting four significant components of Industry 4.0: i. cyber-physical systems, ii. the internet of things, iii. cloud manufacturing, and iv. additive manufacturing. This is very similar to what Xu et al. (2018) recently described as enabling technologies in a comprehensive assessment of Industry 4.0: State of the art and future trends.

Figure 1: ‘Components’ and ‘enabling technologies’ in Industry 4.0.

Adding to the broader understanding of the concept of Industry 4.0, Müller et al. (2018) provide a qualitatively based examination of how key practitioners, representatives of manufacturing SMEs, perceive the term. This pragmatically highlights those elements of particular interest to manufacturing practitioners, and the empirical results reveal three main dimensions of Industry 4.0: (1) High-grade digitization of processes, most notably manufacturing processes, (2) Smart manufacturing through cyber-physical systems resulting in self-controlled production systems, (3) Inter-company connectivity between suppliers and customers within the value chain.

Figure 2: 3 dimensions of Industry 4.0 (adapted from Müller at al., 2018).

We believe these three dimensions would be interesting starting points for creating a refined Maturity Model of organizational Industry 4.0-readiness. This has already been attempted, see e.g. Schumacher et al., 2016, but a new model based on these three empirically backed dimensions might be both simpler and more precise. Müller et al. (2018) do not formalize a new maturity model in their article, but they do provide a four-stage model of manufacturing SMEs ranging from those deliberately not engaged (“we’ve always done things like this”) to full-scale adopters of Industry 4.0 (“we want to be the leader in our industry and can only achieve this through Industry 4.0”). Other identified firm categories were preliminary stage planners (“for us Industry 4.0 us imaginable in the next five to ten years”) and Industry 4.0 users (“more efficient usage of machines while achieving more with less employers”). Motivation level and strategic maturity level to be engaged in Industry 4.0 revolution vary much among German SMEs. Probably, in Finland we could get similar results.

Organizational responses to Industry 4.0

Through their qualitative interviews (with 68 high-level representatives of manufacturing SMEs) Müller et al. (2018) also importantly provides outlines for various strategies for adopting or not adopting elements of Industry 4.0 within business practices. We expect that this theme – identifying and exemplifying organizational Industry 4.0-strategies – will be a key future research topic for business, innovation and organizational research. Finally, the article illustrates dilemmas of smaller suppliers when the value chain become increasingly inter-connected. Increased transparency is not always in the interest of the minor companies, as pointed out by several informants in the study. This view is supported in a recent survey of UK-manufacturers, where, even if 80% of manufacturers believe that new digital technologies will improve the supply chain relationships up and down, several negative responses with fear of “supply chain bullying” can be found (PwC, 2018).

How Industry 4.0-developments affect supply chain relationships and especially affect suppliers might be a particularly pertinent research question in a Finnish context. Three-fourths of Finnish exports are intermediate goods (Ali-Yrkkö, 2017) – a share significantly higher than the EU-average – and changes (positive or negative) to the role of manufacturing supply companies can therefore have effects not only on the individual companies, but perhaps also on the national economy.

Linking Industry 4.0 with the sustainability agenda

Jabbour et al. (2018) examine links between Industry 4.0 and environmentally-sustainable manufacturing. Industry 4.0 and sustainability are argued to be two major trends of, and while they individually cannot be considered revolutionary, together then may “change worldwide production systems forever”. The technological possibilities of Industry 4.0 may help unlock the full potential of environmentally-sustainable manufacturing practices. Whether this will happen, the authors note, depend on eleven distinct critical success factors (CSF) further explained in the article. The CSF’s here are not studied empirically, but they provide research propositions for – as explicitly urged by the authors here – further examination in the synergies between two key societal and manufacturing megatrends. How to best harness these synergies should be of utmost importance to academics, policymakers and practitioners working with sustainable manufacturing and sustainable development, and we will likewise hope that the question of integrating sustainability-dimensions will occupy an important part of the Industry 4.0 research- and implementation agenda. This article together with the highly-cited contribution of Stock & Seliger (2016) provide important background material for this work.

Talkin’ Bout a Revolution?

Like Jabbour et al., Reischauer (2018) and Kim (2018) argue that “Industry 4.0” is not really an industrial revolution. Reischauer argues that, as much as signalling future changes, the particular discourse of “Industry 4.0” serves a policy-driven discourse to institutionalize a distinct now-almost hegemonic idea of innovation systems. Thus, the term itself was developed in the context of a “fluid entanglement of academia, business, and politics”, and the discourse further underpins this entanglement. The discourse hereby both exemplifies and underlines the further need for Triple-Helix Innovation modes (see e.g. Kaivo-oja, 2001, Santonen et al., 2011, Santonen et. al., 2014). It might be illuminating also to see our own MFG4.0 through this critical lens and to remind ourselves that the discourse is neither value- or policy-free.

Kim (2018) puts another critical spin on Industry 4.0. Industry 4.0 is a meso revolution needed by capitalism, because capitalism always needs ever-growing markets, and technology is just one arena for the ever-needed expansion of capitalism. Jumping from this critical view, he goes on to analyse the readiness for this particular meso revolution in South Korea, a topic also explored by Sung (2018). Perhaps surprisingly, both authors conclude that South Korea is a bad position to utilise potential opportunities provided by Industry 4.0. Finland, on the other hand, ranks second only to Singapore in a global competitiveness ranking for the fourth industrial revolution (Sung, 2018). This of course provides some ground for optimism regarding the MFG4.0-project and the general ability of Finland to capture new opportunities and benefits.

Morphological analysis for the future Industry 4.0 transformations

The Special Issue of TFSC also includes also an important methodological paper of Kwon et al. (2018). As we know the generation of new and creative ideas is vital to stimulating innovation, and morphological analysis is one appropriate innovation management method given its objective, impersonal, and systematic nature. In the Big Data-era, we can develop Industry 4.0 strategy on the basis of Big Data files, and the systematic structuring of data becomes vital for success. This methodological case study in the TFSC Special Issue focuses on Wikipedia’s case-specific characteristics using the online database for the development of morphological matrix, which incorporates the data on table of contents, hyperlinks, and categories. This provides interesting results. The feasibility is demonstrated through a case study of drone technology, and the validity and effectiveness was shown based on a comparative analysis with a conventional discussion-based approach. This methodological paper is a milestone study and requires our full scientific attention.

Japanese Industry 4.0 strategy?

Also in the Special Issue, Luo and Triulzi (2018) provide interesting insights about Japanese approach to Industry 4.0. They point out that the architecture of a firm’s network of transactions in its surrounding business ecosystem may affect its innovation performance.  A business ecosystem as a transaction network among firms has been a key issue for successful industrial cooperation in Japan.

The empirical results of Japanese study indicate that a firm’s participation in inter-firm transaction cycles, instead of sequential transactional relationships, is positively and significantly associated with its innovation performance for vertically integrated firms. Within cycles, vertically integrated firms have better innovation performances than vertically specialized firms. Vertically integrated firms that participate in cycles have the best innovation performances in the Japanese electronics sector. This empirical finding can be very relevant also for European firms and companies. The authors also underline that the organizations focusing on quality improvements and production efficiency improvements can be different organizations. Specialization in these fields may be a critical success factor in a national Industry 4.0 strategy. Only in few special cases, organizations are able to integrate these critical industrial functions in one unified organization. We can conclude that Industry 4.0 transformations need more discussions about Japanese historical Industry 1.0-4.0 know-how.

Industry 4.0 in Finland

Discussion about Industry 4.0 will surely continue. Manufacturing 4.0 consortium will contribute to this discussion in various ways and via various channels.

In April 2018, the Manufacturing 4.0 consortium provided the first ‘situation report’ for the Strategic Research Council. The report is (in Finnish).

Link to references

Mikkel Stein Knudsen
Project Researcher, Finland Futures Research Centre, Turku School of Economics, University of Turku

Jari Kaivo-oja
Research Director, Adjunct Professor, Dr, Finland Futures Research Centre, Turku School of Economics, University of Turku

 

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