In its report ‘‘Future Disrupted: 2021 technology trends’, the IT service provider NTT mentions digital twins as one of the major technology trends of 2021. In its report ‘Top strategic technology trends for 2021’, research firm Gartner considers it as one of the emerging technologies.
Definition
According to Gartner, a digital twin is the virtual copy of any object made dynamic by the data coming from this object. Consequently, it is not a simple copy, but a real ‘double’ which can be adapted in real time.
Various research firms (including Gartner and IDC) estimate the global digital twin market between 13,9 billion dollars in 2022 and 15,6 billion dollars in 2023; the maturity of the technology should be reached around 2027. These firms consider that companies which will be investing in digital twins, will see their critical processes improve by circa 30%.
Initially, the digital twin was implemented in Industry 4.0 in order to reproduce often very expensive equipment (such as in the aerospace industry). Subsequently, this technology has been applied to predictive maintenance, product design, the optimization of production processes, agriculture, construction, the hospital environment and even smart cities. Nowadays, the emergence of the Internet of Things (IoT) is giving this technology a new boost.
The continuous supply with field data not only allows to improve the ‘real’ twin, but also to anticipate its evolution over time, and in particular the risks of failure or wear; all of this at a (significantly) lower cost. Moreover, the use of algorithms associated with machine learning allows to perform simulations in near real-time, as the processing power available in the cloud enables unmatched performances.
Design and process
Furthermore, progress in the field of artificial intelligence and machine learning, associated with the processing of large data volumes, has opened up new opportunities for the technology, especially since competition is forcing companies to adapt ever faster, to reduce their costs and to anticipate the market evolutions. To put it briefly: they are being forced to innovate within the framework of new business models.
This is why the digital twin can thoroughly modify the development cycle of a product or process. Indeed, rather than conceiving this product or process before testing it and subsequently applying the necessary modifications or improvements to the physical product, the digital twin can emerge as a clone which can be edited continuously and in real time, without interfering in the operational process. This allows us to understand, anticipate and optimize its performance without having to modify the original item.
With the digitalization projects currently running within companies, the digital twin takes an important place in the development strategy of organizations, since such a twin can serve as a field of experimentation without jeopardizing the global functioning. Moreover, it will make the organization more agile and adaptable.
Challenges
However, this approach induces a fundamental culture change within the organization, which should be collaborative and flexible if it wants the innovation to take on its full dimension. In addition, the notion of continuous learning must be present, while all internal or external players (customers or suppliers in particular) must be involved in the process. We will also need a maximum of data sources which can be used to feed the twin and to acquire artificial intelligence and machine learning technologies in order to process and enrich these data.
And if the technology becomes widespread, it would be ideal to have a certain level of standardization (a common development language in order to create twins or coherent data models), which would allow the different players to participate in their adjustment and improvement, and the technology integrators to collaborate more closely.
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