![]() You can help adding them by using this form. We have no bibliographic references for this item. It also allows you to accept potential citations to this item that we are uncertain about. This allows to link your profile to this item. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. See general information about how to correct material in RePEc.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact. When requesting a correction, please mention this item's handle: RePEc:spr:mgmchp:978-4-5_14. You can help correct errors and omissions. Suggested CitationĪll material on this site has been provided by the respective publishers and authors. Finally, we show the importance of the virtual representation of the world for telcos as well as derive a set of clear managerial implications for telco managers. Second, we highlight the necessity to connect digital twins in a secure way to enable the formation of a true virtual representation of the world as well as discuss associated challenges and key building blocks. In this article, we, first, recap the current status quo of digital twins including their evolution, benefits and challenges. As by 2020 data volumes will have reached 44 trillion gigabytes, digital twins are expected to experience a similar growth resulting in a new phase of the internet that can be described as a virtual representation of the physical world which includes digital twins of all machines, people, things and organizations. The rise of digital twins can be mainly attributed to their ability to integrate large amounts of data and to combine this data with advanced data processing methods such as artificial intelligence (AI), machine learning or high-performance computing. in the form of virtual models of wind turbines, cars, cities, factories or human organs. Recently, they have been emerging across industries and society levels, e.g. exact virtual digital representations of a physical asset, process or system, are becoming increasingly more widespread. In 2022, the global digital twins market was projected to reach USD 73.5 billion by 2027.Digital twins, i.e. The rapidly expanding digital twin market indicates that while digital twins are already in use across many industries, the demand for digital twins will continue to escalate for some time. Therefore, the industries that achieve the greatest success with digital twins are those involved with large-scale products or projects: Manufacturing projects Digital twins excel at helping streamline process efficiency, as you would find in industrial environments with co-functioning machine systems.Power equipment This includes both the mechanisms for generating power and transmitting it.Digital twins can help improve efficiency within complicated machinery and mammoth engines. Mechanically complex projects Jet turbines, automobiles and aircraft.Physically large projects Buildings, bridges and other complex structures bound by strict rules of engineering.On the other hand, numerous types of projects do specifically benefit from the use of digital models: (Keep in mind that a digital twin is an exact replica of a physical object, which could make its creation costly.) Nor is it always worth it from a financial standpoint to invest significant resources in the creation of a digital twin. Not every object is complex enough to need the intense and regular flow of sensor data that digital twins require. While digital twins are prized for what they offer, their use isn’t warranted for every manufacturer or every product created. But digital twins are designed around a two-way flow of information that first occurs when object sensors provide relevant data to the system processor and then happens again when insights created by the processor are shared back with the original source object.īy having better and constantly updated data related to a wide range of areas, combined with the added computing power that accompanies a virtual environment, digital twins are able to study more issues from far more vantage points than standard simulations can-with greater ultimate potential to improve products and processes. For example, simulations usually don’t benefit from having real-time data. The difference between digital twin and simulation is largely a matter of scale: While a simulation typically studies one particular process, a digital twin can itself run any number of useful simulations in order to study multiple processes. Although simulations and digital twins both utilize digital models to replicate a system’s various processes, a digital twin is actually a virtual environment, which makes it considerably richer for study. ![]()
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