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Digital Twins: Bridging The Physical And Digital

CIOInsider Team

What if you had a digital replica of the living and nonliving entities to which you can collaborate virtually, predict accurate results, and understand various uncertain scenarios clearly? The concept of digital twin is no new to the technology landscape and various companies from automotive to aircraft sectors have leveraged its practical applications to optimize the manufacturing value chain process and building new products. Digital twin makes way for operations at virtual and physical world to converge in order to make the industrial product to get a dynamic digital representation. Applications of the digital twin can be predominantly placed in business sectors that help in predicting the current state and future of its assets.

Being termed as the next big thing by business

sectors across various domains, digital twins are created of any physical asset. These are done by engineers after collecting and synthesizing the data from various sources. In the contemporary era of Fourth Industrial revolution, along with automation, big data and analytics comes the limitless opportunities that could transform the traditional physical system based process to a virtual one. It is apparent that with all the huge strides in Artificial intelligence and Machine learning, the future is ready for autonomous systems. The improvements with regard to interoperability and IoT sensors, tools are getting refurbished in availing computing infrastructure. Data that can be extracted from real-time asset monitoring technologies can now be constituted into digital twin simulations and further expand the possibility of feeding data directly into them.

Though digital twins make way for developing novel systems and processes, there are extreme conditions that make it less viable to directly measure some process. There could be cases where it may not be cost effective to orchestrate the physical objects. Such cases give rise to use of proxies that can be attached to the object for capturing data. As aforementioned, machine learning capabilities, artificial intelligence and predictive modelling could possibly help manufacturing sectors in the areas of optimizing fuel consumption and other fleet management areas.

In the coming years, digital twins will purge to a level where deployment of its application will witness presence across multiple industries. Organizations that transcend and scale their venturing to sell bundled products are burgeoning in flourishing the digital twin use cases. Most of the organizations today are reluctant towards encouraging an external integration by shrinking to point-to-point connections. Towards overcoming this challenge will ignite the strength to further open up the opportunities that give access to volumes of data enough to engineer simulations in more detail.

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