What is Digital Twin Technology?
Digital Twin Technology was popular long before the 2020 global coronavirus pandemic accelerated the enterprise adoption of Industry 4.0 initiatives. In fact, the global digital twin market size was valued at USD 2.26 billion in 2017 and is expected to expand at a CAGR of 38.2% from 2018 to 2025, as per Grand View Research. Consequently, businesses are eager to incorporate this technology to improve efficiency and ensure that processes are better serviced, analyzed, and maintained in real-time. So, let’s see the reason behind as to why businesses are seeking such innovative technology.
Although the concept of digital twins was first put forward by David Gelernter’s 1991 book ‘Mirror Worlds,’ with Michael Grieves of the Florida Institute of Technology, it was NASA who first embraced the digital twin concept. It was in a 2010 Roadmap Report, where John Vickers of NASA gave the concept its name. The idea was used to create digital simulations of space capsules and craft for testing.
Afterward in 2017, Gartner named it as one of the Top 10 Strategic Technology Trends stating, “billions of things will be represented by digital twins, a dynamic software model of a physical thing or system.” A year later, Gartner once again named digital twins as a top trend, saying that “with an estimated 21 billion connected sensors and endpoints by 2020, digital twins will exist for billions of things in the near future.”
The Definition and Working Methodology
According to networkworld.com, “Digital twins are virtual replicas of physical devices that data scientists and IT pros can use to run simulations before actual devices are built and deployed. They are also changing how technologies such as IoT, AI, and analytics are optimized.”
Essentially, a Digital Twin is a computer program that makes predictions or simulations of a physical object or the system by taking real-world data about that physical object or system as an input.
The life of the digital twin starts with the creation of a mathematical model that simulates the original with specialists in applied mathematics or data sciences studying physics and operating data of physical subject or system. The developers who build digital twins ensure that input from sensors that collect information from the real-world version is available on the virtual computer model. The technology may be used to provide input on a potential product or also to simulate what might happen to a physical version as it is designed as a prototype. The model can be as complicated or as straightforward as you need to decide how exactly the model simulates the actual version. As an expanded technology, digital twins have been extended to include large objects such as constructions, factories, and even cities. As a result, such a setup enables the digital version to replicate what is happening in real-time with the original version and to gain insights into results and associated problems.
Understanding the concept, businesses can implement digital twins into their various applications. These applications of Digital Twins Technology are prominent in manufacturing, healthcare, supply chain, retail, automotive, and many such industries.