What is Digital Twin?

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, AI and analytics are optimized.

It acts as a bridge between physical and digital worlds by using sensors to collect real-time data about a physical item. This data is then used to create a digital duplicate of the item, allowing it to be understood, analyzed, manipulated, or optimized.

The technology behind digital twins has expanded to include large items such as buildings, factories and even cities, and some have said people and processes can have digital twins, expanding the concept even further.


The idea first arose at NASA full-scale mockups of early space capsules, used on the ground to mirror and diagnose problems in orbit, eventually gave way to fully digital simulations.

But the term really took off after Gartner named digital twins as one of its top 10 strategic technology trends for 2017 saying that within three to five years, “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.”

Other terms used to describe the technology over the years have included virtual prototyping, hybrid twin technology, virtual twin, and digital asset management.

In essence, a digital twin is a computer program that takes real-world data about a physical object or system as inputs and produces as outputs predications or simulations of how that physical object or system will be affected by those inputs.

How Digital twin works?

A digital twin begins its life being built by specialists, often experts in data science or applied mathematics. These developers research the physics that underlie the physical object or system being mimicked and use that data to develop a mathematical model that simulates the real-world original in digital space.

The twin is constructed so that it can receive input from sensors gathering data from a real-world counterpart. This allows the twin to simulate the physical object in real time, in the process offering insights into performance and potential problems. The twin could also be designed based on a prototype of its physical counterpart, in which case the twin can provide feedback as the product is refined; a twin could even serve as a prototype itself before any physical version is built.

The process is outlined in some detail in this post from Eniram, a company that creates digital twins of the massive container ships that carry much of world commerce – an extremely complex kind of digital twin application. However, a digital twin can be as complicated or as simple as you like, and the amount of data you use to build and update it will determine how precisely you’re simulating a physical object. For instance, this tutorial outlines how to build a simple digital twin of a car, taking just a few input variables to compute mileage.

Use cases

Digital-twin business applications are found in a number of sectors:

● Manufacturing

The area where rollouts of digital twins are probably the furthest along, with factories already using digital twins to simulate their processes, as this case study from Deloitte.

● Automotive

Digital twins are made possible because cars are already fitted with telemetry sensors, but refining the technology will become more important as more autonomous vehicles hit the road.

● Healthcare

The sector that produces the digital twins of people we mentioned above. Band-aid sized sensors send health information back to a digital twin used to monitor and predict a patient’s well-being.

Digital Twins and IoT

The explosion of IoT sensors are part of what makes digital twins possible. And as IoT devices are refined, digital-twin scenarios can include smaller and less complex objects, giving additional benefits to companies..

Digital twins can be used to predict different outcomes based on variable data. This is similar to the run-the-simulation scenario often seen in science-fiction films, where a possible scenario is proven within the digital environment. With additional software and data analytics, digital twins can often optimize an IoT deployment for maximum efficiency, as well as help designers figure out where things should go or how they operate before they are physically deployed.


  • Increased reliability and availability.
  • Reduced risk
  • Lower maintenance cost
  • Improved production
  • Faster time to value

Future of Digital Twin

Where they offer new and remarkable possibilities is at the organizational level in the built environment. Implementing them in hospitals or commercial real estate buildings, for instance, offers the potential to create beneficial outcomes not only for building administrators or owners but also for the people inside of those buildings. In this way, they can be used to take a people-centric approach (starting with people) then looking at problems and context, and finally adding IT systems and connected devices to try to solve big problems and create long-term value.

For companies and organizations that already use IoT, digital twins are the next step along the digital journey. They can be used to improve efficiencies, optimize processes, detect problems before they occur, and innovate for the future. If your organization is interested in producing not only better business outcomes, but also better outcomes for everyone, digital twins are worth exploring.





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