What is a Digital Twin and Why it Matters?

Digital twin technology has been gaining popularity in recent years, and for good reason. In this article, I will introduce the concept of a digital twin, the benefits of using digital twin technology, and provide examples of digital twin use cases. I'll also touch on things to consider before using digital twin technology.

What is a digital twin?

A digital twin is a virtual representation of an object in real life, which spans the object's full lifecycle and uses real-time data sent from sensors on the object to simulate the behavior. A digital twin can be used to replicate a single component, or the entire equipment or system. It can even replicate the entire cities.

Below are the major benefits of using digital twin technology:

• Real-time monitoring: the data captured through the sensors can help us understand the exact status of the equipment or system and we can optimize the performance as needed.

• Predictive Maintenance: Digital twin combined with AI can help us perform maintenance better, thus reducing downtime and extend the lifecycle.

• Reduce cost and production time: we can build digital twins of a facility or system before they exist. We can work with the digital twin and run scenarios, which reduces cost and time.

What is the market potential for digital twin technology?

Per Statista, global digital twin market size is going to increase at least 10 times from 2020 to 2025 in almost all industries (see the figure below).

Global digital twin market size in the year 2020 and 2025, by industry(in billion U.S. dollars) Source: Statista

What are exciting use cases of digital twin technology?

An Entire Country: Singapore created the first country-scale digital twin in Feb 2022. Singapore is an island country with rising temperatures. Digital twin technology is expected to play a key part in Singapore’s battle for sustainability and against rising temperatures.

Colorado's Forest: NVIDIA and Lockheed Martin helped the U.S. Department of Agriculture Forest Service and Colorado Division of Fire Prevention & Control (DFPC) using digital-twin simulation to better understand wildfires and stop their spread in 2021.

Every Tesla sold: Tesla creates a digital twin for each car it sells. Sensors from the car feed into the digital twin and Tesla uses AI to interpret the data to remotely monitor whether the car is working as designed or maintenance is needed.

The Human Brain: Neurotwin, an EU funded research program, has been working on a very challenging task - creating a digital twin of the human brain! In this project researchers will develop advanced brain models to find treatments for Alzheimer's disease.

The Human Heart: NTT and Harvard announced a digital twin partnership to engineer the heart to explore the structure-function relationships that may be overlooked in cardiovascular physiology in Nov 2022.

What do we need to consider when using digital twin technology?

There are a few key obstacles that digital twin technology will need to overcome in order to reach its full potential. One of the main challenges is the need for high-quality data. In order for digital twin technology to simulate the behavior and performance of physical objects and systems accurately, it is essential that the data collected from sensors is accurate, up-to-date, and relevant. This can be a challenge, particularly in environments where sensors may be exposed to harsh conditions or where data transmission may be disrupted.

Another key obstacle for digital twin technology is the need for robust data security protocols. As mentioned earlier, digital twin technology relies on large amounts of data collected from sensors, and it is essential that this data is protected from unauthorized access or tampering. This can be a challenge, particularly in complex or distributed environments where there may be multiple parties involved in the collection, transmission, and analysis of data.

Finally, digital twin technology also faces the challenge of convincing businesses and organizations to invest in the technology. While digital twin technology offers numerous potential benefits, it also requires significant upfront investment in terms of both hardware (e.g. sensors, computing power) and software (e.g. AI algorithms, data management systems). As with any new technology, it can be difficult to convince businesses and organizations to make this kind of investment without a clear understanding of the potential return on investment.

Overall, while digital twin technology has the potential to revolutionize many industries, it still faces some significant challenges that will need to be overcome in order to reach its full potential.