What Is a Digital Twin? Definition, Types, and Uses

Key takeaways

Digital twins are virtual representations of physical objects used for modeling and design purposes. 

• Digital twins are virtual models used to digitally represent performance and design solutions to improve real-world assets.

• These models are outfitted with sensors that continuously update their virtual counterparts in real time with granular, high-quality data.

• You can gain experience with digital twins as they become more common in the work world by earning professional credentials in related fields like design.

In this article, you’ll learn more about different types of digital twins, real-world digital twin use cases, and their business benefits. Then, keep learning by enrolling in the IBM Machine Learning Professional Certificate.

What is a digital twin?

A digital twin is a virtual representation of a real-world object or system. These virtual models are used to digitally represent performance, identify inefficiencies, and design solutions to improve their physical counterparts.

Digital twin vs. simulation

Digital twins model specific real-world assets. Simulations, on the other hand, operate in entirely virtual environments divorced from the external world. The digital models are outfitted with sensors that continuously update their virtual counterparts in real time with granular, high-quality data.

Digital twin use cases

Businesses and organizations use digital models to design, build, operate, and monitor product lifecycles. Equipped with up-to-date data on physical objects, digital twins can be paired with AI and machine learning to create detailed predictive models and forecast more accurate outcomes than most simulations.

What is an example of a digital twin? 

Examples of digital twins can be found in various industries, including product development, design, manufacturing, and maintenance. Digital twin technology is expected to grow exponentially in the near future, largely due to the expansion of the Internet of Things (IoT), artificial intelligence (AI), virtual reality (VR), extended reality (ER), and cloud computing [1].

1. In manufacturing: Engineers in the automotive industry might use digital twins to model and test prototypes in different simulations, refine designs, and model and improve the supply chain and manufacturing process used to make the final product before.

2. In healthcare: Using digital twins, health-care providers can model the day-to-day duties of their staff and the availability of critical resources, such as hospital beds or ventilators, in real time to improve patient care and their overall organizational efficiency. 

3. In product development: Product designers can utilize digital wins to create virtual prototypes, iterate on features, analyze mechanical stress, and predict performance under various environmental conditions.

4. In maintenance: Technicians can monitor digital twins equipped with real-time sensor data to identify wear and tear on complex machinery, such as jet engines or wind turbines, allowing them to perform repairs proactively.

Four kinds of digital twin types 

While every type of virtual twin fundamentally does the same thing—virtually modeling a real-world object or system—their purposes and scope greatly vary from one to another. The four primary types of digital twins are:

1. Component twins:  Component twins are digital versions of an individual part, such as a gear or screw, of a system or product.

2. Asset twins: Asset twins, also called product twins, are digital versions of a physical product, rather than its parts.

3. System twins: System twins represent products working together as part of a system; they map how individual assets synchronize during production.

4. Process twins: Process twins are used to optimize the total output of an organization, examining each workflow within a business or facility.

Four benefits of digital twins 

There are as many benefits to using digital twins as there are applications for them. However, some of the most common benefits of using them include the following: 

• Lower overall costs and reduce time to market by designing, testing, and refining products or systems in virtual environments before mass production or roll-out. 

• Improve operational and engineering efficiency by modeling systems with up-to-date information, testing alterations in dynamic simulations, and ultimately implementing real-world changes. 

• Provide swift maintenance to physical assets and existing systems, such as buildings or jet engines, by continually monitoring their performance and identifying issues when they arise. 

• Improve the customer experience when purchasing a product or entering a retail outlet by virtually modeling their customer journey.

Digital twin software

Digital twins are becoming more widely adopted across many industries. To create digital twins, these industries use specific software to run the complex monitoring required. Some of the most popular digital twin software include: 

• Azure digital twin 

• IBM Maximo Application Suite

• AWS IoT TwinMaker

Keep learning with these free resouces 

Expand your knowledge on machine learning, data science, and more career paths related to digital twins with these resources:

• Subscribe to our newsletter: Career Chat

• Watch: Why I’m Learning Machine Learning on Coursera

• Read an interview with a fellow learner: Meet the 18-Year-Old Getting His Master’s Degree in Data Science

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