What Are Digital Twins?

A digital twin is essentially an avatar of a physical object in the virtual world, allowing people to operate it remotely, manage it, and monitor its performance by collecting data from embedded sensors. Digital twins technology is a core element of digital transformation. 

The concept of digital twins can be traced back to NASA’s efforts with its space exploration project in the 1960s. This technology took a step further in 2002 when Michael Grieves publicly introduced the concept and model of the digital twin at a Society of Manufacturing Engineers conference in Troy, Michigan. In 2010, NASA’s John Vickers introduced a new use of the term digital twin that was not confined to manufacturing industries but applied across all sectors, including construction, engineering, transportation, and aerospace.

Also see: What is Artificial Intelligence 

Digital Twins Definition

A digital twin is a 3D digital representation of a physical object. Digital twins are virtual replicas of physical devices, infrastructures, or buildings that can control, monitor and analyze their operation, performance, and wear and tear. These models can also be used to simulate the future state of a system before actually making any changes, so any potential outcomes can be predicted.

Digital twins allow users to make more informed decisions about when to replace or upgrade parts, reduce downtime, or even avoid it altogether by providing proactive notifications for preventative maintenance.

They have been proven helpful for maintenance and predictive analytics in fields such as utilities, transportation, healthcare, construction, retail, medicine, manufacturing, agriculture, infrastructure management, automotive, aerospace, and defense industries.

Manufacturing companies most popularly use them to make production processes more efficient by managing operations in both the virtual and real worlds. These twins work together to improve maintenance efficiency, uptime, and performance.

For example, Lockheed Martin uses digital twins to track its spacecraft, planes, helicopters, and rockets. By using sensors to collect data about these products as well as through algorithmic modeling of their behavior over time, digital twins allow for diagnostic, predictive, and prescriptive analytics in the context of operational excellence.

So instead of waiting until the machine breaks, these models can identify potential problems before they happen, saving money on unplanned repair costs, labor, downtime, and lost productivity.

Also see: The Future of Artificial Intelligence

How Do Digital Twins Work?

A digital twin is an artificial intelligence (AI)-powered 3D model of a physical asset created to provide insight into the performance of the corresponding physical object. It does this by aggregating all data about the object.

You can then analyze this data and decide what needs to be fixed or improved. Digital twins also can predict potential failure before it happens, so you can take preventative measures to avoid any issues before they happen. With a digital twin, companies can better understand the operational status of their assets and make smarter decisions based on insights from the data.

As each company is unique, there’s no one size fits all approach to creating a digital twin for them. One thing remains constant, though: The initial step is always obtaining access to as much data as possible about the physical asset. The more detailed, the better.

From there, create a 3D model of that asset to use as the basis for building the digital twin. Once the model is done, install various sensors and collect multiple data points.

If a decision needs to be made about which sensor would best suit your business, try to match its attributes with those that exist on similar models. Doing so will help ensure you don’t end up with information gaps in your digital twin.

Also see: Top Digital Transformation Companies

Types of Digital Twins

Digital twins can monitor, analyze, and predict the future behavior of an entire production system or individual asset performance. These digital models can then simulate various scenarios to make strategic decisions on how best to operate an asset. There are four types of digital twins.

Component or parts twins

Component twins or parts twins refer to the digital representation of individual parts of a system or machine. It’s the basic form of digital twins and simulates the smallest example of a functioning component to maintain data about its condition, use, and performance.

Asset twins

Asset twins or product twins are a virtual simulation of the interoperability of two or more individual components. For example, a data facility has many pieces that all need to be working for the facility to function optimally.

A digital twin could allow you to simulate the interactions between these different components and see if one part is disrupting the functionality of another within this simulated environment.

System or unit twins

System twins, also known as unit twins, are a virtual representation of multiple systems assets’ interoperability. These twins consist of data that relates to the whole system or product and its individual components.

With system twinning technology, you can keep tabs on how your products behave over time. This allows engineers to detect anomalies in the way these products interact with each other and make any necessary adjustments.

Process twins

Process twins are digital representations of systems working together to create an entire production facility. They are not limited to physical objects; they help map out what goes on in the business world and provide data that can be used to predict potential problems before they happen.

Process twins represent different parts of a business, including machines, workflows and employees. Process twins allow companies to proactively identify risks and problem areas, leading to increased efficiency throughout all levels of the company. They help businesses identify bottlenecks, increase throughput, and cut costs.

Also see: Data Analytics Trends 

Benefits of Digital Twins

The benefits of digital twins can be immense for any business. They allow for more efficient operations, faster response times, better demand forecasting, more cost-effective maintenance, rapid innovation, improved safety, and increased employee collaboration.

Even a small company can take advantage of the benefits that come from digital twins. However, it is essential to recognize that not every industry will have the exact needs for digital twins as other industries might have.

• Digital twins enable companies to optimize their operations on a granular level.
• It helps businesses be more efficient in optimizing the performance of their assets, resources, and personnel performance.
• They also help organizations save money by predicting problems before they happen.
• They give managers insight into critical KPIs that have historically been difficult to measure or monitor because there was no way for an organization to know what its equipment is doing 24/7.
• It also offers a visualization of real-time data, which means you can see events unfold as they happen and respond accordingly.
• It provides employees access to operational data, so they know what’s happening at any given time, leading to better communication among employees about potential issues and opportunities.

Use Cases and Examples of Digital Twins

Manufacturing

Digital twins have been incredibly beneficial to manufacturing companies using 3D printing for decades. The best part is that the process is almost entirely automated with the help of artificial intelligence (AI).

In manufacturing, a digital twin provides a virtual representation of the modeled real-world objects or machines. If the AI-powered system detects an issue, it will automatically notify an engineer about what’s happening, so they can fix it before anything goes wrong.

Healthcare

Digital twins in the healthcare sector monitor the usage and maintenance of medical equipment like oxygen tanks, x-ray scanners, CT scanners, MRI machines, and dialysis equipment. Digital twins also help predict future equipment requirements based on the load on current equipment.

For example, suppose the digital twin detects that a piece of equipment has started malfunctioning due to aging. In that case, it will signal engineers accordingly, and they would switch over to another device while repairing the malfunctioned one.

Supply chain

The supply chain industry could use digital twins to gain insight into its logistics system. A digital twin can be a virtual map of warehouses, ports, and distribution centers. Virtual mapping lets organizations know where products are at any time in the supply chain, so they can make more informed decisions.

These include knowing which warehouse is the most efficient to ship from, determining which order should come first when processing shipments, ensuring safety standards are met during transportation, and identifying risks along the way.

Building  and construction

Construction sites across the globe are equipped with smart devices that measure everything from temperature, humidity, lighting levels, and air pressure. With data generated by these devices collated together in a digital twin simulation environment, builders now have access to comprehensive information about building designs and materials long before construction starts.

Retail

Digital twins are used in retail for customer modeling and simulations to improve customer experience. Customer data collected through digital twins gives retailers valuable information on shoppers’ habits and enables brands to create tailored marketing campaigns.

The Future of Digital Twins

In the future, digital twins will become commonplace in all industries. Enterprises will use them to make smarter decisions, reduce risk, improve efficiency, and provide consumers with a better experience.

According to Allied Market Research, the global digital twin industry is valued at approximately $6.5 billion annually, and it’s projected to reach $125.7 billion by 2030, growing at a compound annual growth rate (CAGR) of 39.48% from 2022 to 2030. This data reflects the immense potential of this technology as more companies leverage its capabilities.

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