Unlocking Efficiency: The Rise of Digital Twin Technology

History

The core idea of digital twins—or creating a replica to mirror something—goes way back to the US National Aeronautics and Space Administration (NASA)’s Apollo program in the 1960s, wherein engineers built exact physical replicas of spacecraft on the ground to prepare for potential risks in space. One of the most well-known uses of early digital twin technology was during the Apollo 13 incident in the 1970s, when an oxygen tank exploded. NASA engineers used ground-based simulations to replicate the spacecraft’s conditions, test solutions, and help guide the astronauts safely back to Earth.

Over time, new developments were made that laid the groundwork for what would become digital twin technology. However, it wasn’t until 2002 that Dr. Michael Grieves formally defined the digital twin concept, explaining how a virtual product could mirror a physical one throughout its lifecycle. Then, in 2010, John Vickers of NASA officially coined the term “digital twin,” highlighting the power of real-time data management to improve these digital models.

Benefits

Consider digital twin technology as your business’s own dedicated virtual test lab. As part of the Industry 4.0 revolution, digital twins let you experiment with the real physical asset safely and efficiently. This technology can effectively help users with the following:

• Save costs by avoiding the need to build a physical duplicate and prevent future damages.
• Save time by identifying issues before launching a system that might fail.
• Prevent accidents by reducing risky real-world testing.
• Improve existing assets through virtual trial runs.
• Use visualizations to help explain or train users on the asset.
• Enable data-driven decisions by providing real-time insights.

Different Types of Digital Twins

While all digital twins represent a digital version of a real-life object or process, the type of twin can vary depending on its purpose or area of application. You can use just one type of twin or combine two or more, even all of them, working together within the same system for a more detailed big picture.

Generally, there are four main types of digital twinning, which are the following:

Component Twins or Parts Twins

This is the smallest and most basic type of digital twin, as it represents a single component, like a single sensor or valve in a machine. These are small but critical parts of a larger system. A Parts Twin refers to something even smaller or less central, like a shock absorber or a protective casing.

Asset Twins

This is the middle ground in terms of scale. You shift focus from a single component to the entire asset, multiple parts working together to do their job. If a Component Twin is one gear in a machine, an Asset Twin shows how the whole machine runs when all the gears work together.

System Twins or Unit Twins

Zooming out even further, System or Unit Twins show how multiple assets come together to form a fully functioning system or production line. If an Asset Twin represents a car engine, then the System Twin represents the whole functioning car. These twins help test how well all the different assets work together to achieve maximum performance.

Process Twins

This is the largest type of digital twin, as it represents an entire production facility or workflow. Process Twins check how well all the different types of twins work together to ensure peak efficiency and achieve their overall goals.

Best Use Cases

Digital twins can give nearly any industry a boost in operational excellence, from digital training and asset optimization to spotting bottlenecks through simulation. Here are some of the best examples of digital twin technology across different industries:

Manufacturing

Manufacturers work with so many different assets, that when something breaks down, a lot of time and money is wasted. As digital twins are a key part of smart manufacturing, they can monitor equipment using real-time data to catch inefficiencies early, enable predictive maintenance, and support lifecycle management. They can also accelerate workflow, support design exploration, and reduce waste generation.

Healthcare

Healthcare is an industry where even the slightest mistake can’t be afforded, which is why digital twins are such a natural fit. They can serve as virtual representations of organs, or even an entire patient’s body. Wearable sensors can scan a person in real time, ensuring that the digital twin is personalized to that individual. They can also be used to monitor patient recovery, predict the aftereffects of surgery, and train surgeons on complex procedures, without any of the real-world risks.

Construction

Working in the construction industry involves a lot of risk mitigation, equipment inspections, and multiple stakeholders, making the use of technology in construction incredibly valuable. Digital twins can serve as digital representations of physical buildings, including ones that haven’t been built yet, allowing for greater efficiency. They can also improve site development planning and support the design of digital risk assessments to better understand site conditions and potential issues.

Oil and Gas

Like construction, the oil and gas industry deals with complex equipment, supply chain management, and data analytics. By creating a digital twin for each piece of equipment, you can continuously collect data to predict future performance. You can also twin an entire pipeline system to detect potential leaks, optimize drilling speed and direction, or even create a digital version of an oil reservoir to visualize its behavior for extraction.

How Digital Twins are Made

How Digital Twins Are Made

Digital twinning can feel like a vague and complex process for any business to understand, but it’s worth the effort if you’re aiming for maximum operational efficiency.

Here are the key steps that go into creating digital twin software so you know exactly what you’re getting into:

1. Asset Selection

Define the assets you want to digitalize. Almost anything with a critical function in your business can be turned into a digital twin. You’ll also need to decide on the type of digital twin that best fits your selected asset(s).

2. Digital Creation

Develop a virtual replica of the asset you’ve chosen. This process can cost anywhere from $50,000 to $100,000, with more complex models reaching up to $500,000. There are also more affordable options for smaller tasks—some you can even download directly to your laptop—but these may lack the detailed insights that larger models offer.

3. Sensor and Data Integration

Equip your real-life asset with monitoring sensors or other Internet-of-Things (IoT) devices to collect real-time data. These are installed directly on the physical asset to transmit data back to the digital twin, keeping it continuously synchronized. Collected data can include how your asset performs in cold environments, under high levels of vibration, or other operating conditions.

4. Analytical Modeling

Use analytics, Machine Learning (ML), and simulation models to process and interpret the collected data. This is an ongoing effort that requires daily management to ensure the twin accurately reflects the real-world asset. It’s essential for your twin to become a reliable, real-time duplicate.

5. Digital Simulations

Create and personalize a digital sandbox for your twin. There are two types of simulations: present-based and future-based. Present-based simulations lets you test different conditions to improve asset performance. On the other hand, future-based simulations use historical data to predict how your asset might perform later on.

6. Autonomous Optimization

Once your twin has gathered enough data, it can start optimizing on its own with minimal input. It analyzes trends, identifies patterns, and adapts using stored data, allowing it to even upgrade itself and help you make smarter decisions faster.

Common Challenges

Every industry can benefit from digital twin technology, but there are a few bottlenecks to work through first.

One major challenge is the sheer amount of data required, as these models need to run continuously.. For example, hospitals generate around 50 petabytes of data every year. That’s why it’s important to have the right digital solutions in place to manage everything your digital twin will produce.

Another challenge is cost. Some companies may be hesitant to invest in something that still feels a bit unfamiliar. But the long-term savings, through optimized equipment maintenance, risk-free testing, and better team collaboration, can far outweigh the initial expense.

Lastly, integration is a big hurdle. Digital twins need to connect seamlessly with your existing systems, whatever your industry may be. Different data formats and standards can make it tricky to bring everything together. That’s why having an all-in-one digital solution can make the transition a lot smoother.

Digital solutions and applications can simplify data management and streamline system integration. By using a smaller digital twin model along with a platform like SafetyCulture, you can avoid common challenges and start optimizing your assets more effectively.