Digital Twins in Product Development

Digital twins are quickly becoming a game-changer for manufacturing operations across industries all over the world. This is in large part down to the increasingly connected nature of factories powered by the Internet of Things (IoT), which allows devices to share real-time data with their virtual counterparts and vice versa. Read on to learn more about digital twins, how they work, and the implications this technology has for product developers moving forward.

Digital Twins in Product Development

Although digital twin technology itself has been around for decades, it was only in the past couple of years that mainstream manufacturing, retail, and healthcare began to really explore its applications. To give you an idea of how this emerging technology is blowing up: the global digital twin market is predicted to be worth USD 106.26 billion by 2028 alone. Meanwhile, Accenture predicts that digital twins will be one of the top five strategic tech trends to watch in 2022.

What are digital twins?

A digital twin is a virtual representation of something that exists in the real world. This thing can be a physical object, process, or service; you just need to be able to apply sensors to it so that data can be sent back and forth between the real object and its virtual counterpart.

Digital twins can replicate a huge variety of real-world objects, ranging in size from millimeters to kilometers. In other words, you can create a digital twin of something as small as a human organ, but also of something as big as an entire city. 

For example, GE uses digital twin technology to create a “Digital Wind Farm”, or a cloud-based model of a wind farm. Using historical data relating to the wind patterns of the area in question, these digital twins help engineers come up with the best possible turbine configurations based on the conditions at the wind farm.

Another real-life example would be “Virtual Singapore”, a living model of the city-state itself designed to help make strategic decisions when it comes to urban planning and city management, as well as improving energy consumption.

Related reading:

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Where do digital twins come from?

Some credit David Gelernter with coming up with the concept of digital twins in his 1991 book “Mirror Worlds”. Others claim that it was Dr. Michael Grieves who popularized the term when he presented it at a Society of Engineers conference in 2002.

However, the use of digital twin technology actually dates back as far as the 1960s, when NASA used basic digital twin models to simulate and assess conditions in space. They regularly created complex systems on the ground which matched their space counterparts. 

One of the most famous examples is the digital twin they created to mirror the conditions aboard Apollo 13, which came in handy when the oxygen tanks on Apollo 13 malfunctioned early on in the mission and NASA was able to test possible solutions on the ground using the digital twin.

Types of digital twins

Generally speaking, there are four types of digital twins. Which one you will use depends on how complex the real-life object, system, or process is.

  1. Parts twin: This is the most basic unit of digital twinning which allows engineers to evaluate how one part or component will react in certain scenarios.
  2. Asset twin: Next, we have a replica of the entire product which shows how all the different parts work together and what engineers can do to improve performance. This means less prototypes are needed, speeding up iterations significantly.
  3. System twin: This level of digital twin allows you to see how various assets work in unison, giving you a birds-eye view of how they interact with each other and where improvements can be made.
  4. Process twin: Finally, process twins show how all the units collaborate with each other in the whole production facility and what can be done to streamline the process

Digital twins vs. simulations

Among many other functions, digital twins help product developers to:

  • Evaluate a design or prototype
  • See how a product or process will react under certain circumstances
  • Create a product before it is physically built
  • Devise and observe product life cycles

You may think this sounds like a simple simulation, and they’re certainly similar concepts. The main difference here is a question of scope. While a regular simulation usually considers one specific process, a digital twin is an entire, rich, virtual environment, so it encompasses many different simulations to study them as a whole.

Simulations don’t typically run from real-time data either. Meanwhile, digital twins operate on a cycle of data that flows to them from object sensors and back out again when the insights generated are shared back with the original object.

Applications of digital twins

Many industries are already using digital twins, and the possibility of applications are endless. Here are a few examples to give you an idea:

  • Manufacturing: Digital twins are used to represent a product’s entire lifecycle, guiding product development efforts all the way from conception to retirement. 
  • Automotive: Digital twins are already very popular in automotive design and are used to streamline production efficiency and vehicle performance.
  • Healthcare: Healthcare companies are increasingly exploring the use of digital twins to optimize diagnosis, treatment planning, drug development, device production, facility management, and more.
  • Disaster management: In order to combat the effects of climate change and global crises, digital twins help organizations and governments to create better emergency response plans and improve infrastructure.
  • Urban planning: Digital twins can help cities make strategic planning decisions, troubleshoot complex challenges, and become more sustainable as a whole.

Benefits of using digital twins

There are several key benefits and advantages when it comes to using digital twins in product development. Depending on the sector, some will be more vital than others. We’ve highlighted our top three here:

  • Streamline R&D
Digital twins help organizations research and design products much more efficiently using a constant feedback loop and huge amounts of performance data. These actionable insights speed up prototyping and enable companies to make any necessary changes before they even get to production.
  • Reduce maintenance and downtime costs
The average cost of an hour of downtime across businesses can go up to approximately $260,000 per hour. With digital twins (and IoT in general), engineers can use predictive maintenance to continuously monitor machines, predict issues before they happen, and devise possible solutions in advance of technician visits. 
  • Peak production efficiency
Organizations who implement digital twins tech experience a 10 percent boost in effectiveness. Digital twins provide an excellent level of visibility and transparency of the whole production process, allowing manufacturers to spot issues early on and continuously improve the way they work.

The implications of digital twin technology for product development

In addition to the benefits outlined above, forward-looking organizations will be investing in digital twin technology in order to stay a step ahead of their competition. Simply put, the ability to test multiple scenarios and innovate without disrupting day-to-day business operations is priceless and gives companies a real competitive edge.

Find out how digital transformation is affecting modern product engineering! Access our eBook:

Digital Transformation in Product Engineering

According to research conducted by Gartner, thirteen percent of organizations that already have IoT projects in place are using digital twins, and 62 percent are in the process of setting them up. Benoit Lheureux, research Vice President at Gartner elaborates, 

“We predicted that by 2022, over two-thirds of companies that have implemented IoT will have deployed at least one digital twin in production.”

In order to be able to take advantage of digital twin technology, organizations will need the IT infrastructure to support:

  • Huge amounts of data analysis
  • Continuous data collection
  • Data analysts who can help with the data models
  • The right software and systems to monitor the product’s full lifecycle

Up next:

The Role of Next Generation ALM in the Future of Product Development

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