Digital Twins: Driving Value for the Metaverse

Today, the world of artificial intelligence (AI) seems to focus on the buzz around the Metaverse a little less. Is it because ChatGPT and large language models have made such an enormous splash, and the Metaverse is taking a backseat in these tech discussions? Or is the use in the business world not going to be as prevalent as once anticipated? Trey Norman from Mindbreeze ponders on how digital twins can aid the implementation of the Metaverse.

In December 2021, Bloomberg predictedOpens a new window the Metaverse revenue opportunity could reach nearly $800 billion.

If it were to reach close to that immense number, it would have to be from the gaming and entertainment industry and not through typical business users to enhance everyday business processes and tasks. 

With that said, there is still tremendous opportunity for it to be used as an industrial solution for the manufacturing industry. Digital twin technology would be a significant driver of primary use across manufacturing. Digital twin technology is making substantial headway today. There are numerous reasons – the ability to quickly simulate business scenarios and processes, develop solution strategies, and identify and implement optimization options. 

In April 2023, Meticulous Research, a research partner for market intelligence needs, completed an exclusive reportOpens a new window projecting the digital twin market to reach $183 billion by 2030 at a compound annual growth rate (CAGR) of 41.6% during the forecast period 2023-2030.

What Are Digital Twins?

Digital twins are virtual representations. Digital twins can be counterparts to numerous things, such as physical objects, virtual environments, or processes. 

Virtual replicas can give businesses tons of knowledge from the information they provide without much risk. For example, instead of developing expensive prototypes and running through lengthy test chains, companies can develop services in the Metaverse even before they are demanded by customers and manage existing resources such as materials, time, and personnel in line with requirements.

Understanding why a mishap happened during the production or testing phases in seconds is highly cost-efficient for a business. No matter the size or value of what a company is making, technology accelerates the product’s performance. In addition, it allows companies to monitor each part of their operation successfully.  

So, would you do it if you could have virtual counterparts to all your assets and accurately predict how processes may go or how you can make a product better before putting in actual time and labor on the real object? Many companies are saying yes, which is why the market is expected to skyrocket over the next few years.

Just as large language models, like ChatGPT, are the present and future of generating content from deep learning techniques, digital twins will be the same for smooth manufacturing operations from simulation.

See More: The Metaverse is Here…But is the Hardware Ready?

Use Cases of Digital Twins as an Industrial Solution

Now that we have uncovered the basis of digital twin use let’s look at some real-world industry use cases.

Improving tire performance with Bridgestone

Bridgestone is the global tire and rubber manufacturing leader. They are heavily invested in digital twins to test and understand factors related to speed, driving performance, and safety in dangerous road conditions. Using simulation, Bridgestone can improve the entire design of their tires without putting a single hand on a piece of machinery – increasing longevity and performance. Bridgestone is also capable of cutting down the development time with the ability to test their tires in a simulated and quicker environment. Decreasing the time spent designing, testing, and prototyping means new products are brought to market quicker. In addition, Bridgestone has seen benefits by identifying areas to be more sustainable during the manufacturing process. Digital twins help the company realize where waste can be reduced. 

Deloitte Insights speaks of Bridgestone’s digital twin use case in an article,Opens a new window “Bridging the physical and digital.”

Digital windfarms with GE

One of the most popular companies using digital twins today is General Electric (GE). 

The story publishedOpens a new window on the renewable energy section of GE’s website shares,

“The Digital Wind Farm starts with the digital twin, a cloud-based model of a wind farm. With digital twin technology, engineers can mix and match up to 20 different turbine configurations to make sure they are building the best wind turbine possible for the real-life location of the farm. Once the physical wind turbine is installed, the digital twin model can really get to work—collecting and analyzing data from its real-life counterpart, and providing suggestions to make it even more efficient. The concept of the digital twin software has been used for GE gas turbines, as well, with great results.”

See More: Why CEOs Want Future-proofing With Cloud-based Solutions

Other Benefits: Predictive Maintenance and Fast Fixes

Digital twins are made up of data from numerous sources and consolidated so users can see a full 360-degree view of whatever object or process they may be monitoring or enhancing. Departmental data, machinery records, along with sensory data can all be used to generate a digital twin. The full breadth of data can help companies predict machine failures or other slowdowns in the supply chain. This brings in the term “predictive maintenance,” all made possible by digital twin technology. Design, testing, and prototyping are just three areas of the production process. 

How about when workers are using complicated machinery to actually build the product? Businesses must avoid major slowdowns so machinery stays running, their bottom line isn’t impacted, and the factory floor remains safe and productive. Rather than checking on machinery during scheduled periods or leaving it up to the worker to report a problem, digital twins make needed maintenance predictable.  

Once the need for maintenance is determined, the worker can use digital twins to see components and interact with them before going in and physically making the fix. Digital interaction allows workers to be confident, safe, and accurate when handling machinery. Digital twins give them a clear path to successful maintenance so other workers can quickly continue building the product. 

Overall, the Metaverse can be seen as a “digital twin of the world.” Companies must begin developing digital twins for widespread use to operate productively in the Metaverse and see its value. For all the reasons highlighted, digital twin technology holds the chips for the Metaverse to play a successful role in 2023 and beyond.

We’d love to hear your thoughts on how digital twin technology could enable success for the Metaverse. Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window !

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