While digital twins can help manufacturers improve their operations, the technology also has some drawbacks. Manufacturing leaders need to be aware of these common issues to avoid a poor ROI on the technology and other issues.
A digital twin is a virtual model of a real-world object or set of steps. For example, an automaker might create a digital twin of an assembled car part or create a digital twin of its factory. A digital twin consists of a physical version of the digital twin, a software version of the digital twin, and the data that connects them.
The use of digital twins is expected to expand across industries over the next decade. According to a Fortune Business Insights report, the global market for digital twin technology is expected to grow from $12.91 billion in 2023 to $17.73 billion in 2024, rising to $259.32 billion by 2032.
Here we discuss the benefits of using digital twins in manufacturing and the digital twin challenges manufacturers frequently encounter.
Four benefits of digital twin technology
Digital twin technology can help manufacturers improve their operations in many ways. Here are a few examples:
1. State-of-the-art operational monitoring
Digital twins provide manufacturers with more up-to-date insights into their operations.
Digital twins provide near real-time visibility into how facilities and assets are performing, says David Williamson, who previously used digital twins in IT manufacturing and is now CIO at San Diego-based life sciences company Abzena.
The physical representation of an item and its software counterpart are typically connected by IoT sensors, whose data gives users insight into the state of the physical object. For example, a user may learn through digital twin data that a piece of equipment is running slower than normal and be able to investigate the problem.
2. Better insight into employee performance
Increased visibility means supervisors have more information about how workers on an assembly line are performing, for example.
Karen Panetta, dean of graduate education at Tufts University’s School of Engineering in Medford, Massachusetts, said digital twins can enable manufacturing supervisors to identify problems in the production process.
For example, digital twin technology can share that a piece of equipment is only running every 15 minutes when it should be running every 5. A supervisor can investigate the issue and find out why an employee is late to work, improving the facility’s overall performance.
3. Scenario simulations to improve insight
Digital twin technology can also tell users how their facility will function in the future if certain changes are made.
For example, users can use digital twin elements in a warehouse to predict how robots will interact with warehouse equipment, said Shreyas Shukla, principal research director at Info-Tech Research Group, an IT consulting firm based in London, Ontario. Users can also add prescriptive analytics to the digital twin that suggest the best action to take in a given scenario.
For example, if the most important consideration in a given situation is building a product quickly, prescriptive analytics can suggest steps to take to ensure that process happens as quickly as possible.
4. Cost reduction
Digital twin technology also reduces costs for companies, as users no longer need to perform as many real-world tests with physical prototypes.
“Because we can ensure that our products meet compliance requirements and customer needs in the digital world before we build them in the physical world, [lot] “Of money,” Shukla said.
Five challenges when using digital twin technology
However, the use of digital twin technology can also introduce potential problems that manufacturing leaders need to learn about so they can prepare for and hopefully avoid them.
1. More Complex than Expected
Digital twins may seem simple to use, but some companies have found this to be different.
“[We] found [using digital twins] “The digital twin was a lot more complicated than we expected,” Williamson said of his experience with it.
For companies building new factories, processes or products, it may be easier to create a digital replica, he said, but organizations with existing IT infrastructure may need to update their existing systems before they can build a digital twin in a cost-effective way.
2. Poor data quality
Digital twins require good data to function, so poor data quality can negatively impact the operation of a digital twin.
Shukla said lack of data or poor quality data can limit or completely disable the use of digital twin technology.
Before adding digital twin technology to their operations, manufacturing leaders need to ensure data quality is optimal.
3. Customization requirements
Business leaders need to plan around the fact that digital twins need to be adapted to their organizations and their needs.
“People just want to jump in and try to do a one-size-fits-all solution, but you can’t do that,” Panetta said.[A digital twin] It needs to be customized to your purpose and goals.”
Doing so will require more work and time, so leaders need to plan for those additional expenses.
4. Exorbitant costs
Organizations may find that digital twins and the process of implementing them are more costly than leaders anticipated.
The costs associated with creating and effectively using digital twins can delay a positive ROI for many organizations.
“I think this technology will become the norm and will bring benefits,” Williamson said. “But for many people, [companies]that’s still a long way off.”
5. Lack of knowledge about proper application
Rather than viewing digital twin technology as the perfect solution for everything, manufacturing leaders should ensure that they only apply it to appropriate processes.
“One of the biggest challenges is [with digital twins] “The key is to understand what the technology is for and what it’s not for,” Shukla said.
For example, many manufacturers believe that digital twin technology can serve as a substitute for real-world testing, but that’s not correct, Shukla said.
“This is intended to complement real-world testing,” he said.
As with many other technologies, manufacturing leaders should verify the information for themselves rather than blindly trusting digital twin insights.
“If you believe [the technology] It’s definitely going to come back to haunt me,” Panetta said.
Mary K. Pratt is an award-winning freelance journalist specializing in enterprise IT and cybersecurity management.
