How digital twins are enhancing manufacturing

Manufacturers are increasingly turning to digital twin technology to beef up operations and improve their supply chains. 

Digital twins technically date back to at least the 1960s — when NASA was experimenting with virtual models during the Apollo program. Thanks to increasingly powerful sensors and software packages in recent years, manufacturers can now take advantage of the technology, too, and create live digital models of their products, machinery and even their manufacturing floors.

The idea behind digital twins is to save money and time. With these virtual simulations, manufacturers can experiment with new product designs, plan factory expansions and trial different machinery parts by using digital recreations first, which is often easier and faster, said Gary Fedder, a professor of electrical and computer engineering at Carnegie Mellon University.

“One can do basically virtual experiments to understand: How do you make the factory more efficient?” Fedder said. “The reason that's super important is that usually translates into a lot of money.” 

Once implemented, these systems can have enormous benefits, including assisting with preventative maintenance, improving factory efficiency and even boosting cybersecurity, according to the National Institute of Standards and Technology.

Digital twins can also improve a company’s supply chain operations by allowing manufacturers to test different digital representations of equipment layouts, as well as model different production outputs based on changes in demand, said Patrick Penfield, a supply chain practice professor at Syracuse University.

Implementing one of these systems is an investment. Manufacturers may need detailed measurements of different parts if they’re using digital twins to design products, according to Fedder. If a digital twin is meant to represent an assembly line, a manufacturer may need a wide array of sensors and corresponding network to gather data from the real-life assembly line.

Leading companies put digital twins to use

A growing number of companies, including Microsoft and IBM, are now selling digital twin software packages to help manufacturers implement this approach. But the cost of installing one of these systems can start in the hundreds of thousands, Penfield said, and there can be a long process of trial and error before the system works properly. 

Lockheed Martin has started using digital twin technology to model parts used on the wing lines of its F-35 planes, said Jan de Nijs, who works on digital twins as a fellow at the company. Right now, parts arrive in standard sizes, but the “real” size needed for installation can vary — which can create problems during assembly. The company is now using digital representations of these pieces to simulate assembly and speed up the process of measuring parts individually.

That approach can also reduce the need for metal shims, which are used to fill in loose space between various components that don’t perfectly fit together. 

“We’ve proven that the fundamental technology could work and that could really, really speed up assembly. People complete assemblies together quicker, more efficiently, and more to our customers’ specifications,” said de Nijs. “The benefits are humongous if we can get this done.”

The company is also using digital twins for other applications, like test flying vehicles and programming its robots, de Nijs added. 

There are other ways to deploy the technology, like tracking for abnormalities in a production line and improving cybersecurity protections. Bosch, the appliance maker, uses Microsoft’s digital twin technology to track the performance of its electric motors. And General Motors has worked with GE to use a digital twin to mimic factory floors and test new technologies. 

There’s also growing interest in using the technology to support supply chain operations. Kraft Heinz, for example, is using digital twins to improve its supply chain and — through automation — modulate production priorities more efficiently. At a factory in Brazil that manufactures laundry, Unilever is using digital twin technology to replace certain physical trials of new product formulations, while also using machine learning to predict how orders should be allocated and transported. 

But there are still challenges — especially for applying the technology to the supply chain. 

“The issues on the lack of adoption by many manufacturers is upfront cost of buying the software,” Penfield said. “The complexity of the software itself and finding people who can manage and operate this type of technology within manufacturing processes.”

This should get easier, Fedder explained, as suppliers move to standardize the software and measurements they use to represent their parts and factory equipment virtually. This will allow companies to better understand how all the aspects of their operation fit together, and eventually, build an even richer digital twin. 

“There is a lot of bespoke work in developing the core of the digital twin, really the modeling and then also taking data,” Fedder said. “You need a few brainiacs to do this right now. The goal is: downstream, can we make this as easy as using a cell phone?”

As more manufacturers turn to digital twins, the pharmaceutical sector has been weary of signing up.

In the next two to three years, 31% of manufacturers plan to adopt the technology, which can be used to monitor operations in real-time and run simulations for workflow changes and other facility improvements, according to Gartner analyst Alfonso Velosa.

However, some stakeholders in the pharmaceutical industry, which thrives off of innovation to develop new drugs and patient solutions, are hesitant to integrate virtual representations of their assets without clear cybersecurity standards. 

Moving forward, experts say that digital twin adoption in the space should depend on how companies plan to use the technology, and how comfortable they are accepting the risks that may come with integration. 

Digital twins’ double-edges complicate adoption 

At biotechnology and bioenergy consulting firm Stealth, co-founder Agustin Llamas is helping pharma companies to navigate adopting new technologies — including digital twins. 

Concern over cybersecurity, Llamas said, is one of the main reasons why the technology has not been widely adopted in the sector. Companies are hesitant to upload their data to a central platform, like a digital twin, as well as possibly share that information with a software vendor. 

“What we have in the sector is really high proprietary value,” he said. “So it’s really difficult to get these companies to share such advancements. Being able to implement this process comes with a lot of input — machines cannot think by themselves.”

For example, if a company owns a patent to produce a certain drug, the intellectual property behind the entire manufacturing process — from how its compounds are extracted to how they are combined to create the medicine — is needed for a digital twin model to run simulations, Llamas said. 

“You’re in a really sensitive ecosystem,” he added. “I think it’s going to be more effectively used in other sectors like alternative proteins or especially ingredients, which are a little less sensitive.”  

However, Purdue professor of advanced manufacturing Nathan Hartman believes the potential for a security breach should not automatically hinder digital twin adoption.

The data behind a digital twin can be secured in a similar manner to how companies protect other sensitive data, Hartman explained. 

The only caveat, he noted, is that companies across the industry are still grappling with how to secure all the digital technologies on their shop floors.

Nearly half of critical manufacturers across the globe face a significant risk of a data breach as companies increasingly rely on automation, which tethers their cyber resiliency to how secure connections are between a web of applications and connected parts.

“The network structures, the routers, the servers, all the various sensors that people seem to be enamored with that are being put on various machines, they have to connect to something,” Hartman, who also serves as co-executive director of the Indiana Next-generation Manufacturing Competitiveness Center, said.

“You have to take the time to configure them, adjust the security settings and make sure they are behind your firewall. There are a lot of technologies being developed right now and not a lot of commonality in terms of software [and] security protocols," he added.

A lack of standard cybersecurity procedures for integrating a stream of new tech leads to hesitancy when it comes to deploying technologies like digital twins, Hartman said. 

But the risks digital twins present don’t necessarily outweigh the benefits, especially for companies producing over-the-counter or off-patent medicines, according to Llamas.

“If you’re going for a generic, then definitely it can add value to the process,” Llamas said.

Some of the biggest advantages of using digital twins are improved quality control and optimizing the drug production process, he said. This includes reducing waste, energy use and even costs by identifying new compounds for use in drug production that could be cheaper.

In general, Hartman suspects a lot of companies are integrating digital twins into their operations without a specific goal in mind for using the platform.

“I do think there’s a risk to people in manufacturing where they are trying to do something because all of their competitors are doing it,” he said. “It’s not a one-size-fits-all approach.”

In a 60,000-square-foot warehouse on Wichita State University's campus, several of the country's biggest tech companies have set up shop to create next generation manufacturing innovations.

Established in June 2022 and led by Deloitte, The Smart Factory focuses on both training young engineers and working with a host of clients to solve production and process challenges using solutions developed and advanced onsite.

The project is based around an ecosystem of more than 20 partners to collaborate on smart production processes. Founding partners include Siemens, Dragos, Amazon Web Services, SAP, Infor and Wichita State.

The site is an example of the partnerships universities, consultants, manufacturers and others are forging to bring forth new technologies and benefit the industry.

Work at The Smart Factory is centered around advancing eight use cases in smart manufacturing, including factory asset intelligence/performance management, engineering collaboration/digital twins, plant consumption and energy management, quality sensing and detection, factory synchronization and dynamic scheduling, augmented workforce efficiency, smart conveyance and smart warehousing solutions.

External companies can then visit the factory and explore solutions that can be integrated into their own operations, an in-depth process that takes up to eight weeks of planning, said Ken Hall, Siemens program director and digital enterprise solutions architect who is stationed at the factory. Visiting companies include those from aerospace, food and beverage, electronics and soon semiconductors.

As part of the factory, the company and Deloitte operate a dedicated digital twin R&D space. One of the space's projects is the creation of a digital twin of the factory's in-house manufactured smart Rover education kits.

The Rover kits are later donated to area schools, but Siemens first created the digital models to demonstrate the use of digital twins in practice to simulate how to improve its system before making any physical changes, an application it also shares with companies looking to use the same solution at their own sites, Hall said.

As another way to showcase the use of 3D modeling, The Smart Factory created a digital twin of its own facility and is developing another to simulate its energy usage, Hall added.

Digital twins are a key part of The Smart Factory's work given their many applications for companies, whether to make improvements on the shop floor to boost efficiency, improve quality control or to better monitor their onsite energy usage, said Brian Meeker, leader of Deloitte's U.S. product engineering and development practice.

But while the use of 3D models is becoming an increasingly common tool used during R&D design processes, Meeker noted that fewer companies have integrated the technology more holistically throughout the manufacturing process to become a model-based enterprise throughout engineering and manufacturing.

Digital twins can be used to model each machine on a shop floor and simulate manufacturing processes and use cases, he added. Such integrations are ones that companies can learn how to undertake at The Smart Factory.

"And the big leap when I integrate all this data together, and I become one of those few companies that have models used for both engineering and manufacturing, I can now launch a product virtually," Meeker said. "And I can see exactly what the product's going to be and virtually simulate the whole manufacturing process to make sure I'm not only going to build it but I'm going to build it flawlessly.”

The recent influx of new manufacturing projects announced in the U.S. over the past year also gives many companies a chance to integrate new technologies and capabilities into a site, said Bill O’Neill, head of Siemens’ Digital Enterprise Center of Competence, Americas. Companies building new sites are also being pushed to use solutions like digital twins to help them produce goods faster and more efficiently.

“Greenfields represent an amazing opportunity for a company because they have a chance to really do things in ways they might always have dreamed of and wanted to do, but have been limited by the infrastructure and legacy equipment systems,” O’Neill said. “ ... We have the capability and the resources across the ecosystem to really sit down and workshop how to plan a facility for ultimate flexibility or other key drivers.”

Mercedes-Benz is partnering with tech company NavVis to create digital twins of its manufacturing plants.

The cloud-based factory images and floor plans will allow the automaker to share information between planning, assembly or facility management teams to simulate production line changes or other process improvements, according to a press release.

The digital replicas allow Mercedes-Benz employees and partners to take measurements and mark important locations, acting as the "first touchpoint for all the people involved in factory planning," NavVis CEO and co-founder Felix Reinshagen said in a statement.

"We are constantly working on improving our production lines, but to do that you have to know what the plant currently looks like," Oliver Geissel, head of digitalization of production planning at Mercedes-Benz, said in the announcement.

"Now, our employees and partners can navigate through the virtual version of the plant and easily perform tasks such as simulating new layouts and optimizing available space from their devices — without ever stepping foot inside the actual plant,” Geissel said.

Founded in 2013, Germany-based NavVis operates in four countries, including Germany, the U.K., China and the U.S.

Mercedes-Benz is joining other automakers in using digital twins to enhance manufacturing operations.

Last year, BMW announced that it would create a digital twin of each of its plants worldwide, also partnering with NavVis to complete the digitization process.

Tesla, meanwhile, is known to create a digital twin of every car it manufactures. In addition, the EV maker has used digital twins to create digital models of power plants and model the recreation of the electrical grid in the age of alternative energy sources.

Many of the world's biggest tech companies are getting involved in the semiconductor manufacturing to improve the production process, enhancing speed, accuracy and data integration. 

Germany-based tech conglomerate Siemens is bringing its industry knowledge to the intersection of digital twins and semiconductors, developing hardware and software solutions that work with chipmakers from design ideation through to the final manufacturing process. This includes work with both semiconductor manufacturers, as well as chip equipment makers.

Alan Porter, vice president of electronics and semiconductor industry strategy at Siemens Digital Industries Software, spoke with Manufacturing Dive about how the company is using digital twins to enhance semiconductor production processes and push the industry forward. 

Editor’s note: This interview has been edited for length and clarity. 

MANUFACTURING DIVE: What are some of the ways Siemens is using digital twin technology to enhance semiconductor production?

PORTER: When it comes to semiconductors and we think about smart manufacturing, using technologies, advanced technologies that we provide, we can really drive digitally the planning and scheduling, mechanical and thermal stress manual management, also assembly manufacturing and planning. 

And we can also drive assembly data analytics, site qualification, assembly process and operations, even advanced package manufacturing, closed loop quality, work-in-process management, and lock dispatch, manufacturing execution of the recipe and also traceability. 

We have technologies that can drive all those different aspects that are important to the manufacturing process of the semiconductor. And we can do it in a smart digital way by managing the data and by processing the data and analyzing it. 

The other important aspect of it is providing the traceability around that information, that data, that IP and metadata, and having that traceability be secure and also understanding and making sure that we have data provenance. 

When you think about the security aspects of this, you want to make sure that you have a digital footprint of your [integrated circuits] . . . You can go back as far as raw materials. You want to know where your materials came from. You want to know if they've come from the right sources or if they've been corrupted, that they come from socially acceptable sources, that you can trace your sustainability metrics, all those things come into play.

Why has Siemens chosen to prioritize digital twin research and development, what does it see as valuable in the tool?

PORTER:  The reason why Siemens is interested in this is because we see it as a challenge both internally and externally. We have our own factories, so we have experience in the large amount of data that's involved with the twin digital twin methodology and being able to process that and optimize that. And we see our customers having those same issues in their digital transformation journey.

What are the difficulties in having a manual versus digital system and how you're helping semiconductor teams progress into the digital realm? 

PORTER: In today's world, our customers, the semiconductor manufacturers, need to be able to be more nimble. They need to be able to change up their floors, their capabilities, because the [integrated circuits] are being designed and manufactured at a much more rapid pace. They need to make sure that they have zero defects. 

The more manual processes you have, the more you slow down things and the more you have areas where you can introduce errors or quality problems. So each step you take to alleviate that in a digital sense, both by transferring information digitally and also, by analyzing it, you're really optimizing for those things.

How does the industrial metaverse interact with digital twins and the work that Siemens is doing?

PORTER:  The metaverse is providing a platform where you can tie into more technologies in the ecosystem. Perhaps [if] you want to do visualization at a different level so you can interact with platforms that provide additional visualization capabilities. You want to be able to monitor things [in] real time throughout the world, both visually and from a metric standpoint. 

And also in terms of interoperability and user interfaces, leveraging the metaverse allows you to utilize augmented reality and other other types of AR-VR applications to be able to do functions that previously you could only do with a physical presence.

What are some other ways you'd like to see semiconductors and digital twins to advance chip production moving forward?

PORTER: As we work with the digital twins in the semiconductor space, we're looking to be able to optimize our semiconductors from the standpoint of power utilization, density, materials, because the world is consuming more and more data and it's becoming more and more connected. And that's requiring semiconductors to reach different boundaries from a physical aspect. 

Also the aspects of how much data can be processed, how much memory is available, just even AI itself, has been pushing those limits and then when you take autonomy into the equation and you're dealing with semiconductors using sensors, you know improvements in that space are increasing exponentially. 

And without this visualization, without this digital twin, you're in a world where you just can't handle things manually and you're not going to get the throughput you need to advance.