By Devon Maresco
Marketing Coordinator
SpaceIQ
Digital twins have become a staple in workplace governance across many sectors, but they’re rooted firmly in manufacturing. There’s an abundance of digital twin manufacturing examples that paved the way for the rise of digital asset management in other aspects. For example, a company might use a digital twin to monitor the condition of its HVAC system—a practice rooted in factory machine monitoring and maintenance.
As facility managers get familiar with digital twins, it’s important for them to look at the roots of this technology. Not only are there lessons in manufacturing that translate across industries, there are also clues about how to maximize the effectiveness of a digital twin in the face of an ever-expanding IoT.
Here’s a brief look at digital twins in manufacturing and why they broke sector barriers to become relevant far outside the factory environment.
What is digital twin in manufacturing?
A digital twin is a digital mirror of a real-world asset. In manufacturing, it’s a virtual replica of a specific machine, informed by data. This data can come from networked sensors or manual input, and when combined, provides a clear picture of the condition and history of the machine.
More than a representation of equipment, a manufacturing digital twin is vital for the opportunities it offers. According to Digitalist, the manufacturing roots of digital twins set the stage for their exponential potential:
Digital twins represent an enormous opportunity for manufacturers, including engineering, design customization, production, and operations. Digital twins are vital to improving situational awareness and allowing CIOs to test future scenarios that can enhance asset performance and proactively anticipate maintenance faults.
According to the United States Environmental Protection Agency’s Kaizen philosophy of continuous improvement, in the world of manufacturing, digital twins provide the decision-making insights factories need to run Lean. As they seek to eliminate waste, manufacturers turn to quantitative insights from digital twins. These systems are increasingly essential as part of the Kaizen philosophy.
What is an example of digital twin in manufacturing?
The best example of a digital twin in manufacturing is a piece of equipment that’s outfitted with sensors. For the sake of example, let’s say it’s a machine with an electric motor and a driveshaft, outfitted with a vibration sensor, temperature sensor, and rpm meter. These devices all feed real-time data into a digital twin of the machine. There are several ways this digital twin becomes useful.
- Real-time observation. A trigger programmed into the digital twin alerts maintenance techs if the vibration level, temperature, or rpms exceed a specific threshold. This incites real-time action to prevent long-term damage.
- Historical data. The motor suddenly fails. During a root cause analysis, the maintenance tech reviews digital twin data and sees that rpms spiked several times prior to the failure, and the temperature rose dramatically moments before failure.
- Preventive maintenance. Maintenance techs integrate the digital twin data with a CMMS platform. The CMMS schedules routine service based on average component lifespans and manufacturer-recommended service schedules.
These are just simple, practical examples of digital twins in manufacturing. Modern factories have much broader, more complex integrations that range from better machine maintenance practices to value stream monitoring.
Examples of digital twin in manufacturing
The more sensors and other data inputs there are to feed a digital twin, the more accessible insights become. In the factory environment, they lead to a bevy of lean manufacturing advantages:
- Reduced waste. More insight into machine operation helps to create initiatives that reduce total machine waste, as well as peripheral waste in the value stream.
- Improved throughput. The ability to keep a machine up and running at peak efficiency improves the total throughput of a line.
- Better uptime. Stronger insight into equipment function and potential catalysts for failure allow maintenance teams to subvert them for more reliable uptime.
- Equipment longevity. Better-maintained equipment lasts longer and performs more reliably, lowering the total cost of ownership.
- Preventive maintenance. Instead of reactive maintenance, manufacturers can move toward preventive approaches that improve predictability.
- Better asset ROI. Fewer problems and longer lifespan establish a better ROI for equipment as it continues to contribute to operational excellence.
The true purpose of manufacturing digital twins is to realize Lean philosophies. That means less waste, better equipment availability, proactive action, and better efficiency across the value stream.
Digital twins manage manufacturing’s complex environment
Step into any modern factory and it’s easy to see how digital twins got their start. There are so many intelligent systems running continuously, relaying data about everything from machine condition to throughput. All this data needs to go somewhere. Digital twins arose out of necessity and quickly became the foundation for smart factory operations.
Manufacturing is the original case study for digital twins, and it paved the way for broader application across other sectors. In the same way factories became smarter and generated more data, so too have office buildings. And, with the prospect of smart cities rising each year, it’s a safe bet that digital twins will continue to gain traction. As they do, professionals can look to manufacturing to see just how powerful these systems are.
Keep reading: Digital Twins: A Revolution in Workplace Management
By Jonathan Davis
Jonathan writes about asset management, maintenance software, and SaaS solutions in his role as a digital content creator at Eptura. He covers trends across industries, including fleet, manufacturing, healthcare, and hospitality, with a focus on delivering thought leadership with actionable insights. Earlier in his career, he wrote textbooks, edited NPC dialogue for video games, and taught English as a foreign language. He hold a master's degree in journalism.
