Why Modern Manufacturing Needs the Digital Twin

Why Modern Manufacturing Needs the Digital Twin
30 June 2020
By Veronica Goudzward
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In our modern era, the idea of using a digital twin in some form or another to test a product is nothing new. After all, we have long learned that practice makes perfect and replicating something in a virtual environment saves both time and resources.
However, as technology and data become more ubiquitous and increasingly available for every aspect of an industry, so has the concept of the digital twin. Virtual replication, simulation, and testing can be applied to the entire process of the manufacturing lifecycle as opposed to just the end product or deployed asset. Thanks to IoT, cloud computing, and the proliferation networked sensors, manufacturers can now take advantage of the digital twin to refine their production cycles, stay ahead of potential obstacles, boost efficiency, and improve their products.
Modern Challenges Require a Modern Solution
Manufacturing and supply chains are more complex and satisfy a higher demand than ever before in human history. In our globally connected lives, even simple products often require sourcing from all over the world as consumers are increasingly demanding faster delivery, lower prices, and greater transparency all while desiring a higher quality product.
In order to meet the modern demands of the consumer, manufacturers are forced to increase their efficiency and streamline processes like never before. After all, the only way to stay viable in today’s world is to cut waste.
This need for efficiency rings especially true for situations like the COVID-19 pandemic that has swept the world in recent months. Due to the spread of the virus, both consumers and manufacturers are increasingly worried about product shortages as global supply chains are unexpectedly disrupted in ways that will affect economies for years to come. While global crises cannot be avoided, smart manufacturers are looking for ways to plan and mitigate their negative impact.
If leaders within an organization are going to stay ahead of the game, innovate, and make smart decisions, they need accurate information in the form of actionable data with regard to their manufacturing process—and that is where the manufacturing digital twin comes in.
What is a Digital Twin?
It sounds like something used by the villain of a science fiction story, but the digital twin is actually a tried and true concept used in everything from high performance cars to rocket ships . A digital twin is essentially a virtual replica of a real world object. The ideal digital twin replicates the same parameters as its real life counterpart so that engineers can test the digital version and simulate certain conditions without causing any real damage to a physical product. Think of digital twins as a type of sandbox proving ground where designers can break their product in imaginative ways in order to improve the real life version.
In addition to testing, digital twins can be used to make predictions on how the real world version will perform over time. By using sensors in the real product to provide feedback to the digital twin, the true operating parameter of the product can be used to project the wear and tear on components, estimate lifecycles, and allow for preventative maintenance. The behavior of a digital twin can also be compared to that of the physical model in order to determine if the real world product is performing as originally intended and expected. If not, then analysis can be done on the two models to see where the differences lie and how to fix it.
How Digital Twins Can Help Manufacturing
Digital twins have traditionally been seen as a way to test an end product. Car designers can test out new concepts before going to production, NASA engineers can evaluate the safety parameters of space equipment without endangering human lives, and companies can forecast needed updates, recalls, and maintenance to their products. However, digital twins are increasingly being taken a step further to simulate the entire manufacturing process as opposed to simply the end result.
In the past, replicating an entire process as complex as manufacturing has not been feasible due to the cost of the technology and the lack of needed data processing and computing abilities. However, we now live in a world where sensors and data exist everywhere from your car to your toaster. Even your hairbrush can be networked to provide real-time data. Follicle health detectors and burnt toast notifications aside, this is good news for manufacturers, as the technology now exists for their production lifecycle to be networked and connected to digital twins.
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This enables organizations to test desired changes to their production process before full implementation without interfering with the existing production chain. It also allows companies to input real-time operating data into their digital models to forecast when components of the process will need to be repaired or replaced, identify single points of failure, and otherwise find ways to increase the operating efficiency of their manufacturing process.
As every modern industry has learned, ones and zeros often cost significantly less than iron and steel. Being able to test out and tweak aspects of one’s manufacturing chain without having to purchase and implement physical equipment to test out can result in significant savings to time and resources. An organization can simulate the use of different manufacturing equipment, new sourcing options, alternative materials, and more without touching or interfering with their existing real-world process. Leaders can also test out different operating parameters to see what their peak output looks like and find out which factors result in maximized efficiency to meet their organizational goals.
Bottom line, manufacturing digital twins can answer what works and what doesn’t without tampering with existing processes in order to help leaders make smart decisions.
But, What About Manufacturing Execution Systems?
If the concept of using data to improve the manufacturing process sounds familiar, that is because many organizations are already doing it through the use of an MES or, Manufacturing Execution System . An MES uses real time data to track how equipment is performing in order to increase throughput and reduce costs. Sounds a lot like a digital twin, right?
In actuality, an MES can be considered a type of simple digital twin. However, here is the key difference. An MES (typically) is not a wholistic and centralized representation of an entire manufacturing process. With a true digital twin, all of the data being produced within the production chain is collated into a working model that captures a comprehensive view of the system as a whole for easier testing and visualization. An MES is a great foundational starting point for a manufacturer to develop a digital twin.
A digital twin can be considered the next step or evolution of an MES in order for a manufacturer to adapt to the changing technological capabilities of the world around them.
Conclusion
Digital twins are increasingly becoming a standard method of business intelligence and process improvement. In fact, Gartner listed the digital twin as one of the top ten strategic tech trends in 2017. As data becomes increasingly more critical to organizational decision making, having a wholistic virtual model to test real world effects is the most effective tool to stay innovative and successful.
To learn more about how digital twins and IoT can be used to improve your processes, reach out to us today .