Editor’s note: What’s on your mind? Submit a supply chain question to Editor-in-Chief Elizabeth Rennie at erennie@ascm.org. If yours is selected, she’ll research the topic and share the answer in the ASCM Insights Blog.
Reader J.B. asks: I’m fairly new to the supply chain field and am interested in learning more about digital twins. The topic is so complex that I’m having trouble even understanding exactly what they are. Can you break it down for me?
Defining digital twins is trickier than it sounds
When dealing with a subject as complex as digital twins, it’s always best to begin with a definition. Unfortunately, depending on who you ask, a digital twin can be explained in many different ways: an integrated replica of a product intended to reflect manufacturing defects; a sensor-enabled model that simulates a physical object in a live setting; the digital version of the physical, based on historical, current and predicted future information. I once worked on a feature article about digital twins and noted that my interviewees all gave similar, yet distinctly different, definitions!
Getting a clearer picture
Luckily, our trusty ASCM Supply Chain Dictionary has a very nice explanation: A digital twin is an exact virtual replica or model of a real-world process, product, or service used to digitally simulate, test, model and monitor it. So, digital twins enable manufacturers to build virtual replicas, from design and development to the end of life. The technology then mirrors its exact counterpart in the real world.
Why digital twins matter
Companies benefit from digital twins because they model the way things interact with their environments, enabling users to foresee potential outcomes. As every shop floor manager knows, the slightest supply chain interruption can affect everything that follows. This is why digital twins are so exciting: They make it possible to not only address, but actually prevent, potential disruptions and keep supply chains as productive as possible. In the current global landscape, characterized by increased volatility, uncertainty, complexity and ambiguity, the ability of digital twins to predict and mitigate disruptions has become even more critical.
Digital twins also provide essential data about an item’s design, the system that built it and how it is used. They rely on thousands of sensors, which are distributed throughout the physical manufacturing process. They deliver masses of cumulative measurements in a wide range of dimensions — everything from the behavior of machinery to environmental conditions in the factory itself. The information is continually communicated, aggregated and analyzed, with the ultimate goals being to optimize processes, detect current and potential physical issues, predict results, and build better products. Over time, the technology uncovers undesirable performance trends compared with an ideal range. Furthermore, the integration of digital twins with advanced analytics, AI and machine learning has significantly enhanced their predictive capabilities and ability to identify subtle patterns that human analysis might miss. This allows for more proactive and precise interventions.
How digital twins work
When working on the aforementioned article, I had the opportunity to interview Christian Urnauer, a specialist in digital manufacturing and doctoral candidate at the Institute of Production Management at the University of Darmstadt. He recounted his experience working with simulation models of production plants. Urnauer told me critical preliminary steps are tool selection and definition of scope. To help you get started, he suggested considering the following questions:
- What are the core functions that must be modeled by the digital twin?
- Which assumptions and simplifications can, or need to, be made?
- What is the input for both real and simulation data?
- What output will be monitored?
“This way, I could define the scope and make sure that I didn’t get lost in details that are not of importance,” he explained. “When I started to build the model, I asked myself the same questions again.”
Ensuring supply chain success
Verification and validation are also vital phases for ensuring successful application of a digital twin and increasing its reliability and acceptance. Validation requires assessing whether the digital twin serves the purpose for which it was intended; verification involves confirming if model components and functions are working properly. Experts also recommend creating a digital version of supply chain flows and tapping into data from multiple systems. This often involves integrating data from enterprise resource planning, transportation management and warehouse management systems, as well as external sources such as weather data or geopolitical news feeds, to create a truly comprehensive and dynamic representation of the supply chain.
Digital twins offer uniquely realistic glimpses into the future. To learn more, check out ASCM’s Supply Chain Technology Certificate, and help your supply chain organization advance via truly data-driven decision-making.
Submit your supply chain question by emailing Editor-in-Chief Elizabeth Rennie at erennie@ascm.org.
