MT Magazine January/February 2025

FEATURE STORY

JANUARY/FEBRUARY 2025

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Because there is a big difference between the two operative adjectives. Digital twins are simple, but creating them isn’t exactly easy. Doing so is a cross-functional and cross-technical undertaking that involves designers, engineers, product experts, and production personnel – to name a few. The list of technologies relating to digital twins is rather extensive. They include the aforementioned sensors and IoT devices; edge computing devices, routers and servers; software including digital twin platforms (yes, there are specific software available from all the major vendors), 3D modeling and simulation, data analysis and visualization; machine learning – and the list goes on. How big is this? According to McKinsey, the global market for digital twin technologies is anticipated to reach the order of $73.5 billion by 2027. Again, the creation and operation of digital twins is not easy. And while vendors can help make it simple, it is still essentially complex. But there should be little doubt that (1) this technology is emerging in a big way, and (2) consequently, those who don’t engage with it – and possibly fully embrace it – are going to find themselves at a competitive disadvantage because others are. Stephen Laaper is a principal at Deloitte Consulting LLP and a manufacturing strategy and smart operations leader in Deloitte’s Supply Chain & Network Operations practice. He leads the firm’s smart manufacturing services. Laaper understands manufacturing. Given that, it isn't surprising that he talks a lot about generative AI (GenAI). However, it is surprising that when describing the capabilities of GenAI in the process of making things, he cites tool clearances as an example. GenAI is trained on text, images, and audio and can create solutions and recommendations related to that content. While Deloitte has identified seven total types of AI, GenAI is all we need to consider for the purposes here. As has been the case with other emerging technologies, Laaper says that AI has been around for a while but that there is now tremendous interest across the board, from consumers to manufacturers. “We recently completed a comprehensive AI strategy and road mapping with a large OEM,” he says. What’s more, the technology has moved beyond just proof of concept and is now adding direct value through deployment. Which brings us back to the tool clearances. Working the AI Laaper points out that during the development of a product, there are “very robust exchanges between manufacturing engineers and design engineers.” One of the issues that must be addressed during this process is whether what is designed can be manufactured – as in whether there are required tool clearances (e.g., for a spotwelding gun to fit into a particular area). Laaper says that typically requires spending a non-trivial amount of time ensuring that the space for tools is included in

Heating or Rotating Consider, for example, the top surface of an automotive instrument panel made from ABS material. For issues related to both cost and mass, there is an effort to make the part as thin as possible. Given the sun load in places like Phoenix, Arizona, the temperature of a dashboard can reach nearly 160 degrees. So, this would be an ideal situation to use a thermocouple to measure the temperature and amount of warpage that occurs in real life from the heat – data that is then sent to the digital twin. That then allows digital analysis based on real-world information. As a result, the material, thickness of the shell, design of the component, or some other characteristic can be adjusted as needed to improve future instrument panels. Or consider a machine with rotating elements. A digital twin of the bearings can be created to measure the behavior of the actual bearings over time and use. Companies can then predict the machine’s maintenance needs, allowing users to schedule routine service at their convenience, minimizing downtime and keeping the physical machine running in peak condition. Big Benefits The impacts on both process and product is significant. According to a survey conducted of the aerospace and defense industries by Capgemini Research Institute: • 75% say digital twins improve value from the start – when design commences. • 81% say there are operational improvements, such as availability and reliability of equipment. • 73% say there is improvement in the production rate. • 76% say there is an improvement in quality. And from a competitive point of view, it is worth considering that 73% of companies surveyed said they have a long-term roadmap (more than five years) for digital twins, and 61% said that digital twins are a strategic part of their digital transformation. Said simply, the aerospace and defense industry perceives digital twins as providing a significant advantage – and there is no reason to think that this isn’t the case for any durable goods industry. Not Easy Those of a certain age will remember a series of books for students written to help them with their studies: “Latin Made Simple,” “Calculus Made Simple,” and so on. Note that they weren’t … made easy. In fact, McKinsey consultants note that machine building – particularly in the area of customized, special machines – can greatly benefit from digital twins: not necessarily in the context of having models of the machines, which are one-offs, but by creating a library of models of key components (like the aforementioned bearing arrangements) that can be assembled.