When I first got started in manufacturing most manufacturers were still at some point on their journey to retool their production environments with CNC machines. After all, it was not that long ago that I was individually loading blanks into a lathe to produce a key part of the rearview mirror assembly – initially for a major automaker and then as an aftermarket offering. It kept the facility busy for several years, but it was a very tedious and undeniably manual process.
There has always been a population of manufacturers living on the bleeding edge, utilizing their willingness to embrace the latest technology in order to gain a significant competitive advantage. And these firms continue to play a meaningful role. After all they keep the thirst for new technology alive.
However, the big picture utilization of technology is evolving considerably. Step into just about any production facility today, and it is easy to see that manufacturing technology has a completely different meaning than it did then. Advanced technology is no longer something that was nice to have. Today, manufacturing technology takes center stage. And as the data-driven experience economy continues to solidify, the tech story ultimately becomes one of convergence.
Consider, for instance, the mass customization story that Nancy Giordano, post digital futurist and founder of Play Big Inc., shared with me recently. During the Oslo Business Forum last year, she strapped on a pair of virtual reality goggles in order to take a digital journey through the back-end processing and logistics necessary to enable her to design her very own custom pair of Adidas sneakers – and have them delivered to her within four days.
“Imagine all the players that need to come together to make that happen: from the AI powered online site that guides my choices and maybe takes my bitcoin payment; to the materials partners alerted in real time of the colors and textures I’ve chosen; to the 3D printer and/or robot seamstresses nearest me waiting to produce and sustainably package my shoes; to the scheduling of drone pick-up; to the creation of a secure delivery site, and it is all tracked in real time,” says Giordano.
As anyone in manufacturing knows, such scenarios do not simply happen. If a manufacturer hopes to economically, and repeatedly, deliver on the promise of a customer driven experience, it requires a seamless convergence of advanced data capabilities with both emerging and maturing manufacturing technologies. And, it needs to be a cyclical process of continuous improvement, always learning from the data created by today’s technology.
Understandably, for most manufacturers, this scenario is more likely a peak into the future than it is today’s reality. However, as Giordano suggests, digital manufacturers who have fully embraced the power of data are on the verge of some version of this “on-demand, hyper-personalized, infinitely-dynamic” new reality. The ongoing convergence of technology making mass customization is actually “a mash-up of retail, manufacturing and logistics. Executing something like this will require a well-coordinated and synchronized collection of partners and systems," says Giordano.
Thinking back to when we had three shifts hand loading blanks, offering a two-week delivery time for a very standardized part was considered a rush delivery. That would never work in the growing world of mass customization. Of course, at that time most of the technologies converging to enable this growing trend were not even available to manufacturers operating on the bleeding edge.
Digital twins are rapidly becoming a business imperative for the next evolution of digital enterprises. By design, digital twins take advantage of the vast amounts of data from connected devices to provide a detailed, data-based model of a physical system. As the industrial internet of things (IIoT) connects more and more of these devices within organizations, virtual simulations of physical environments are becoming an increasingly critical piece of an enterprise digital strategy.
The capability is compounding with the vast processing power of high-performance computing (HPC), enabling diverse industries to apply digital twins to scale simulations, shorten development timeframes and derive tangible business results. “While digital twins are being looked at in the digital engineering realm for design and simulation work, there is significant untapped potential in the services part of the value chain,” DXC Technology’s CTO and Product Leader, Aerospace, Defense and Manufacturing Shankar Nagarajan tells IndustryWeek. “Examples include servicing parts and products optimally and correctly the first time, and in training service technicians via augmented instructions to achieve significant business benefits.”
Some of the growing vertical use cases include:
· Smart cities: Cities around the world are building models of their smart systems in a virtual world to make predictions, test and learn using data and analytics prior to deployment. For example, they can understand how autonomous vehicles respond to connected traffic lights to make better and more timely decisions that improve outcomes, such as citizen safety.
· Healthcare: In a value-based care environment, doctors are under pressure to become increasingly efficient and accurate in their diagnoses. Digital twins can help providers home in on the two or three most important questions to ask a patient to enable a finite diagnosis. Medical device manufacturers can use digital twins to create and test 3D-printed, custom-built orthopedic implants that dramatically decrease a patient’s wait time.
· Aerospace and defense (A&D): Companies in the A&D industry leverage digital twins to visualize the status of their products at each step of the production process and in the aftermarket. Digital twins have completely transformed the way some ships and aircraft are designed. For example, digital twins assess the quality of airfoil blades in jet engines by providing manufacturers with a comprehensive process view, including the thrust produced under real-life circumstances.
Understanding Obstacles
However, even though IDC predicts that 30% of G2000 companies will use data from digital twins in 2020 to improve workforce performance and manufacturing efficiencies, some companies are currently experiencing challenges in making this happen, explains Nagarajan. “IIoT platforms can process data from machines so that real-time insights are available to continuously optimize digital models, but companies must first move away from their legacy systems before they can fully capitalize on these advances,” he says. “It may not be possible to leverage digital twin technology effectively across the entire value chain until they do so.”
According to Nagarajan, it is not only the technological aspect of digital twins that manufacturers need to think about. “They also need to consider the skill sets of their workforces to fully optimize the benefits that digital twins provide,” he says. “Even as design and simulation become increasingly automated and efficient, companies still need their workforces to make the right decisions in order to move quickly and efficiently. The lack of knowledge across the value chain and a potentially aging workforce trained in legacy methods could continue to be a hindrance for some companies.”
2020-02-10 11:36:17Z
https://www.industryweek.com/technology-and-iiot/article/21122344/tech-convergence-is-driving-the-future-of-manufacturing
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