More than a decade ago, we started this conversation surrounding the IoT (Internet of Things) in manufacturing. Now, with the rise of AI (artificial intelligence) and the industrial metaverse, we have new opportunities with ML (machine learning) and deep learning to sharpen our skills, reduce mundane tasks, and improve business processes once again. We are at that next stage where we can truly improve profitability, while becoming more efficient, all with the help of technology.
This was reinforced on my recent trip to the Innovation Design Lab and the Houston Microsoft Technology Center, aka MTC, which helps enterprise customers tackle a challenge—one that might be new or more complex.
Grand View Research suggests the global industrial metaverse market size was estimated at $18.36 billion in 2022 and now it is estimated to experience a CAGR (compound annual growth rate) of 33.6% from 2023 to 2030. Not surprisingly this is driven by several factors.
We see manufacturing executives recognize opportunities to improve quality, output, and sales when investing in the industrial metaverse, all driven by AI, process simulations, and digital twins. Many of us who have been in this space a decade ago recall similar discussions with the birth of the IoT. However, now with a better use of AI, ML, and deep learning, manufacturers can truly streamline processes in ways we once hoped to achieve. Based on end-use, the manufacturing segment dominated the market with a revenue share of 25.5% in 2022—and continued growth is predicted for the years ahead.
Precedence Research suggests the industrial metaverse represents an emerging digital terrain that integrates virtual and augmented, leveraging AR (augmented reality), VR (virtual reality), MR (mixed reality), AI, and the IoT. North America contributed more than 34% of market share in 2023, due to technological infrastructure, widespread adoption of advanced technologies, and a high concentration of key market players.
All about the Data
On my recent trip to Houston, Andy Pratt, CVP of emerging technologies, Microsoft, shared with me the opportunities that exist for manufacturers with technology—and some of the steps required to be successful with technology adoption.
“If you are collecting all this data from this data estate from your factory, from both your manual systems and your digital systems and fully automated systems, even the transcripts and the conversations you have from expert supporting, we are even now in a position to leverage all of that capability to drive new insights and develop specialty Copilot AI for the factory floor, which is super exciting,” he says.
One of the challenges can potentially be data overload. He recommends exposing them to the information that is most appropriate to the pattern or the problem they are trying to solve. He adds to be intentional about what data you get, where you get it from, and how you structure it. That is one of the first steps: get the data estate right and make sure you are collecting the right amount of information for the right applications.
Some of the most common scenarios and applications in manufacturing include reducing downtime and bottlenecks and doing predictive maintenance. The customer will start by identifying the issue and then Microsoft will provide a few scenario offerings.
Something else to consider in all of this is identifying the three “As.” First, assess both the physical environment and the digital capabilities. Next, align IT and operations and business objectives, then adjust—and make sure you are constantly adjusting.
All about the Supply Chain
One of my big questions surrounding all of this is how it will work with supply-chain partners with all the data from the AI. As we all know, with procurement in the supply chain, there are some big challenges to be addressed.
“It is a big pressure on manufacturing. Material costs are changing. The supply chain is constantly flexing, the demand curve is spiking and dropping, and a lot of that is out of the control of the person directly making the product,” says Pratt. “Being able to have all of these data estates available in the cloud and being able to reason over it and optimize it is really exciting.”
He also outlined how discrete event simulation can help—taking simulation capabilities and combining it with generative AI capabilities, which is giving a whole new way to think about how we optimize supply chains. Also, what-if scenario analysis can help see how simulations work.
All about the Intelligence
While I was in Houston, I also had an opportunity to sit down with Josh Stevenson, director, Houston MTC. He shared the purpose of the space and several of the types of events they hold there such as high school programs and community outreach, just to name a few.
“The reason it exists is because this is …. the marquee location where our enterprise customers can come when they are tackling some new challenge that is sufficiently complex that spills outside the banks of the ways that Microsoft organizes itself,” he says.
Here, Microsoft has an opportunity to harness this AI moment for the manufacturing industry—something that started many years ago. As we all know, AI dates back decades, but really exploded in the 1990s when machine learning and deep learning came onto the scene based on this notion of a neural network.
Stevenson shared Microsoft’s AI ambitions started in 2016 at which time it formed a team to align on principles before it decided to go back the technology. The principles are around trust. Ultimately, Microsoft believes the human is in control of the action. Hence, the name Copilot perfectly encapsulated Microsoft’s notion of what responsible AI is. The authority lies with the pilot—the person.
Soon, we saw the rise of modern AI as we know it today and the rise of Open AI, which can provide text, code, or photos based on a prompt. Now, the opportunity exists for manufacturers to tap into those opportunities—reducing downtime and bottlenecks and doing predictive maintenance, just to name a few. How will your company step into the new era of innovation?
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