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GE Digs Deep on Digital Manufacturing


Every year, General Electric gathers hundreds of its engineers under one large roof at its expansive Global Research Center campus outside Albany for a days-long symposium focused on best practices, recent developments and moving forward with new ideas. The brainpower collected there is, to say the least, impressive.

Part of the week’s activities last month included an hour-long roundtable with four top thinkers and leaders from across the company. What follows is a version of that roundtable, edited for length and clarity, a deep dive into the potential near future of digital manufacturing at GE and across the industry.

GE is rolling out its new Industrial Internet Control System. How does it accelerate GE’s transformation to the digital industry?

Jim Walsh, general manager, automation and controls, GE Automation: I guess the way I would summarize it is, it makes the outcomes real. In other words, when I had a software role, when I thought about data, it was a lot of, ‘How do I get the data together? How do I ultimately run some analytics against it?’ What I didn’t necessarily have was a way to get those insights back to the equipment. So what we’re talking about here is a world where you’re collecting the data, you’re applying a rich domain expertise against it, … and then you’re able to bring those insights back to fundamentally change the way that an asset or process operates. Customers care about outcomes, not necessarily the technology or the theory.

From a perspective of its strategic importance, how is the digital twin important to this effort?

Colin Parris, vice president, GE Software Research: If you look at the digital twin itself, it’s a digital involved in the physical asset that actually uses a constant stream of data to deliver business outcome. So when you combine that with the control’s capability, which gives you real-time capability, what you have is a constantly-adapting system. So I can predict, or I can optimize, but regardless, as the data comes back, I adjust the twin model, predicting and optimizing with the current state. That constant stream allows us to customize it as close as possible to what the customer actually needs.

The twin system is a set of model management techniques. You can build a model, but how do I know when to update the model? How do I know when to retire the model? How do I know if the model is valid? These things have to be done by building a modeling system, which is what we’re doing here at GRC and transitioning to Predix, and we have to use them effectively. It goes beyond analytics. It’s the entire system.

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Source: Industry Week