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The Grand Challenge: The Process of Traceability

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When you hear the words smart manufacturing, what comes to mind? Systems and processes within the manufacturing process? Or maybe intelligent machines? According to Athulan Vijayaraghavan, CTO and Co-founder of System Insights, there is no smart manufacturing without connectivity. The key to achieving efficient, data-driven manufacturing is taking into account all the connections that need to be made.

It all started when Vijayaraghavan was a graduate student at UC Berkeley developing the MTConnect standard, an open, royalty-free standard that fosters greater interoperability between devices and software applications. After MTConnect was launched in 2008, Vijayaraghavan was focused on using the data gathered from the MTConnect-enabled machines to solve real manufacturing problems. He said, “How do we now take all of this data, and how do we really start making decisions off of the data?” Vijayaraghavan and his business partner, Will Sobel, decided to take on this challenge and co-founded System Insights.

Vijayaraghavan says the grand challenge is the process of traceability, a more holistic approach to the manufacturing process. “The reason I like to call it the grand challenge is that it kind of helps refine everyone’s thinking to think about one big problem to solve, as opposed to many smaller problems to solve,” he explained. In order to make decisions from the manufacturing data, you need to understand everything that is happening to a particular part throughout the entire manufacturing process, not just where the part is located at any given time. He says, “Process traceability is being able to track and reason over the flow of data and information in a manufacturing system. So as a part gets made, how does the part get made? What are the decision that are taken as the part gets made? And how do we analyze everything that went into making a part across the entire manufacturing system?”

The ultimate goal is a two-way flow of data within the manufacturing process. As Vijayaraghavan says, “One of the holy grails of manufacturing is how do we tie what is happening on the shop floor with what’s going to happen when the part is being used, and how do we tie in what’s happening on the shop floor with how the part was designed.” The only way to achieve this, he says, is by having the ability to capture and reason over this manufacturing data. Once this is achieved, you can use this data to determine what kind of performance the part will give you across its lifecycle. The only way to make this happen is through rich manufacturing data.

Vijayaraghavan goes on to say that this includes data from the human in charge of running the machine. The real challenge is figuring out how to take this extremely valuable data from the operator, or tribal knowledge, and quantify it into useable, relatable manufacturing solutions. What’s most important is data that pertains to decisions made by the expert running the manufacturing process, and using this rich data to help them make even better decisions.

The final step is introduced in Vijayaraghavan’s definition of the Internet of Manufacturing Things (IoMT). Like the IoT, the IoMT is all about connectivity. But it focusses mainly on the manufacturing piece of the puzzle. He says, “IoMT helps us focus the whole conversation about the IoT on what it means to be making decisions in a manufacturing context.” Through people, sensors, processes and equipment, the IOMT can help make the process of traceability a reality.

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2016 [MC]2 Conference | April 19-21, 2016 | Dallas, Texas

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