In Stay Sharp Episode 130, How Product Data Management Was Born—and What it Means for AI Today, co- hosts Jonathan Scott and Juliann Grant sit down with Brion Carroll, arguably one of the godfathers of PLM. Currently the CEO and principal consultant of Digital Solution Group, Brion was a co-creator of the original product data management (PDM) system. Together, they discuss where the need for data management began, how it evolved into PLM, and what the future holds for AI’s role in the industry.
The Origins of Product Data Management
The scene was Computervision in the 1980s, a company at the forefront of the CAD/CAM world. Brion helped create a system to contain and manage customer data, doing backup and recovery, revision control, access, and security. While Brion says the ability to centralize storage was great, the driving force was enabling manufacturers to keep their data and product information safe. “Computervision sold that product, which was called Product Data Manager, and we had to make up an industry name,” Brion says. “We call it product data management, of which we were the leaders. Of course we were, because we just made up the name.”
Brion Carroll, CEO and Principal Consultant of Digital Solution Group
How PDM Became PLM
Tracking metadata is where PLM started, Jonathan explains. That’s what moved PDM from managing data into more of a lifecycle. “It wasn’t that PDM vendors had to keep making new versions,” Brion says. “There was nothing that guided engineering, then moves to detailed engineering, then moves to tests and prototyping, and then back to engineering and so on—depending on how many cycles it needed to do for a product to come out the other end. Well, why don’t we manage that?” Given vendors looking to innovate, plus industry analysts saying PDM should be managing a lifecycle, adding the workflow engine was the next step.
The new PLM was supposed to be collecting all the data manufacturers needed for the full lifecycle of a product, Jonathan notes, but it really wasn’t. The new concept of a digital thread meant tying PLM to other systems, but the industry missed that step. “That’s the point where technology is the enabler of a different discussion,” Brion says. “If you think about it back then, and I’m going back to, as people transition to where an internet-based PLM could actually exist … everyone had to catch up. You still didn’t have anything that encompassed the breadth of where data goes from ideation through to commercialization.”
Another part of the reason there’s no “one PLM to rule them all,” in Jonathan’s words, was that while PLM was evolving, so were other enterprise systems like MES and ERP that were storing other bits of data. Now we have multiple platforms and an industry sounding the drumbeat of integration, integration, integration—which Brion agrees is going to be critical as everyone’s favorite topic becomes more integrated.
The Foundation for AI
As someone who’s tracked and developed software solutions for manufacturers, Brion is attuned to how both needs and capabilities are evolving, especially now that AI has entered the conversation. There’s now escaping it in platforms, he says, “Everything’s now going to say ‘with AI’.” The problem he sees is that when PLM has AI, MES has AI, and ERP has AI, what businesses get is a narrow view, limited to the data inside that platform. PLM, he believes, can be the solution—specifically not just a platform, but a solution. And AI should be put on the solution, not on siloed systems, because the information needs to flow back and forth between systems so that changes made in one update in the others.
What’s missing from digital business solutions are business insights, and AI can provide them, but only if it’s looking at data across silos. If you don’t integrate siloed systems across the product lifecycle solution, he says, “Your AI won’t be a dream to come true, it’ll be a mission that’ll fail, period. It’s just not going to work. So as they say, put down your mashed potatoes before you put the gravy. Get all your fixins right. You’ve got to have those things all set up before you pour the gravy on.” Because unlike PDM, Brion adds, “AI is going to be here forever.”
Learn More About PDM, PLM, and AI
The full podcast contains more discussion and details on a variety of topics, including the three phases PLM has gone through, what it meant that vendors “rolled the carpet in front of the queen,” why stabilizing data is the critical factor for ensuring AI produces value, and more.
Be sure to check out Stay Sharp Episode 130: How Product Data Management Was Born—and What it Means for AI Today and join us each week for a new podcast.
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