Robert Brooks, editor-in-chief of Foundry Management & Technology and American Machinist, sister brands to Smart Industry, explores from his vantage point the role of AI in industrial data gathering.
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Brooks has been a business-to-business reporter, writer, editor, and columnist for more than 20 years, specializing in the primary metal and basic manufacturing industries. Brooks and his editorial team report on manufacturing activities in the metal casting sector, including foundries and die casters, and the industrial technology sector, including machine shops and machining operations that are a part of larger OEMs.
Below is an excerpt from this podcast:
Robert Brooks: In this installment, I'm going to raise an issue that I think is lingering in the minds of various manufacturers that I speak with concerning what they have long referred to as big data, meaning the huge volumes of information that accrue in manufacturing activities from older generation to production data to performance metrics.
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For the past two years, the discussion of big data has been subsumed by the growing awareness of artificial intelligence and the ability of large language models to detect patterns and simulate results and generate conclusions about present and future activities. At the International Manufacturing Technology Show, that is IMTS 2024, which I attended earlier this month, the novelty of AI capabilities in the manufacturing space was quite evident by the presence of tech giants like Google and Microsoft and Amazon. But exactly what this new visibility portends is still a question to many of the manufacturing figures that I have asked.
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One executive frankly told me that AI is a buzzword, a topic without a foundation. And I think there are two reasons for drawing this conclusion. First is that manufacturers in the space occupied by IMTS exhibitors and attendees have been gathering data on this scale for decades. They know what they mean when they discuss big data, and they do not grasp the new significance of artificial intelligence.
My theory on that is that the work of manufacturing is not really about answering questions. That is, not answering questions based on prompts. It's about solving problems based on conditions and variables. Of course, there is a huge role played by simulation in manufacturing in CAD and CAD programming, and there's a lot going on in those disciplines which are converging in very interesting ways. In short, the proponents of AI and manufacturing at the present moment do not seem to be proposing anything that manufacturers do not already have in use.
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Which causes me to wonder about the second point, which is what do manufacturers expect from data resources? One answer is compliance. Recently at American Machinist, we posted a report by a frequent contributor, Matt Heerey, who is the president of ECI Software Solutions, which develops enterprise software specifically for job shops and discreet and batch and process manufacturers. His article, well worth reading, is titled “The Vital Role of Data Security.” Let me quote him in part.
“Manufacturers cannot afford complacency. Defense and aerospace manufacturers, specifically, must comply with stringent standards and documentation procedures. A critical example of compliance necessity comes via the U.S. Department of Defense’s Cybersecurity Maturity Model Certification. Non-adherence is not an option, as failing to comply with ever-changing requirements leads to severe repercussions, including revenue losses and impediments to growth.”
