Noodle Notes: AI for manufacturing
This week we’re all about AI for manufacturing. There are significant benefits to implementing predictive data science and machine learning solutions in process manufacturing, maintenance solutions, and supply chain applications. In fact, the more you combine the art and science of math with the sensing capabilities of today’s treasure trove of data sources, the better and more accurate your predictions become. This is crucial for reducing waste and creating radical efficiencies.
AI Hype Cycle
First up is Forbes, with “AI in Manufacturing: Ready for Impact.” While the authors of the Forbes article were cautiously optimistic about AI in manufacturing and stated that manufacturers were slow adopters of this new tech, CIO.com thinks manufacturing is already leading the way in AI adoption. This contradiction is a great example of the AI hype cycle we cautioned against earlier this year, frankly, and one reason we work hard to take a no-nonsense approach to being your AI resource.
AI and Robots
Another common AI media misdirection is a focus on robotics, like this article in The Manufacturer. Robotics on a shop floor can have AI as part of their core automation OS, but we know the value in AI for manufacturing is even more granular than snazzy robots (even if they learn to dance like the one below from Boston Dynamics). The best way to reduce costs, reduce wastes, and generate efficiencies is through data, prediction, and probabilistic AI.
Real AI in Manufacturing
We’ll close out our focus on manufacturing and AI with this article in Forbes from Intel AI, “How AI Builds a Better Manufacturing Process.” It takes a deeper look at the many ways we can expect to see real AI in the manufacturing process, including predictive maintenance, the concept of digital twins, generative design, computer vision, and other implementations of smart data science. This is the kind of thinking about AI that we applaud – less hype, more focus.
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