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Mark Hembree

Innovation

What Manufacturers Should Know About AI

Maintenance and quality control are early adopters

Published: Thursday, January 27, 2022 - 12:03

From asking Siri to suggest a nearby restaurant to programming a drone flight on Mars, artificial intelligence (AI) has become a part of everyday life—and its presence and influence will inevitably grow.

What do manufacturers need to know about it?

First of all, it’s already here. Capgemini Research Institute reports that more than half of European manufacturers (51%) use AI, followed by Japan (30%) and the United States (28%). So far, the most popular applications are for maintenance (29% of the examples cited) and quality control (27%).

What is AI, and what can it do for you?

Without debating the philosophical meaning of “intelligence,” examples of AI capabilities include speech (“Hey, Siri!”), image and video recognition (simple as bar codes, as complex as facial recognition), autonomous creations (robots, self-driving cars), language processing (Rosetta), and analytics and predictions gained through AI’s ability to “learn.”

Just consider the two popular applications already mentioned above—maintenance and quality control.

Maintenance

Wouldn’t it be nice if you knew your equipment would break down before it happened, and what would be wrong with it? That’s one of the things AI can do for your assembly line.

In “Top 5 Cases to Use AI in Manufacturing” (Robotics and Automation News, July 20, 2021), Mark Allinson describes how AI can predict product and equipment failures to prevent breakdowns, shorten downtime, reduce costs, and improve productivity.

The same data that run your assembly lines can be compiled and analyzed by AI to forecast maintenance issues up and down the line, in greater quantity and more detail than any number of human technicians working around the clock.

Technology-industry consultant AI Multiple provides more about that here.

Quality control

The same predictive powers of AI can detect design flaws and identify failures before a product ever leaves the plant. Robotic sensors and computerized quality tests gather more data, much faster, than what many manufacturers have in place today.

The result is higher quality for products you already make, and less time to market for new products.

And much more

Thinking of what AI can see, hear, and learn—through its precise sensors and massive amounts of data—can help you further visualize its capabilities and benefits.

Allinson points out AI’s potential and possibilities through other examples you may find surprising—or that never even occurred to you until you stopped to think about it:
• Demand and price forecasting: Data, data, and more data, gathered from different sources, can be analyzed to predict demand and forecast pricing.
• Inventory management: The information AI can analyze and predict can help you manage inventories based on demand and supply.

Additionally, AI Multiple describes other AI applications that break new ground in the manufacturing process from start to finish.

Just as 3D and CNC modeling can generate prototypes without the physical production costs, “digital twins” provide a virtual representation that can be used for:
• Product development: Products can be studied, developed, and improved before the physical object is produced.
• Design customization: Different versions of the product can be designed according to individual preferences, front-loading the customer’s performance desires.
• Shop-floor improvements: Using digital twins, the production process can be analyzed to predict quality and performance issues before the assembly line starts.
• Logistics optimization: Studying digital twins gives manufacturers a global understanding of materials used and the ability to automate the replenishment process.

And that’s just the beginning

AI Multiple’s reports include a detailed description of the analytical capabilities of AI for manufacturing through data gathered from machines and operators. See Alamira Jouman Hajjar’s article, “Ultimate Guide to Top 10 Manufacturing Analytics Use Cases.”

Though all of us have well-founded doubts about such things as software rollouts and “upgrades,” humans will find it easier to communicate with their machines as AI improves and learns.

The future of AI seems limited only by our imagination.

Discuss

About The Author

Mark Hembree’s picture

Mark Hembree

A former professional musician and longtime editor and writer, Mark Hembree has been a staff writer for marketing companies in the music and automotive industries, and a magazine editor covering the scale model industry for hobby and B2B publications. He’s also written a book about his music days, On the Bus With Bill Monroe: My Five-Year Ride With the Father of Blue Grass (University of Illinois Press, 2022). Mark is an associate editor with Quality Digest.