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Numerous facets of the AI revolution are aimed at knowledge work – partly because these new models excel at thinking in human-like ways. We aren’t accustomed to computers having the ability to ponder tasks typically done by paralegals or writers, for instance.

AI in manufacturing is quite distinct. It automates monotonous tasks and enhances accuracy and precision.

While attending various conferences and events, I've been reflecting on some of the core principles of manufacturing automation using AI. These seem to offer a comprehensive view of what firms are doing to modernize conventional processes.

Digital Twinning

I've discussed this topic previously, and I've heard numerous experts explain it in diverse ways. (Honestly, it feels like I've heard it explained countless times.)

To put it simply, here's how digital twinning relates to manufacturing.

The user creates a digital replica of a simulated factory process to depict the desired outcome. Then, they execute the actual process and compare it to the digital twin. This serves as a form of precise reinforcement that can aid in detecting defects and process mistakes.

That's the general concept, but there's more to consider regarding how digital twinning is implemented. For example, in a Ted talk focusing on this kind of application, John Hart (who has ties to MIT) describes using digital twinning like this:

“Before a part is ever printed, such as the watch housing in my hand, we're able to simulate the process,” he says. “We built and adopted software tools to simulate the process, to virtually lay out the print, and then when the printing starts, we do a relatively straightforward thing that's really important. The printer takes pictures every time it completes a layer, and then, on the fly, looks for microscopic flaws. And the process runs uninterrupted. If flaws are detected, (or) as flaws are detected, it can identify ways to correct for those flaws. And at the end of the print, there is a digital twin of the printed geometry that can be compared to the expected outcome, and can also be used to train the system over time.”

That's one approach, but there are others as well. Digital twinning is quickly becoming a standard practice across industries ranging from defense to transportation and medical equipment manufacturing.

Cobots

Then there are "cobots," a blend of "collaborative robots" that people discuss when referring to automated machines working alongside humans.

What do these machines do?

Well, cobots can handle a variety of tasks, from repetitive motion assembly to inspection and moving materials to precisely where they need to be for a manufacturing process.

The key point is that cobots are designed to assist humans – whereas industrial robots are built for precision, cobots are usually created with economics and safety in mind.

Factory In a Box

When you mention the "factory in a box," you're talking about self-contained systems that can be set up at demand locations. They are also highly adaptable in producing various types of products.

“Equipped with AI-driven automation, IoT sensors and real-time data analytics, these units enable flexible, localized production,” write Matthew Finio and Amanda Downie for IBM. “This allows companies to bring manufacturing closer to demand, reduce logistics costs and swiftly respond to changing requirements.”

Centralized Control of Machines

There is also often a requirement for specialized systems to provide centralized control in manufacturing settings. Comprehensive architectures establish benchmarks and facilitate more thorough evaluations and process enhancements.

This resource from Tupl outlines Manufacturing Execution Systems (which authors refer to as a “bridge between the production floor and the enterprise’s higher-level operations”), Enterprise Resource Planning software and SCADA (Supervisory Control and Data Acquisition) data collection systems on the floor.

More on industrial AI

In his talk, Hart also speculates about three significant advancements with multimodal systems.

One is the increase in new product design concepts.

The second is the manufacturing flexibility of a "factory in a box" design.

The third is changes to the value chain, where these innovations will alter production and delivery for companies.

These are some of the foundational technologies that are "simplifying complexity," in Hart's words, and driving novel methods of production.

For individuals familiar with discussing business process optimization, I would use the term "business process revolution" or "business process transformation" in contemplating major shifts to AI-native designs that will make tomorrow's factories appear significantly different from what we have today.

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