Introducing chaos theory to automation

Just mentioning "chaos theory" usually generates a smile, plus the confession: "We've got a lot of it here." Chaos is a hot topic with information and encryption folks, game theorists, economists, and geophysicists. But what does it have to do with engineers and automation? Chaos theory dates back to Einstein.


Just mentioning "chaos theory" usually generates a smile, plus the confession: "We've got a lot of it here." Chaos is a hot topic with information and encryption folks, game theorists, economists, and geophysicists. But what does it have to do with engineers and automation?

Chaos theory dates back to Einstein. It's a language that envisions reality as "energy" instead of "matter." It replaces industrial imagery—the superstition in science, business, and politics that reality is "structured"—with a language of dynamic and organic images. Organic forms can be observed in everyday objects and in corporate business plans.

Another significant chaos concept is "self-organization" rather than structured organization. For example, interpersonal relationships are not structured, but they are organized, for better or worse, every day. Businesses also consist of webs of internal and external relationships.

Chaos also stresses adaptive identities, rather than defined roles. This recognizes that automation users and managers are also people with varying motivations. Consequently, one chaos rule is that each organizational change produces an equal, offsetting behavioral change.

Complexities of issues

Structures can be taken apart, but energy cannot. So, chaos theory talks about "connectedness." This term includes a wider circle of issues and people, and considers a richer, more iterative context. Context makes a difference.

This is important because so many failures in manufacturing and automation are the result of issues unrelated to design intent. A basic chaos theorem is that small changes in a variable can eventually cause huge imbalances in an overall system. A seemingly small decision or a minor problem with one sensor can generate a snowball effect.

Consequently, chaos theory refers to "synthesizing" rather than "analyzing." A legacy of "structure" is analytical logic, which dissects issues to deal with them one at a time. Chaos considers "analysis" fundamental to logic and science, but adds that analysis takes focus away from "synthesis," which is searching the context for explanation.

Though technology is logical, its application is not. Automating a new line is like introducing a strange animal to an environment—it might thrive like rabbits in Australia or fail. Chaos theory applies these concepts and a mathematical system to help predict which systems will succeed.

A favorite premise of chaos is: "If you play a game, it changes." Automation systems are proposed, sold, and assessed on the premise that they'll reduce production costs. However, they can also increase other expenses, such as facility and design costs. It's no wonder that these are manufacturers' biggest, most sensitive decisions.

What if the model changes?

In an industrial model, 10,000 units cost approximately 10,000 times as much as one unit. However, in a post-industrial model, 10,000 units may move toward costing approximately the same as one former unit, all factors considered. This trend pressures businesses to find markets that can absorb more products. It also drives corporate consolidations, mergers, and takeovers.

But what if the game changes? For instance, we know that production cost is marginalized, while design and facility costs increase. So what's relevant for total cost is "how many units over time?" The selling price helps determine accessibility, which plays into "how many units," potentially bringing down "cost." In other words "cost" is now determined by—among other things—the selling price!

Chaos is all around you. Watch for the images and answers.

Author Information

David Bell, owner, Blue Skies, Victoria, B.C., Canada Comments? E-mail

Chaos Theory Websites

The Maryland Chaos Group at

Center for Nonlinear Dynamics at the University of Texas at Austin at

Applied Chaos Lab of Georgia Tech University at

Chaos Research Group in the College of Engineering at the University of Tennessee at

Santa Fe Institute at

Santa Fe Chaos in Manufacturing Conference and bibliography for chaos sections of R.Morely Inc.'s The Barn's Home Page at

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