I’ve always thought that knowing when something was going to fail was unknowable. Of course, predictive maintenance vendors shout from the rooftops about the effectiveness of their technology. Even the onboard computers tell you to replace components, somehow knowing when the failure will occur.

But really, most failures are not related to wear or aging, so how can we know what is going to happen and when? This discussion leads me into the sales pitches for AI (Artificial Intelligence). Can we ever get to the point where there is so much data that we can really predict the future?

It turns out people have been thinking about this kind of stuff for 80+ years. There is a weird (to me) distinction in management called Complexity versus Complicated. In a thesaurus, they are synonyms, but in real life, they are used differently. They are different, but to confuse them could be either disastrous or, at the least be a giant waste of money.

The dictionary, in this case, is useless.

Complicated: ADJECTIVE consisting of many interconnecting parts or elements; intricate.

Complexity: NOUN the state or quality of being intricate or complicated.

We must go back to 1938 to find a helpful definition of the two related concepts.

Complicated vs. Complex

Complicated System -Exhibits linear behavior and is predictable. It is equal to the sum of its parts.

Many machines and all modern trucks are complicated. We can study them; we can learn the rules and eventually understand how they work. Some failure is complicated. We must follow the rules to avoid failure.

Complicated problems can be hard to solve, but they are addressable with rules and recipes, like the algorithms that place ads on your Twitter feed. They also can be resolved with systems and processes, like the hierarchical structure that most companies use to command and control employees.

Complex System -Exhibits Non-Linear responses, unpredictable behavior to inputs.

The systems respond to positive and negative feedback, spontaneous emergence see. Complex systems cannot be adequately described by analyzing the components alone.

(Source: Ciliers, 1938).

There are many aspects to fleet maintenance that are complex, and applying rules of thumb, following recipes, or just using algorithms will not fly. Think about the complexities of supervision or of changing maintenance strategy. You might be able to follow the book, but two companies following the same book will end up in entirely different places. Unlike complicated repairs, if you follow all the rules, you are not assured success.

The solutions to complicated problems don’t work as well with complex issues, however. Complex problems involve too many unknowns and too many interrelated factors to reduce to rules and processes. A technological disruption like blockchain is a complex problem. A competitor with an innovative business model — an Uber or an Airbnb — is a complex problem. No algorithm will tell you how to respond.

We can ignore this problem  as an exercise in semantics, except for one thing: When facing a problem, says Nason (from It’s Not Complicated: The Art and Science of Complexity in Business Rotman-UTP Publishing, 2017) Rick Nason) managers tend to automatically default to complicated thinking. Instead, they should be “consciously managing complexity.” Nason explains how.

Consciously managing complexity in a business context is broadly a function of four different strategies or tactics. They are:

(1) recognize which type of system you are dealing with;

(2) think “manage, not solve”;

(3) employ a “try, learn, and adapt” operating strategy;

(4) develop a complexity mindset.

If I get back to complexity for failure prediction, I hope we are on the brink of making the complexity merely complicated. Is it possible that complexity means that you don’t understand the system?

Think about just over 100 years ago. There were several “cures’ for common bacterial illnesses. The illnesses acted like a complex system where the outcome was not a given. People were given various cures (many of which were poisons that worked much like chemotherapy today). They didn’t work reliability until the underlying causation was understood (bacteria), and a way to attack them directly was developed (antibiotics).

I’m still not sure! Be alert for continued complexity.

Good luck

Joel