Knowing when something is going to fail seems impossible, though predictive maintenance vendors often tout the effectiveness of their technology. Onboard computers can notify the fleet when it is time to replace a component, calculating when a failure is likely to occur.
In reality, however, many failures are not related to wear or aging, so how can we know what is going to happen and when? This discussion leads into the sales pitches for artificial intelligence (AI). Will we reach a point where there is so much data that we reliably can predict the future?
People have been thinking about these issues for more than 80 years. There is a distinction in management called “complex versus complicated.” In a thesaurus, they are synonyms, but in real life, they are used differently. To confuse the two terms could be disastrous or—at the very least—a waste of money.
For these two terms, the dictionary is useless.
Complicated: (adjective) consisting of many interconnecting parts or elements; intricate.
Complexity: (noun) the state or quality of being intricate or complicated.
A more helpful definition of the two related concepts can be found when looking at systems.
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 and 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 a Twitter feed. They can also 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 with spontaneous emergence. Complex systems cannot be adequately described by analyzing the components alone.
There are many aspects to fleet maintenance that are complex, and applying rules of thumb, following recipes, or just using algorithms will not always work. Think about the complexities of supervision or of changing a maintenance strategy. It may be possible to follow the book, but two companies following the same book could end up in entirely different places. Unlike complicated repairs, following all the rules does not ensure 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—such as Uber or an Airbnb—is a complex problem. No algorithm can determine how to respond.
We can ignore this problem as an exercise in semantics, except for one point. Rick Nason, associate professor of finance at Rowe School of Business in Nova Scotia, wrote in his book, “It’s Not Complicated: The Art and Science of Complexity in Business,” that when facing a problem, managers tend to automatically default to complicated thinking. Instead, they should be “consciously managing complexity,” Nason explained.
Consciously managing complexity in a business context is broadly a function of four different strategies or tactics, he said. They are:
- Recognize which type of system you are dealing with.
- Think “manage, not solve.”
- Employ a “try, learn, and adapt” operating strategy.
- Develop a complexity mindset.
If we 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 you don’t understand the system?
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 (i.e. bacteria) was understood, and a way to attack them directly (i.e. antibiotics) was developed.
We can look at maintaining a fleet in much the same way. Solutions to various problems work more reliably when we fully understand the underlying cause.
Joel Levitt is the president of Springfield Resources, a management consulting firm that services a variety of clients on a wide range of maintenance issues. Levitt has trained more than 17,000 maintenance leaders from more than 3,000 organizations in 38 countries. He is also the creator of Laser-Focused Training, a flexible training program that provides specific, targeted training on your schedule, online to one to 250 people in maintenance management, asset management, and reliability.
