In Defense of Fuzzy Thinking

With continuous improvement, wherever we are, there we are

Published: Thursday, February 1, 2018 - 12:02

It’s not easy to find topics to write about, and even if I find good topics, it has to pass my threshold level. As I was meditating on this, I started to think about procrastination and ambiguity. So my column today is about the importance of “fuzzy concepts.” I am using the term in a loose sense and will not go into depth or specifics.

We like to think in boxes or categories. It makes it easy for us to make inferences and aids in decision making. “She is tall” or “He is short”; “This is hard” or “This is easy.” This is a reductionist approach and from a logic standpoint, this type of thinking is called “Boolean logic” and is based on a dichotomy of true or false (0 or 1). Something is either “X” or “not X.” This type of thinking has its merits sometimes.

In contrast, fuzzy logic helps us in seeing the in between. The fuzzy logic approach utilizes a spectrum viewpoint. It starts as 0 at one end and slowly increases bit by bit all the way to 1. We can express any point between 0 and 1 as a decimal value.

In the picture above, the left point is white (0), and as we go toward the right it changes the color to black (1.0) at the far right. Any point in between is neither white nor black. It is just in between, and we can identify the gradient as a value between 0 and 1.

In this vein, if I am to get myself to write an article, I could be either prepared and ready or not prepared and ready. I could wait for a long time for the inspiration to strike or have an epiphany that would add value to the article. From a Boolean standpoint, this is black-and-white thinking. I have to wait until I am fully ready (1) to write the article. If I am not ready (0), I should not write the article.

The fuzzy-thinking concept is not recent. In fact, there is an old Greek paradox called Sorites paradox, which is attributed to Megarian logician Eubulides of Miletus. The word “Sorites” is derived from the Greek word soros, which means “heap.” The paradox is as follows: If you have a heap of sand, and you take away a grain, would that heap still be a heap? What would happen if you keep taking grains away? At what point does it cease being a heap? We can express this in the Boolean logic as: (1) = heap, and (0) = no heap. However, if we use the fuzzy logic, we could define what a full heap means and what “no heap” means. Anything in between can be defined as a “partial heap.” Fuzzy logic helps us to add a matter of degree to any statement.

The fuzzy-thinking concept goes really well with continuous improvement philosophy and the thought that lean is a journey and not a destination. We will never be 100-percent complete with our improvement. We are always incomplete with our improvement, and it is OK that we are incomplete. We have to keep on improving. We do not have to wait until we have the perfect idea or the expensive machinery or tool to start improving our processes. We do not have to wait for others to start on the improvement journey. In a Zen-like fashion, “wherever we are, there we are”—the right place to start improving. We will always be between 0 and 1 in terms of perfection of the process. We will always be on the journey and never at the destination. Taiichi Ohno, the father of Toyota Production System, had a great saying that encapsulates the fuzzy concept: “Don’t seek perfection; 60 percent is good enough!”

I will finish with a story I read online from an anonymous source:
The family was driving to their destination for a vacation. The child asked his father, “Are we there yet?”

The father replied, “No, son. We are always here.”

I wish all of readers a Fuzzy 2018. You are exactly where you are to start exactly what you want to start. 

Always keep on learning....

First published on the Harish's Notebook blog.

About The Author

Harish Jose

Harish Jose has more than seven years of experience in the medical device field. He is a graduate of the University of Missouri-Rolla, where he obtained a master’s degree in manufacturing engineering and published two articles. Harish is an ASQ member with multiple ASQ certifications, including Quality Engineer, Six Sigma Black Belt, and Reliability Engineer. He is a subject-matter expert in lean, data science, database programming, and industrial experiments, and publishes frequently on his blog Harish’s Notebook.