Weber’s Law at the Gemba:

Ernst_Heinrich_Weber

In today’s post, I am looking at Weber’s Law. Weber’s Law is named after Ernst Heinrich Weber (24 June 1795 – 26 January 1878), a German physician who was one of the pioneers of experimental psychology. I highly recommend the Numberphile YouTube video that explains this in detail.

A simple explanation of Weber’s Law is that we notice things more at a lower intensity than at a higher intensity. For example, the light from your phone in a dark room may appear very bright to you. At the same time, the light from your phone in a bright room may seem insignificant. This type of perception is logarithmic in nature. This means that a change from 1 to 2 feels about the same as a change from 2 to 4, or 4 to 8. The perception of change for an increment of one unit, depends on whether you are experiencing it at a low intensity or a high intensity. At low intensity, a slight change feels stronger.

This is explained in the graph below. The green ovals represent the change of 2 units (2 to 4) and the red ovals represent the same change of 2 units (30 to 32). It can be seen that the perceived intensity is much less for the change from 30 to 32 than for the change from 2 to 4. These are represented by the oval shapes on the Y-axis. To achieve the same level of perceived intensity (change from 2 to 4), we need to create a large amount of intensity (~ change from 30 to 60, a difference of 30 units).

Weber

All of this fall under Psychophysics. Per Wikipedia; Psychophysics quantitatively investigates the relationship between physical stimuli and the sensations and perceptions they produce. What does all this have to do with Gemba and Lean?

How often were you able to see problems differently when you came to the production floor as an outsider? Perhaps, you were asked by a friend or colleague for help. You were able to see the problem in a different perspective and you saw something that others missed or you had a better perception of the situation. Most often, we get used to the problems on the floor that we miss seeing things. We do not notice problems until things get almost out of hand or the problems become larger. Small changes in situations do not alert us to problems. This to me is very similar to what Weber’s law teaches us. Small changes in intensity do not appear in our radar unless we are at the low intensity area.

A good example is to imagine a white sheet of paper. If there is one black spot on the paper, it jumps out to us. But if there are many spots on the paper, an additional dot does not jump out to us. It takes a lot of dots before we realize things have changed. One of the experiments that is used to demonstrate Weber’s law is to do with dots. It is easier to see the change from 10 to 20 dots, rather than the change from 110 to 120 dots.

Weber-Fechner_law_demo_-_dots

Ohno and Weber’s Law:

Taiichi Ohno was the father of Toyota Production System. I wonder how Taiichi Ohno’s perceptive skills were and whether his skillset followed Weber’s Law. I would like to imagine that his perceptive skillset was linear rather than logarithmic. He trained his perceptive muscles to see a small change no matter what the intensity was. Even if he was used to his gemba, he was able to see waste no matter if it was small, medium or large. Ohno is famous for his Ohno circle, which was a chalk circle he drew on the production floor for his supervisors, engineers etc. He would have them stand in the circle to observe an operation, trying to see waste in the operation. Waste is anything that has no value. Ohno was an expert who could differentiate a little amount of waste. Ohno’s Weber’s Law plot might appear to be linear instead of being logarithmic, when compared to a student like me.

Weber Ohno

What we can learn from Weber’s Law is that we need to improve our perception skills to perceive waste as it happens. We should not get used to “waste”. When there is already so much waste, the ability to perceive it is further diminished. It would take a larger event to make us notice of problems on the floor. We lack the ability to perceive waste accurately. We can only understand it based on what has been perceived already. This would mean that we should go to gemba more often, and each time try to see things with a fresh set of eyes. As the Toyota saying goes, we should think with our hands and see with our feet. Change spots from where you are observing a process. Understand that gemba not only means the actual place, but it also includes people, equipment, parts and the environment. We should avoid going with preconceived notions and biases. As we construct our understanding try to include input from the actual users/operators as much as possible. Learn to see differently.

Final Words:

One of the examples I came up with for this post is about cleaning rooms. Have you noticed that cleaner rooms get messy fast? Actually, we perceive a slight increase in messiness when the room is clean versus when it is not. The already messy room requires a larger amount of mess to have a noticeable difference. What Weber’s law shows us is that our natural instinct is not to think linearly.

Humans evolved to notice and minimize relative error. As noted on an article on the Science20 website:

One of the researchers’ assumptions is that if you were designing a nervous system for humans living in the ancestral environment, with the aim that it accurately represents the world around them, the right type of error to minimize would be relative error, not absolute error. After all, being off by four matters much more if the question is whether there are one or five hungry lions in the tall grass around you than if the question is whether there are 96 or 100 antelope in the herd you’ve just spotted.

The STIR researchers demonstrated that if you’re trying to minimize relative error, using a logarithmic scale is the best approach under two different conditions: One is if you’re trying to store your representations of the outside world in memory; the other is if sensory stimuli in the outside world happen to fall into particular statistical patterns.

Perhaps, all this means that we learn to see waste and solve problems on a logarithmic scale. And as we get better, we should train to see and solve problems on a linear scale. Any small amount of waste is waste that can be eliminated and the operation to be improved. It does not matter where you are on the X-axis of the Weber’s law plot. I will finish with an excellent anecdote from one of my heroes, Heinz von Foerster, who was also a nephew of Ludwig Wittgenstein. I have slightly paraphrased the anecdote.

Let me illustrate this point. I don’t know whether you remember Castaneda and his teacher, Don Juan. Castaneda wants to learn about things that go on in the immense expanses of the Mexican chaparral. Don Juan says, “You see this … ?” and Castaneda says “What? I don’t see anything.” Next time, Don Juan says, “Look here!” Castaneda looks, and says, “I don’t see a thing.” Don Juan gets desperate, because he wants really to teach him how to see. Finally, Don Juan has a solution. “I see now what your problem is. You can only see things that you can explain. Forget about explanations, and you will see.”

You become surprised because you abandoned your preoccupation with explanations. Therefore, you are able to see. I hope you will continue to be surprised.

In case you missed it, my last post was OODA Loop at the Gemba:

I also encourage the readers to check out my other similar posts:

Drawing at the Gemba

The Colors of Waste

Maurice Merleau-Ponty’s Lean Lessons

7 thoughts on “Weber’s Law at the Gemba:

  1. Wow, really liked this post. Can i find a link to each post? I want to share the link to colleague, but only find a link to the page.
    Regards Halvor

    Like

  2. I’m not sure whether it relates to the same phenomenon, but I think it helps by not looking at the big picture when you try to spot waste, but to isolate a small area and focus just on that area. I’m sure you will “see more by seeing less”.
    I see the same happening with data. Companies tend to aggregate all their numbers over time (daily, weekly, monthly etc.) and by doing so to lose all the underlying details that will show the variation (waste) in their processes. A good measure relates to the behavior of a single unit that you observe. So look at subsequent individual units and cycles instead of talking about stuff like the monthly productivity, OEE, turnaround or on-time numbers.

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