Bodo Wiegand heads Germany’s Lean Management Institute. In his latest newsletter, on Wiegand’s Watch, he explains his concerns about the future competitiveness of German companies. Here is my full translation of his article, followed by my comments:
Bodo Wiegand: “A huge potential is not realized and simply left fallow – can we really afford that?
I think we cannot afford it.
In China and India, more engineers are trained each year than we have in Germany in total, and then we fail to exploit the huge potential of the engineers we have. Why? Because we do not want to give up our fiefdoms, our functional thinking and our single-minded concern for our turf.
Six years ago, one of the first posts in this blog — Is SPC Obsolete? — started a spirited discussion with 122 comments. Reflecting on it, however, I find that the participants, including myself, missed the mark in many ways:
- My own post and comments were too long on what is wrong with SPC, as taught to this day, and too short on alternatives. Here, I am attempting to remedy this by presenting two techniques, induction trees and naive Bayes, that I think should be taught as part of anything reasonably called statistical process control. I conclude with what I think are the cultural reasons why they are ignored.
- The discussions were too narrowly focused on control charts. While the Wikipedia article on SPC is only about control charts, other authors, like Douglas Montgomery or Jack B. Revelle, see it as including other tools, such scatterplots, Pareto charts, and histograms, topics that none of the discussion participants said anything about. Even among control charts, there was undue emphasis on just one kind, the XmR chart, that Don Wheeler thinks is all you need to understand variation.
- Many of the contributors resorted to the argument of authority, saying that an approach must be right because of who said so, as opposed to what it says. With all due respect to Shewhart, Deming, and Juran, we are not going to solve today’s quality problems by parsing their words. If they were still around, perhaps they would chime in and exhort quality professionals to apply their own judgment instead.
“[…]As a zone leader, Stinson is responsible for about fifteen employees on a section of the production line that makes parts for Steelcase’s Ology series—height-adjustable tables built for the standing-desk craze. Until last year, the plant workers had to consult a long list of steps, taking pains to remove the correct parts out of a cart filled with variously sized bolts and screws and pins and to insert each one in the correct hole and in the correct order. Now computerized workstations, called ‘vision tables,’ dictate, step by step, how workers are to assemble a piece of furniture. The process is virtually mistake-proof: the system won’t let the workers proceed if a step isn’t completed correctly. We stood behind a young woman wearing a polo shirt and Lycra shorts, with a long blond ponytail. When a step was completed, a light turned on above the next required part, accompanied by a beep-beep-whoosh sound. A scanner overhead tracked everything as it was happening, beaming the data it collected to unseen engineers with iPads.[…] ”
Sourced through The New Yorker
Michel Baudin‘s comments: This is excerpted from a long article entitled Welcoming Our New Robot Overlords, from the 10/23/2017 issue of The New Yorker that caught my attention because it’s not about robots and it seems to be in the same spirit as Omron’s Digital Yatai back in 2002: using technology to eliminate hesitation and to mistake-proof operations that are too long or have too many variants to allow operators to go “on automatic” while performing them.
When repeating the same 60 seconds of work 400 times in a shift, operators quickly develop the ability to execute rapidly and accurately with their minds elsewhere. If on the other hand, the takt time is 20 minutes or the work is customized for every unit, the work requires the operators’ undivided, conscious attention and their productivity is increased by systems like the vision tables described in the article, that prompt them for every step and validate its completion.
iscussion on LinkedIn with the following question:
“My Sensei Mr. Nakao once told me: ‘The hardest thing to do in TPS is to create flow.’ What do you think about that?”
It started a spirited debate, with the following participants, in alphabetical order: , Rob Beesley , , , , , , ,, , , , , , Jerry O’Dwyer, , , , , , ,
Sourced through LinkedIn
The following is a digest of my own answers, collated before they vanish in the replies-of-replies bowels of LinkedIn.
Mass shootings versus number of guns by country
Mass shootings versus guns per capita by country
“When the world looks at the United States, it sees a land of exceptions […] But why, they ask, does it experience so many mass shootings?[…] Perhaps, some speculate, it is because American society is unusually violent. Or its racial divisions have frayed the bonds of society. Or its citizens lack proper mental care under a health care system that draws frequent derision abroad. These explanations share one thing in common: Though seemingly sensible, all have been debunked by research on shootings elsewhere in the world. Instead, an ever-growing body of research consistently reaches the same conclusion. The only variable that can explain the high rate of mass shootings in America is its astronomical number of guns.”
The source for both charts is Adam Lankford from the University of Alabama. The charts Include countries with more than 10 million people and at least one mass public shooting with four or more victims.
Sourced from The New York Times
Michel Baudin‘s comments: Six months ago, I was bemoaning the absence of scatterplots in business analytics and more recently complimenting the New York Times for the sophistication of its graphics. Manufacturing professionals should not be shy about using scatterplots, as they have learned to do in Middle School. Here, they are used to highlight outliers, which isn’t the most common application. What this article — and these charts — show is how the tool can be used not just to solve technical problems but to inform a political debate as well.-
5 years ago, I pointed out several omissions in the ASQ’s History of Quality pages, which have not been corrected. Specifically, I faulted them for ignoring the TPS/Lean approach to quality, the role of interchangeable parts technology, and the Roman philosopher Cicero, who coined the word “quality.” The first page, however, also contains what I think is an error of commission, where it credits the guilds of medieval Europe as precursors in the field, as follows:
“From the end of the 13th century to the early 19th century, craftsmen across medieval Europe were organized into unions called guilds. These guilds were responsible for developing strict rules for product and service quality. Inspection committees enforced the rules by marking flawless goods with a special mark or symbol.[…] Inspection marks and master-craftsmen marks served as proof of quality for customers throughout medieval Europe. This approach to manufacturing quality was dominant until the Industrial Revolution in the early 19th century.”
Don Wheeler’s Understanding Variation starts with a chapter entitled “Data are random and miscellaneous” that contains no discussion of any part of its title. Implicit in Wheeler’s book, however, is the view that data consists of tables of numbers, representing either measured variables — lengths, weights, densities,… — or event occurrence counts — defective units, defects, machine failures,…
Many times, I have quoted computer scientist Don Knuth on this subject, saying that data is “the stuff that’s input or output,” meaning anything that can be read or written, and it includes much more than tables of numbers. The data we work with today includes, for example, the following:
- Unstructured text, like 25,000 incident reports written by maintenance techs all over the world in their versions of English about problems with jet engines, or thousands of product reviews posted by consumers on e-commerce sites
- Images, like photographs of visual defects on products, or electron-microscope images of integrated circuits.
- Videos recordings of operations.
Analyzing data about a manufacturing process today means extracting information from all sources. The state of the art, based on automatic data acquisition and databases includes analytical techniques that were unthinkable in Shewhart’s day, known under the labels of data science, data mining or machine learning.
“Six Sigma as a problem-solving methodology causes many hang-ups for Japanese managers. Many Americans seeking training in Six Sigma in Japanese organizations face resistance with little explanation as to why. This often leads to frustration and contempt towards management. They write off the Japanese resistance to the training as resistance to change, preventing growth and feeling unrepresented.“
Sourced through Nipponica
Michel Baudin‘s comments: In this post, Ian Moore makes the case that rejection of Six Sigma by Japanese organizations is rooted in the national culture, which is ironic, given that Six Sigma’s Black Belt concept was borrowed from Japanese martial arts with the obvious intent of creating the perception of a connection to Japanese culture.