Have you ever heard something like: “I’m committed to Dr. Deming’s approach [or Six Sigma or lean or TPS, it doesn’t matter], but executives don’t seem to listen anymore. All they do is keep interrupting my very clear explanations with, ‘Show me some results, then show me what to do.’ I was shocked that my demonstration of the red bead experiment neither awed nor convinced them; several of them even walked out during it. Which of Dr. Deming’s 14 Points should I start with to get their attention and the results they want?”
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My answer would indeed be: None of them—and all of them!
If anyone either continues to ask that question or is confused by my answer, please read this, then heed the following advice from Deming himself. Why? Because you don’t quite get his message.
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Comments
Deming's 14 Points
Right on Davis. In the UW-Madison College of Engineering, one of my statistics professors knew that I came into the program with over 10 years applying SPC. He asked, How many data points are needed to estimate limits? Twenty-five, I quickly informed all of my classmates. Professor Wu, then pointed out to the class that I was wrong. The answer is two. I went on to teach statistics in the College of Engineering and never forgot the lessons of Wu, Bisgaard, Ermer, and the most wonderful George Box who quoted Cole Porter's song lyrics Experiment. "Experiment. . . And it will lead you to the light." Simply put, use statistics to explore.
Data points needed to estimate limits!
The answer by the professor is two.
I do not quite understand this.
Can you pl enlighten me !
Regards and thanks
virendra
Estimating Limits
Two data points provide a mean and a standard deviation. While recognizing that they are a limited sample size, we still have a ballpark estimate of a process. We move on from there. Also, consider that with two data points we can conduct Monte Carlo simulations for whatever hypothesized distribution we may want to explore. Claiming that an absolute number of data points is needed, say 25 or 30, before we can estimate control limits is the stuff statistical immaturity. Needless to say, however, there are a lot of caveats that experienced statistician/engineers know how to conceptualize and act on.
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