How Western Electric rules mislead in statistical process control
The statistical model behind control charts for in-control processes is based on the assumption a Gaussian process with no autocorrelation (i.e. independent) with a constant mean and constant variance: in other words a white noise process. The various Western Electric rules try to find patterns that are not white noise, and thus show that the [...]
More circular reasoning on statistical process control
After my last post I received a number of comments on the website KPIExperts. Most of them completely misunderstood my point, and their misunderstanding was so fundamental that rather than reply to their comments individually I decided to write a new post. I myself have trained thousands of people in SPC over more than two [...]
Circular Reasoning in Statistical Process Control
In a Statistical Process Control (SPC) chart, measurements are plotted on a chart with upper and lower “control limits.” The idea is to compare the plotted points with the limits to see if a process is stable, and to identify “special” causes of variation. The control limits are supposed to indicate when action should be taken [...]
The numbers from your performance indicators cannot tell the whole story
Consider the figure below, which shows the KPI (key performance indicator) ”inventory age in days” for 20 months. Looking at the chart raises a lot of questions. Why did inventory age shoot up by almost 40% between the 7th month and the 9th? Why did it suddenly drop down again? Why was there much more variability in [...]
How statistical process control charts mislead
The statistical process control chart is designed to help differentiate between what are usually called “common” causes of variation and “special” causes. But this is very misleading. Typically, six-sigma consultants and various authors advise you to ignore data between control limits because it is “random.” They say to only make process adjustments when the process [...]
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