Archive for the 'statistical process control' Category

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 [...]