Mar 12 2019
More About the Math of the Process Behavior Chart
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In statistics on time series with “moving” in their name, each value is correlated with past and future neighbors — that is, the series is autocorrelated. It affects the way you can use these statistics to detect anomalies and issue alarms.
The moving range in the XmR chart is a case in point. Its autocorrelation in the moving range chart is self-inflicted. It is autocorrelated by construction, regardless of whether the raw data themselves are.
Some raw data are autocorrelated. For example, when you issue a replenishment order for a part by pulling a Kanban from a bin, you are assuming that the demand for a coming period to match that of the period that just elapsed, with minor fluctuations. Implicitly, you are leveraging the autocorrelation of the part consumption across periods.
On the other hand, if a physical characteristic of a manufactured part is the sum of a constant and noise, then the noises are independent, and therefore uncorrelated. Taking moving ranges introduces an autocorrelation between consecutive values that is absent in the raw data.
Nov 4 2019
Phase Two Charts and Their Probability Limits | Don Wheeler | Quality Digest
“The ability to react to process changes is more important than protecting yourself from occasional false alarms. […] So do not worry so much about straining out the gnats of false alarms that you end up swallowing the camels of undetected process changes.”
Sourced through Quality Digest
Michel Baudin‘s comments:
Of course, we should fact-check their claim. Rankings of engine quality are not readily googleable. The closest I could find is a ranking of engine reliability from 2014 in a UK blog called The Car Expert, based on data from Warranty Direct, a UK provider of extended warranties. According to them, Honda indeed made the most reliable engines:
According to Anna engineers, their machine tools can hold tolerance ten times tighter than necessary. The few quality problems they do have are due to operators picking the wrong parts in assembly. Control charts in the machine shop would produce nothing but false alarms. With the charts crying wolf, the alarms would lose credibility and nobody would react when a real one hit.
In this kind of situation, Wheeler’s statement can be reversed. The ability to protect yourself against false alarms that send your engineers on wild goose chases is more important than detecting changes that hardly ever happen. You do want to detect changes in the process but control charts are too crude a tool for this purpose.
#SPC, #ControlChart
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By Michel Baudin • Press clippings • 0 • Tags: Control Charts, SPC