Elaborating a control chart with skewed data

#1
I searched a lot in the forums but couldn't find a proper solution.

I have a "production yield" parameter that mostly remains at 100% (or very close to it - in the 98 - 99% range).
However sometimes it goes from 99 to 96%.

Is it correct to use a control chart in these cases? There's a large gap between my data and the upper control limit, and my concern is that the lower limit is being skewed by the data.
 
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Steve Prevette

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#2
First, do take a look at a p-chart control chart. This will take into account if the 96% is from one out of 25, or 10 out of 250, or . . .

If it works out that the rare 96% is below the resulting LCL - and if you can find out why, it is proper to not include that result in the baseline average. Also be sure you are not averaging the percent values (96%, 99%, 100% etc) but are adding up the numerators and the denominators and then dividing. This will also help keep an odd result from a small sample size from affecting the baseline.

I do have a resource area here on SPC, and there is a writeup for the p-chart at https://elsmar.com/elsmarqualityforum/attachments/p-charts-pdf.19353/
 
#3
First, do take a look at a p-chart control chart. This will take into account if the 96% is from one out of 25, or 10 out of 250, or . . .

If it works out that the rare 96% is below the resulting LCL - and if you can find out why, it is proper to not include that result in the baseline average. Also be sure you are not averaging the percent values (96%, 99%, 100% etc) but are adding up the numerators and the denominators and then dividing. This will also help keep an odd result from a small sample size from affecting the baseline.

Thank you so much for your response!

Should I use the p-chart even if my results are still a "pass" result?
My specification is 95%
 

Welshwizard

Involved In Discussions
#5
Hi Cassandreta,

Going back to your original question, it sounds like you are tracking a characteristic which is aggregated i.e. its a summation of other sources. In this instance the data may well be gapped to the upper and lower limits because the signals are hidden in the noise.

My advice is to dive deeper into the data to try to find streams that will give you a clue about the behaviour of the underlying process. It doesn't matter what chart you use or what assumptions you make about the distribution.

The Individual moving Range chart will be fine for this.

Btw, the yield of a process is directly related to its predictability, if we don't have predictability then any estimates are flawed because the process has a tendency to go on walkabout.

Hope this helps
 
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