Method to compute Cpk for Attribute Data?

reynald

Quite Involved in Discussions
#1
I saw this on a six-sigma Process Capability training that i attended.
Cpk for an attribute data was totally news for me.
Here is thier way of doing it:
Step1: From attribute data, compute process yield.
Step2: From the yield information, determine Z-value correspoding to the same
area under the Normal Curve. Call this Sigma Level. In excel SigmaLevel=NORMINV(YIELD,0,1)
Step3: Remember that Z-score = (X-Mean)/(Sigma). This is almost similar to Cpu=(USL-Mean)/(3*Sigma) except for the factor 1/3. Therefore it is possible to think that Cpk = Sigma Level/3.

What do you think? Is that logic valid? Is this method generally acceptable?
 
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Miner

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Staff member
Admin
#2
Your math is correct. In the six sigma community, the attribute data is typically converted to a z-score only. Yes, you can proceed further to the Cpk, but everyone needs to be clear that the meaning is different.

A variable Cpk implies a location and an amount of variation to a specification. An attribute Cpk only implies a percentage of nonconforming material. If you want to compare a variable process to an attribute process, it is better to convert both to a z-score then compare. A z-score implies nothing more than % conforming.

Please remember that you need much larger sample sizes for attibute data to have the same level of confidence in the Cpk value.
 

bobdoering

Stop X-bar/R Madness!!
Trusted Information Resource
#3
If the math supports it or not - and we have seen the math fool us - it is hard to imagine that you can tell if your process is centered or not if your measurement system is just looking at the "ends", as in attribute go/no go.

I am going to have to ponder this...something funny about this.... :cool:
 

reynald

Quite Involved in Discussions
#4
Yes, you can proceed further to the Cpk, but everyone needs to be clear that the meaning is different.
That is one more issue. Cpk i believe is first and foremost a way of communicating the process's status. And the general perception is that Cpk is for variables only. Now if i were to present the Cpk for attribute, im sure that my audience would interpret it the same way as they interpret the Cpk for variable. That can be confusing. I wish they called it by a new name.

Anyway i believe Yield and %Defect sums it all for attribute data. I dont see any special need for translating it to Z-score, even to Cpk. Simple is best.
 
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