I recently had an interesting problem in acceptance criteria between 2 companies being different (AQL vs CPK). Acceptance criteria for a given lot was AQL at one company, and the other CPK.

The question was how to convert between the 2 and I found no straightforward answer. However, I thought because AQL sampling is prescribing a sample size for 0 defects you could determine the reliability with confidence (choosing 95%) using n= ln(0.05)/ln(reliability) of a sample.

I also thought I could make use of the k-factor for determining if variable data is meeting a confidence/reliability level. This is typically found in charts in my experience, but I did find the equations to calculate it. I thought this k-factor could be calculated for an equivalent confidence and reliability level to that calculated for AQL and sample size (The sample size that will be used to determine CPK, which is not necessarily the same as AQL sample size). The K-factor being the number of standard deviations the mean of the sample must maintain from the spec limit (2-sided in my case, but that same logic should apply for a 1-sided k-factor) can then be simply divided by 3 to determine the CPK.

Is this an accepted/known method? Would there be any issues with using this to justify equivalence?

The question was how to convert between the 2 and I found no straightforward answer. However, I thought because AQL sampling is prescribing a sample size for 0 defects you could determine the reliability with confidence (choosing 95%) using n= ln(0.05)/ln(reliability) of a sample.

I also thought I could make use of the k-factor for determining if variable data is meeting a confidence/reliability level. This is typically found in charts in my experience, but I did find the equations to calculate it. I thought this k-factor could be calculated for an equivalent confidence and reliability level to that calculated for AQL and sample size (The sample size that will be used to determine CPK, which is not necessarily the same as AQL sample size). The K-factor being the number of standard deviations the mean of the sample must maintain from the spec limit (2-sided in my case, but that same logic should apply for a 1-sided k-factor) can then be simply divided by 3 to determine the CPK.

Is this an accepted/known method? Would there be any issues with using this to justify equivalence?

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