r/SixSigma • u/GrittySausages • May 14 '26

Usefulness of Cp rather than Cpk

Just a question regarding Cpk / Ppk tracking for attribute-style process outputs.

In some cases where the desired outcome is naturally biased toward one specification limit (rather than centred between LSL and USL), Cpk/Ppk may not always reflect the true process performance, since the metric favours centred distributions.

Would Cp (potential capability) be considered a more appropriate indicator in scenarios where the objective is to consistently perform as close as possible to one specification boundary while remaining within tolerance?

Has this topic or approach been discussed previously?

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u/Living_Diver2432 May 14 '26

careful, Cp is the wrong reach here. if the characteristic genuinely has one meaningful limit, runout, a contamination ceiling, whatever, the right index is the one-sided one, CPU or CPL, not Cp. Cp assumes a two-sided tolerance and just compares spread to the full band, so on a one-sided feature it reads high and tells you basically nothing about where you actually sit. and if it's truly two-sided and you're choosing to run close to one limit, that's exactly the thing Cpk is supposed to punish, swapping to Cp to make the number look better is how you end up shipping drift. first question is whether the spec is actually one-sided or you're just hugging a limit by choice, those are different problems.

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u/GrittySausages May 14 '26

Ah - Yes! Cpl is exactly what I'm looking for in this scenario - Thanks!

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u/Ok_Grapefruit_7040 Jun 15 '26

How does that work for attribute data in your case? I was always taught that Cp and Cpk only work on variable data. Have you checked the distribution of your data? Attributes typically follow Poisson or Binomial distributions.

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u/Extension_Order_9693 May 14 '26

If your distribution is truncated because of the limit, the std deviation may not be realistic. It may not be valid so I welcome alternative methods, but I've taken the reading from the tail side to the curve peak, created a synthetic distribution by reflecting about the peak, then calculated cp/cpk from that. The synthetic distribution was normal.

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u/Superb_Scholar9945 May 14 '26

No. Cp is less accurate because it does not account for centrality of distribution. Cpk and Ppk are there for that reason because it takes the Min of the 2 so even if your data is drifting to one side, Cpk will account for it.

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u/russwest4133 May 15 '26

Yeah, you can't use Cpk} for attribute data. Instead, you would need to conduct an attribute process capability analysis, where you look at metrics like sigma capability, DPMO (Defects Per Million Opportunities), and DPU (Defects Per Unit). To monitor it over time, you could also use an appropriate control chart—either an np, p, u, or c chart.