“It could be argued that the use of and C_{pk} (with sufficient sample size) are far more valid estimates of long and short term capability of processes since the 1.5 sigma shift has a shaky statistical hot Christian dating foundation.” Eoin

“C_{pk} tells you what the process is CAPABLE of doing in future, assuming it remains in a state of statistical control. tells you how the process has performed in the past. _{pk}, because the process is not in a state of control. The values for C_{pk} and will converge to almost the same value when the process is in statistical control. that is because sigma and the sample standard deviation will be identical (at least as can be distinguished by an F-test). When out of control, the values will be distinctly different, perhaps by a very wide margin.” Jim Parnella

“C_{p} and C_{pk} are for computing the index with respect to the subgrouping of your data (different shifts, machines, operators, etc.), while P_{p} and are for the whole process (no subgrouping). For both and C_{pk} the ‘k stands for ‘centralizing facteur it assumes the index takes into consideration the fact that your data is maybe not centered (and hence, your index shall be smaller). It is more realistic to use P_{p} and than C_{p} or C_{pk} as the process variation cannot be tempered with by inappropriate subgrouping. However, C_{p} and C_{pk} can be very useful in order to know if, under the best conditions, the process is capable of fitting into the specs or not.It basically gives you the best case scenario for the existing process.” Chantal

“C_{p} should always be greater than 2.0 for a good process which is under statistical control. For a good process under statistical control, C_{pk} should be greater than 1.5.” Ranganadha Kumar

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“As for /C_{pk}, they mean one or the other and you will find people confusing the definitions and you WILL find books defining them versa and vice versa. You will have to ask the definition the person is using that you are talking to.” Joe Perito

“I just finished up a meeting with a vendor and we had a nice discussion of C_{pk} vs. . We had the definitions exactly reversed between us. The outcome was to standardize on definitions and move forward from there. My suggestion to others is that each company have a procedure or document (we do not), which has the definitions of C_{pk} and in it. This provides everyone a standard to refer to for WHEN we forget or get confused.” John Adamo

## P_{pk}

_{“The Six Sigma community standardized on definitions of Cp, Cpk, Pp, and} from AIAG SPC manual page 80. You can get the manual for about $7.” Gary

“C_{pk} is calculated using an estimate of the standard deviation calculated using R-bar/d2. uses the usual form of the standard deviation ie the root of the variance or the square root of the sum of squares divided by n 1. The R-bar/D2 estimation of the standard deviation has a smoothing effect and the C_{pk}statistic is less sensitive to points which are further away from the mean than is .” Eoin

“C_{pk} is calculated using RBar/d2 or SBar/c4 for Sigma in the denominator of you equation. If not in control, your calculation of Sigma (and hence Cpk) is useless it is only valid when in-control.” Jim Parnella

“You can have a ‘good C_{pk} yet still have data outside the specification, and the process needs to be in control before evaluating C_{pk}.” Matt

“ produces an index number (like 1.33) for the process variation. C_{pk} references the variation to your specification limits. If you just want to know how much variation the process exhibits, a measurement is fine. If you want to know how that variation will affect the ability of your process to meet customer requirements (CTQs), you should use C_{pk}.” Michael Whaley