There are several statistics that can be used to measure the capability of a process: \(C_p\), \(C_{pk}\), and \(C_{pm}\). July 2014 This monthâs publication takes a look at process capability calculations and the impact non-normal data has on the results. Most capability indices estimates are valid only if the sample size used ⦠If both Cp and Cpk are greater than or equal to 1 then the process is considered capable. Cpk or Ppk is less than CP or Pp. Cp & Cpk use an estimate for the standard deviation using the R Bar / d2 method. how well a process can perform if there is no change in the underlying process conditions. Pp, Ppk are more liberal where Cp, CPK are more conservative. In actual practice, this shouldnât happen very often. the process being analyzed should be under statistical control. Large enough is generally thought to be about 50 independent data values. Cpk is more widely used than Cp, since it takes into account the mean and the standard deviation in its calculation. Consequently, you can have a capable process (Cp > 1) and not be making any good product. The Cpk calculation assumes that the data is normally distributed. Caution: Only after a process is under statistical control, can one safely assume that the mean and standard deviation to have a stable values over time. Use Pp & Ppk when you are initially setting up your process. Cpk on the other hand helps indicate how centered the data is within the range. Cp and Pp will always be greater than Cpk and Ppk respectively. When these assumptions are not met the values are not valid. the process is centered on the . A perfectly centered process will have Cp = Cpk. Cp gives the process owner an idea of potential but doesnât imply anything about whatâs actually IN THE PROCESS which is why we need to look at Cpk also and graphical representations. Where the Cp and Cpk values are equal, then the process is centered between the specifications, where not equal, then the greater the gap between the two values, the greater the shift in the process mean from the nominal mean. The addition of "k" in Cpk quantifies the amount of which a distribution is centered, in other words it accounts for shifting. If Cp = Cpk, the process is centered at the midpoint of the specifications. Cp will normally be used in conjunction with the Cpk measure, so that both centering and spread can be understood. Cpk value can be found if we know the Cp and can calculate the k value also i.e. Pp, Ppk vs Cp, CPK. Related reading: What is Capability Analysis? Cpk uses "s-short-term" to predict the behavior of the process. if Cp>Cpk, then the process is off-center. The minimum value of "k" is 0 and the maximum is 1.0. Also, the statement of why to use Cpk is because we can only get damaged on the closest side implies you canât get defects on the other side. The Cp & Cpk calculation is based on the process mean & range and has nothing to do with how many points are in or out of spec. The index Cp provides a measure of potential process capability i.e. Cpk considers the mean of the process and calculates two values ([Cp-usl = (USL -)/3] and [Cp-lsl = (- LSL)/3]). The Cpk is an indicator of how centered your process is (use Cp and Cpk together to evaluate this). Cpk 3.0 Relationship between Process . If your process is not stable, the results will be meaningless. specification range ⢠Cp=Cpk when process is centered. Cpk = Cp(1-k), where K can be any value from 0 to 1. This prediction enables us to âqualify" a new manufacturing process as being fit for use in production. Cp considers only the spread and not the centering of the process. A perfectly centered process where the mean is the same as the midpoint will have a "k" value of 0. Normal distribution. Use Cp & Cpk once the process is in a state of statistical control. 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