Most capability indices estimates are valid only if the sample size used is "large enough". There are three key assumptions for Cp or Cpk 1. Cp and Pp will always be greater than Cpk and Ppk respectively. Cpk or Ppk is less than CP or Pp. Pp, Ppk vs Cp, CPK. The Cpk is an indicator of how centered your process is (use Cp and Cpk together to evaluate this). Cpk uses "s-short-term" to predict the behavior of the process. the process is centered on the . A perfectly centered process where the mean is the same as the midpoint will have a "k" value of 0. 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. Cp & Cpk use an estimate for the standard deviation using the R Bar / d2 method. July 2014 This month’s publication takes a look at process capability calculations and the impact non-normal data has on the results. The Cpk calculation assumes that the data is normally distributed. how well a process can perform if there is no change in the underlying process conditions. Cpk is more widely used than Cp, since it takes into account the mean and the standard deviation in its calculation. Cpk = Cp(1-k), where K can be any value from 0 to 1. Cp considers only the spread and not the centering of the process. Cp gives the process owner an idea of potential but doesn’t imply anything about what’s actually IN THE PROCESS which is why we need to look at Cpk also and graphical representations. There are several statistics that can be used to measure the capability of a process: \(C_p\), \(C_{pk}\), and \(C_{pm}\). In actual practice, this shouldn’t happen very often. When these assumptions are not met the values are not valid. Consequently, you can have a capable process (Cp > 1) and not be making any good product. Use Cp & Cpk once the process is in a state of statistical control. Large sample size 3. Pp, Ppk are more liberal where Cp, CPK are more conservative. Stable process 2. Cpk 3.0 Relationship between Process . Cpk on the other hand helps indicate how centered the data is within the range. This prediction enables us to “qualify" a new manufacturing process as being fit for use in production. 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 can’t get defects on the other side. Most capability indices estimates are valid only if the sample size used … The addition of "k" in Cpk quantifies the amount of which a distribution is centered, in other words it accounts for shifting. 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]). Large enough is generally thought to be about 50 independent data values. A perfectly centered process will have Cp = Cpk. 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. Cpk value can be found if we know the Cp and can calculate the k value also i.e. 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. I mean is centered and there is no shift in the mean, then Cp and Cpk value would be the same. Cp will normally be used in conjunction with the Cpk measure, so that both centering and spread can be understood. Related reading: What is Capability Analysis? specification range • Cp=Cpk when process is centered. If your process is not stable, the results will be meaningless. the process being analyzed should be under statistical control. The most common method of expressing process capability involves calculating a Cpk value, i.e., a process has a Cpk = 1.54. 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