The measured data is representative of the process. This means that it should be a randomly selected and large sample, taken over a long period. Samples taken over a short period can suffer from a limited range of changes in either external seasonal effects or internal process variables, such as humidity or tool wear.
When interpreting values of Cpk, there are three significant regions which may be considered, and a general rule is given in the table below . The value of 3 as a 'total confidence' limit may be lowered if measurements are taken as the average of sample batches. This commonly happens when Cpk is measured using the same data that is used to plot the Control Chart (e.g. the confidence limit reduces to 2 for the common sample size of 4).
In the broader sense, studying Process Capability is more than just measuring Cp and Cpk; it involves understanding the statistical performance and operational working of the process. Most importantly, it means understanding what causes variation within the process, under what conditions, and how these variables interact. The purpose of doing this is to enable confident process improvement that steadily reduces variation.
The measured data is representative of the process. This means that it should be a randomly selected and large sample, taken over a long period. Samples taken over a short period can suffer from a limited range of changes in either external seasonal effects or internal process variables, such as humidity or tool wear.When interpreting values of Cpk, there are three significant regions which may be considered, and a general rule is given in the table below . The value of 3 as a 'total confidence' limit may be lowered if measurements are taken as the average of sample batches. This commonly happens when Cpk is measured using the same data that is used to plot the Control Chart (e.g. the confidence limit reduces to 2 for the common sample size of 4).In the broader sense, studying Process Capability is more than just measuring Cp and Cpk; it involves understanding the statistical performance and operational working of the process. Most importantly, it means understanding what causes variation within the process, under what conditions, and how these variables interact. The purpose of doing this is to enable confident process improvement that steadily reduces variation.
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