TABLE OF CONTENTS We shall very briefly sketch the basic assumptions and properties of process capability indices. Process capability indices have been widely investigated as a means of summarizing process performance relative to a set of specification limits. These indices are effective tools for both process capability analysis and quality assurance. The proper use of process capability indices, which are statistical measures of process capability, is based on several assumptions. One of the most essential is that the process monitored is supposed to be stable and the output is approximately normally distributed. When the distribution of a process characteristic is non-normal, PCIs calculated using conventional methods could often lead to erroneous and misleading interpretation of the process's capability.
A number of quality control experts have provided useful and insightful information regarding the errors in interpretation of the values of the PCIs that occur due to the misapplication of the indices to non-normal data (see, e.g., Sarkar and Pal (1997), Chou et al. (1998) among others). English and Taylor (1993) have examined the effect of the non-normality assumption on PCIs and have concluded that C pk is more sensitive to departures from normality than C p. Somerville and Montgomery (1996) studied the errors that can occur in calculating C p or C p for a non-normal distribution and making inferences about the PPM non-conforming under normality assumption. They have reached a conclusion for the four non-normal distributions, most common in engineering and reliability applications: the t,...
