Abstract
Process Capability Indices (PCI) are widely used in manufacturing industry today. These statistical measures (Cp, Cpk) provide a quantitative measure of the process performance for decision makers. They are based on normality assumptions and provide a better estimation of process parameters if the process data is normally distributed. Unfortunately, this assumption is often violated in practice. In most cases, the distribution of a process characteristic data is non-normal. Application of conventional methods for calculation of process capability indices based on normal assumption will therefore give erroneous results that could lead to wrong decisions.
In non-normal data situations, estimation of accurate PCI is critical for process improvement purposes. This paper explores application of a novel method [1] based on Burr distribution for PCI calculations when the process data is not normally distributed and compares simulation results with the commonly used Clements' method Finally, an example illustrating application of this method with real world data is presented.