Process Capability Analysis for Non-Normal Quality Characteristics Using Gamma Distribution

In today's ultra competitive business environment, it is becoming more critical than ever to assess precisely process losses due to non-compliance of customer specifications. To assess these losses, industry is widely using process capability indices for performance evaluation of their processes. Determination of the performance capability of a stable process using the standard process capability indices (Cp, Cpk) requires that the quality characteristics of the underlying process data should follow a normal distribution. Departures from this normality assumption could lead to erroneous results when applying conventional statistical capability measures which are based on the assumption. Many researchers have been investigating solutions to the non-normality problem. This paper explores application of an innovated method [1] based on Burr distribution for Process Capability Index (PCI) calculations when the quality characteristics data is not normal. A simulation study using Gamma distribution is conducted and simulation results are then compared with the commonly used Clements' method. Finally, a case study based on the proposed method will be presented using real data.