Process Capability Assessment for Univariate and Multivariate Non-normal Correlated Quality Characteristics

In today’s competitive business and industrial environment, it is becoming more crucial than ever to assess precisely process losses due to noncompliance to customer specifications. To assess these losses, industry is extensively 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 such as and requires that the underlying quality characteristics data follow a normal distribution. However it is an undisputed fact that real processes very often produce non-normal quality characteristics data and also these quality characteristics are very often correlated with each other. For such non-normal and correlated multivariate quality characteristics, application of standard capability measures using conventional methods can lead to erroneous results. p C pk C The research undertaken in this PhD thesis presents several capability assessment methods to estimate more precisely and accurately process performances based on univariate as well as multivariate quality characteristics. The proposed capability assessment methods also take into account the correlation, variance and covariance as well as nonnormality issues of the quality characteristics data. It is an established fact that the fundamental objective of all capability measures is to help process engineers and managers decide whether to