ABSTRACT
In today's competitive manufacturing environment, the challenge is to responsively
produce products with minimum cost and high quality. Achieving and controlling the
targeted quality level in manufacturing processes does not only increase customer
satisfaction, but it can also result in significant cost and time savings. Further,
measuring the process performance is a critical issue in process improvement
initiatives. The common practice in several industries is using the Univariate Process
Capability Indices (UPCIs) to measure the process performance, which are based on
only a single quality characteristic. In most of the applications, it is not acceptable to
judge the performance based on a single quality characteristic as it actually relies on
more than one characteristic. In this paper, univariate and multivariate PCIs are used
to measure the performance of the flare making process. This process is a critical
step in the straight fluorescent light bulb production line. In addition, multivariate
control charts such as the Hotelling
 as well as the Multivariate Exponentially
Weighted Moving Average (MEWMA) are constructed for the collected data to verify
that the process is in control before assessing its capability. Besides, Principal
Component Analysis (PCA) and Joint Normal Distribution (JND) techniques are
applied in the multivariate process capability assessment. In this paper, Multivariate
Process Capability Indices (MPCIs) have been evaluated to compare the process
performance before and after improvement efforts. In the considered case study,
MPCIs provide the user with an overall assessment of process capability regardless
of the fluctuations in the individual variables capabilities.