Abstract:
Statistical Process Control (SPC) is an important
quality control technique, which aims to reduce variability
and monitor the performance of a production process in
order to improve and assure the quality of the' product.
Control charting can be successfully applied for
implementation of SPC in any industry. The theory on most
of established control charts is based on the normal
assumption and their performances are very much sensitive
the departures from the normal assumption. Minitab 15
statistical software performs individual distribution
identification which allows us to fit the data with 14
parametric distributions and 2 transformations. Through
this distribution identification technique, in this paper, we
have provided a solution to construct control charts for non
normal data. As an effective application of control charts,
mainly, this study focuses on constructing control charts for
quality assurance in crepe rubber industry. Since the output
of crepe rubber manufacturing process usually generates
individual observations due to the inborn features of the
production process, Individual and Moving Range chart (1-
MR chart) has been used in this study.
