Use of statistical software to construct 1-mr control charts for non normal data

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.