The performance evaluation of the bivariate EWMAcontrol chart using CARL distribution and EPC. (May 2020)

• Record Type: Journal Article
• Authors: Acita, Selly; Mashuri, Muhammad; Prastyo, Dedy Dwi
• Is Part Of: Journal of physics. Volume 1538(2020)
• Publication Date: 2020-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1538/1/012053
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In general, control charts are developed with the assumption that the critical quality of a production process is normally distributed with known parameters. However, in practice, critical quality is not always normally distributedand process parameters are typically unknown. In such a case, it is necessary to estimate the parameters. One of the measuresto evaluate the performance control charts is Average Run Length (ARL). When the process parameters are estimated, the run-length follows its conditional distribution so-called Conditional Average Run Length (CARL). In this study, the performance of the Bivariate Exponentially Weighted Moving Average(BEWMA)control chart will be evaluated by considering the practitioner to practitioner variability using the CARL distribution and the Exceedance Probability Criterion (EPC). The value of CARL is calculated using the Markov Chain method. The EPC is used to evaluate practitioner to practitioner variability that is closely related to parameter estimation.The results show that to guarantee the in-control performance Phase II chart based on EPC, the large size of observations in Phase I data is needed. However, in practice, it is difficult to collect such a huge size ofdata in Phase I. Therefore, to produce the best in-control performance with available Phase I, the control limits are adjusted.