{"title":"One-sided Downward Control Chart for Monitoring the Multivariate Coefficient of Variation with VSSI Strategy","authors":"Xinying Chew, K. W. Khaw","doi":"10.5614/j.math.fund.sci.2020.52.1.8","DOIUrl":null,"url":null,"abstract":"In recent years, control charts monitoring the coefficient of variation (CV), denoted as the ratio of the variance to the mean, is attracting significant attention due to its ability to monitor processes in which the process mean and process variance are not independent of each other. However, very few studies have been done on charts to monitor downward process shifts, which is important since downward process shifts show process improvement. In view of the importance of today’s competitive manufacturing environment, this paper proposes a one-sided chart to monitor the downward multivariate CV (MCV) with variable sample size and sampling interval (VSSI), i.e. the VSSI D MCV chart. This paper monitors the MCV as most industrial processes simultaneously monitor at least two or more quality characteristics, while the VSSI feature is incorporated, as it is shown that this feature brings about a significant improvement of the chart. A Markov chain approach was adopted for designing a performance measure of the proposed chart. The numerical comparison revealed that the proposed chart outperformed existing MCV charts. The implementation of the VSSI D MCV chart is illustrated with an example.","PeriodicalId":16255,"journal":{"name":"Journal of Mathematical and Fundamental Sciences","volume":"1 1","pages":"112-130"},"PeriodicalIF":0.5000,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mathematical and Fundamental Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5614/j.math.fund.sci.2020.52.1.8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 8
Abstract
In recent years, control charts monitoring the coefficient of variation (CV), denoted as the ratio of the variance to the mean, is attracting significant attention due to its ability to monitor processes in which the process mean and process variance are not independent of each other. However, very few studies have been done on charts to monitor downward process shifts, which is important since downward process shifts show process improvement. In view of the importance of today’s competitive manufacturing environment, this paper proposes a one-sided chart to monitor the downward multivariate CV (MCV) with variable sample size and sampling interval (VSSI), i.e. the VSSI D MCV chart. This paper monitors the MCV as most industrial processes simultaneously monitor at least two or more quality characteristics, while the VSSI feature is incorporated, as it is shown that this feature brings about a significant improvement of the chart. A Markov chain approach was adopted for designing a performance measure of the proposed chart. The numerical comparison revealed that the proposed chart outperformed existing MCV charts. The implementation of the VSSI D MCV chart is illustrated with an example.
近年来,监测变异系数(CV)(表示为方差与均值之比)的控制图由于能够监测过程均值和过程方差并非相互独立的过程而引起了人们的极大关注。然而,很少有研究用图表来监测向下的过程转移,这是很重要的,因为向下的过程转移显示了过程的改进。鉴于当今竞争激烈的制造业环境的重要性,本文提出了一种单侧图来监测具有可变样本量和抽样间隔(VSSI)的向下多元CV (MCV),即VSSI D MCV图。本文监测MCV,因为大多数工业过程同时监测至少两个或两个以上的质量特征,同时纳入了VSSI特征,因为它表明该特征带来了图表的显着改进。采用马尔可夫链方法设计了该图表的性能度量。数值比较表明,所提出的图表优于现有的MCV图表。通过实例说明了vssdmcv图的实现。
期刊介绍:
Journal of Mathematical and Fundamental Sciences welcomes full research articles in the area of Mathematics and Natural Sciences from the following subject areas: Astronomy, Chemistry, Earth Sciences (Geodesy, Geology, Geophysics, Oceanography, Meteorology), Life Sciences (Agriculture, Biochemistry, Biology, Health Sciences, Medical Sciences, Pharmacy), Mathematics, Physics, and Statistics. New submissions of mathematics articles starting in January 2020 are required to focus on applied mathematics with real relevance to the field of natural sciences. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.