Open AccessPerformance of Joint Quality Monitoring Schemes under Gaussian distribution
Author(s) -
Athambawa Mohamed Razmy,
Mohamed Ababneh Faisa,
Al-Hadhrami Ahmed,
Mohammad Zakir Hossain,
Abdullah Ibrahim Al-Obaidy Sadoon
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3201.079220
Subject(s) - variance (accounting) , joint (building) , statistics , bivariate analysis , joint probability distribution , gaussian , mathematics , cusum , moving average , ewma chart , control chart , computer science , process (computing) , engineering , architectural engineering , physics , accounting , quantum mechanics , business , operating system
Jointly monitoring the process mean and variance has become a well-known topic in statistical quality control literature after it is considered as a bivariate problem. Many joint monitoring schemes have been proposed by using the Shewhart, cumulative sum and exponentially weighted moving average techniques. In this paper, best performing schemes from each technique has been selected and compared for their performance using average run length properties. It was found that selection of better joint monitoring scheme based on the shift in mean and variance to be detected quickly. In particular, the Shewhart distance joint monitoring scheme performs well when there is larger shifts in mean, variance or in both. In addition, the Shewhart distance joint monitoring scheme performs specific when there is no shift in mean and decrease in variance. For the smaller shifts in mean, variance or in both, cumulative sum and exponentially weighted moving average joint monitoring schemes can be recommended. At this scenario exponentially weighted moving average joint monitoring scheme performs marginally better than the cumulative sum scheme.