Performance of Joint Quality Monitoring Schemes under Gaussian distribution
A M Razmy1, Faisal Mohamed Ababneh2, Ahmed Al-Hadhrami3, Mohammad Zakir Hossain4, Sadoon Abdullah Ibrahim Al-Obaidy5
1Dr. AM Razmy*, Department of Statistics, Sultan Qaboos University, Sultanate of Oman. & Department Mathematical Sciences, South Eastern University of Sri Lanka, Sri Lanka.
2Dr. Faisal Mohamed Ababneh, Department of Statistics, Sultan Qaboos University, Sultanate of Oman & Department of Mathematics, Al-Hussein Bin Talal University, Jordan.
3Dr. Ahmed Al-Hadhrami1, Department of Statistics, Sultan Qaboos University, Sultanate of Oman.
4Dr. Mohammad Zakir Hossain, Department of Operations Management and Business Statistics, Sultan Qaboos University, Sultanate of Oman.
5Dr. Sadoon Abdullah Ibrahim Al-Obaidy, Department of Mathematics, Al-Hussein Bin Talal University, Jordan.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 335-340 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3201079220/2020©BEIESP | DOI: 10.35940/ijrte.B3201.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Average run length, Control chart, Cumulative sum, Exponentially weighted moving average, Joint monitoring scheme, Shewhart scheme.