X-bar Chart Based on Six Sigma to Continual Quality Improvement: Implementation on Oil Vapor Pressure Characteristic, at Yemen's Aden Oil Refinery

Abstract

Producing higher-quality products while keeping them affordable has always been a challenge in today's competitive market. As a result, producers are actively searching for ways to cost-cutting and increase efficiency. A control chart is the most widely used SPC tool in practice for maintaining process control at a low cost. The X_-bar chart is the most widely used control chart due to its simplicity. Despite the fact that the sample mean is an unbiased estimator of the population mean, but the main drawback of the X_-bar chart is that the population standard deviation is unknown. With the knowledge available to researchers there are many methods of estimating the unknown standard deviation that can lead to different conclusions. In this study, a groundbreaking approach to estimate the population standard deviation from the perspective of Six Sigma quality that is implemented for the design of the suggested control-chart for mean is X-bar Chart based on the Six Sigma, from the standpoint of Six Sigma quality and the process specification. This paper presents Six-Sigma SS evaluation focusing on the process capability with SS-X_-bar control chart was used to minimize variations in the oil Vapor Pressure characteristic in an oil refining process in Aden, Yemen, a twenty-five oil Vapor Pressure characteristic samples with a normal distribution were collected at random, each sample containing four items. After having the main statistical tests like the Normality Test, unit root, Autoregressive Test and Capability, it has been found that the sigma level used in the Aden refinery is less than 4. According to the findings of this analysis, the X chart based on the six-sigma estimation method is effective in reducing variance in the oil Vapor Pressure characteristic. Furthermore, it is able to keep the process mean near to the target, leading towards improving the process.  As the process capacity improves, the sigma levels increase. As a result of the increase in sigma levels, the process refinery of oil Vapor Pressure characteristic performance improves. Finally, this paper presents the fundamentals and skills needed for quality control researchers and engineers to use Six Sigma to minimize variances in the industrial process.