• Industrial Engineering and Management Systems
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• 제12권2호
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• pp.112-117
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• 2013

The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

• 품질경영학회지
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• 제34권4호
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• pp.125-132
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• 2006

An adjusted control limit of the $\overline{X}$ chart is proposed for monitoring the continually improving processes. The continual improvement of the process implies the decrease of the process variance, which is represented by a logistic curve. The process standard deviation is estimated by the exponentially weighted moving average of the sample standard deviations from the past to the current times. The control limits are adjusted by the estimated standard deviation at every sampling time. The performance of the adjusted control limit is compared with that of the standard control limits for various cases of the decreasing speed and size of the variance. The results show that the $\overline{X}$ chart with the adjusted control limits provides better performances for monitoring the small and moderate shifts in continually improving processes.

• Communications for Statistical Applications and Methods
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• 제5권2호
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• pp.373-381
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• 1998

In this paper a new control chart which accounts for correlation between process subgroups will be proposed. We consider the case where the process fluctuations are autocorrelated by a stationary AR(1) time series and where n($\geq1$) items are sampled from the process at each sampling time. A simulation study is presented and shows that for correlated subgroups, the proposed control chart makes a significant improvement over the traditionally employed X-bar chart which ignores subgroup correlations. Finally, we illustrate the proposed chart by comparing the standardized residuals and X-bar chart on a data set of motor shaft diameters.

• 응용통계연구
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• 제35권2호
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• pp.203-215
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• 2022

본 논문에서는 연속적으로 관측되는 개별 관측치에 대하여, 모평균의 변화를 모니터링하는 데 적용 가능한 관리도에 대하여 고찰해 보고자 한다. 가장 대표적인 관리도로 슈하르트의 X 관리도, 지수가중이동평균 관리도와 이들의 결합관리도에 관하여 살펴보고 모의실험을 통하여 각 관리도의 성능을 비교 평가해 보고자 한다. 또한, 실제 자료분석을 통해 실질적인 문제에서 관리도를 어떻게 사용해야 하는지 알아보고, 각 관리도의 문제점에 대하여 살펴보기로 한다.

• 품질경영학회지
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• 제29권3호
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• pp.79-85
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• 2001

Estimation of the process deviation is an important problem in statistical process control, especially in the control chart, process capability analysis or measurement system analysis. In this paper we suggest the use of the Gini's mean difference for the estimation of the process deviation when we design the control limits in construction of the control charts. The efficiency of the Gini's mean difference was well explained in Nam, Lee and Jung(2000). In this paper we propose an $\overline{X}$ control chart which use the control limits based on the Gini's mean difference. In various classes of distributions, the proposed control chart shows food performance.

• 산업경영시스템학회지
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• 제27권2호
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• pp.114-122
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• 2004

In statistical process control applications, variable sample size (VSS) $\bar{X}$ chart is often used to detect the assignable cause quickly. However, it is usually assumed that only one assignable cause results in the out-of-control in the process. In this paper, we propose the algorithm to minimize the function of cost per unit time and compare the economic design and the statistical design by use of the value of cost per unit time. We consider double assignable causes to occur with compound in the process and adopt the Markov chain approach to investigate the statistical properties of VSS $\bar{X}$ chart. A procedure that can calculate the control chart's parameters is proposed by the economic design.

• 품질경영학회지
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• 제24권1호
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• pp.73-86
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• 1996

Adaptive EWMA(Exponentially Weighted Moving Average)-x control chart using the Kalman gain recursive average is designed. The designed control chart is effective to on-line process monitoring as continuous flow processes. Performance evaluation between the designed control chart and traditional one is implemented. For this, ARL(Average Run Length) is adopted as a criterion. Results show that the designed adaptive EWMA-x control chart has shorter ARL than EWMA-x control chart when process mean is shifted. This model can be extended to process prevention control. The methodology proposed in this research is turned out to show the high performance than that of the given methodologies.

• 품질경영학회지
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• 제28권3호
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• pp.44-52
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• 2000

In this paper we apply VSS X chart to the case when two assignable causes effect the process mean shift. We compare the case when two compound assignable causes effect the process mean with another one which effects individually. For the practical use of VSS $\bar{X}$ chart we propose the sample size which minimizes the average number of samples until the signal is given in out-of-control state.

• 품질경영학회지
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• 제35권2호
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• pp.37-44
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• 2007

Today, the statistical process control (SPC) in manufacture environment is an important role at the process by the productivity improvement of the manufacturing systems. The control chart in this statistical method is widely used as an important statistical tool to find the assignable cause that provoke the change of the process parameters such as the mean of interest or standard deviation. But the traditional SPC don't grasp the change of process according to the points fallen the near control limits because of monitoring the variance of process such as the fixed sampling interval and the sample size and handle the cost of the aspect of these sample point. The control chart can be divided into the statistical and economic design. Generally, the economic design considers the cost that maintains the quality level of process. But it is necessary to consider the cost of the process improvement by the learning effects. This study does the economic design in the VSI $\bar X$ control chart and added the concept of loss function of Taguchi in the cost model. Also, we preyed that the VSI $\bar X$ control chart is better than the FSI $\bar X$ in terms of the economic aspects and proposed the standard of the process improvement using the VSI $\bar X$ control chart.

• Journal of the Korean Data and Information Science Society
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• 제21권4호
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• pp.681-688
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• 2010

통계적 공정관리에서 결점수를 모니터링 하는데는 c-관리도가 사용된다. 전통적인 c-관리도는 표본에서 결점의 발생은 포아송분포를 따른다는 가정 하에서 만들어진다. 포아송 분포에 대한 가정이 맞지 않을 때에는 X-관리도가 사용될 수 있다. 지수가중이동평균관리도는 공정의 작은 변화를 찾는 데 유용한 것으로 알려져 있다. 본 논문에서는 다양한 카즈분포족으로부터 생성된 계수자료에 대하여 3시그마 X-관리도와 지수가중이동평균관리도의 효율을 평균 런의 길이에 근거하여 비교한다. 즉, 자료가 어떤 분포로부터 생성되었는지 알 수 없을 때, X-관리도와 지수가중이동평균관리도를 비교하는 것이다.