• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.465-470
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• 2012

EWMA(exponentially weighted moving average) charts and CUSUM(cumulative sum) charts are very effective to detect small shifts in the process mean. These charts have some control-chart parameters that allow the charts and be tuned and be more sensitive to certain shifts. The EWMA chart requires users to specify the value of a smoothing parameter, which can also be designed for the size of the mean shift. However, the size of the mean shift that occurs in applications is usually unknown and EWMA charts can perform poorly when the actual size of the mean shift is significantly different from the assumed size. In this paper, we propose the design procedure to find the optimal smoothing parameter of the EWMA chart when the size of the mean shift is unknown.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.973-982
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• 2006

Performances of variable sampling interval(VSI) multivariate control charts with accumulate-combine approach for monitoring mean vector of p related quality variables were investigated. Shewhart control chart is also proposed to compare the performances of CUSUM and EWMA charts. Numerical comparisons show that performances of CUSUM and EWMA charts are more efficient than Shewhart chart for small or moderate shifts, and VSI chart is more efficient than fixed sampling interval(FSI) chart. We also found that performances of the CUSUM or EWMA chart with accumulate-combine approach are substantially efficient than those of Shewhart chart.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.347-354
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• 1999

Statistical Process Control(SPC) which uses control charts is widely used to inspect and improve manufacturing process as a effective method. A parametric method is the most common in statistical process control. Shewhart chart was made under the assumption that measurements are independent and normal distribution. In practice, this assumption is often excluded, for example, in case of (equation omitted) chart, when the subgroup sample is small or correlation, it happens that measured data have bias or rejection of the normality test. A bootstrap method can be used in such a situation, which is calculated by resampling procedure without pre-distribution assumption. In this study, applying bootstrap percentile method to (equation omitted) chart, it is compared and evaluated standard process control limit with bootstrap percentile control limit. Also, under the normal and non-normal distributions, where parameter is 0.5, using computer simulation, it is compared standard parametric with bootstrap method which is used to decide process control limits in process quality.

• Journal of Korean Society for Quality Management
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• v.35 no.2
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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 Korean Society for Quality Management
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• v.38 no.2
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• pp.171-179
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• 2010

Pearn et al.(2002) supposed the $C_{pp}$ multiple process performance analysis chart. This chart displays multiple processes with the process variation and process departure on one single chart. But, this chart can not display the distribution of the process variation and process departure and is inappropriate for processes with non-normal distributions. With bootstrapping method, we can display the distribution of the process variation and process departure on the $C_{pp}$ multiple process performance analysis chart.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.385-389
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• 2012

With the development of computer storage and the rapidly growing ability to process large amounts of data, the multivariate control charts have received an increasing attention. The existing univariate and multivariate control charts are a single hypothesis testing approach to process mean or variance by using a single statistic plot. This paper proposes a multiple hypothesis approach to developing a new multivariate control scheme. Plotted Hotelling's $T^2$ statistics are used for computing the corresponding p-values and the procedure for controlling the false discovery rate in multiple hypothesis testing is applied to the proposed control scheme. Some numerical simulations were carried out to compare the performance of the proposed control scheme with the ordinary multivariate Shewhart chart in terms of the average run length. The results show that the proposed control scheme outperforms the existing multivariate Shewhart chart for all mean shifts.

• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.62-73
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• 1997

Previous VSI control chart works have been done on quality variable whose distribution is normal. But there are many situations in which hte assumption of not a, pp.opriate. Also, in many industrial processes, the interest is to monitor the number of defectives. In this paper, we will take the existing properties of VSI control chart developed for the normal distribution and a, pp.y them to the np-chart based on the discrete binomial distribution. We will consider the CUSUM chart for the number of defectives. Here, the interesting object is to compute the VSI ATS for CUSUM control chart using Markov chain a, pp.oach and to compare FSI ATS and VSI ATS.

• Journal of Korean Society for Quality Management
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• v.29 no.1
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• pp.1-10
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• 2001

We consider the problem of detecting special variations in multivariate $T^2$-control chart when two or more multivariate outliers are present. Since a multivariate outlier may reflect slippage in mean, variance, or correlation, it can distort the sample mean vector and sample covariance matrix. Damaged sample mean vector and sample covariance matrix have difficulty in examining special variations clearly, An alternative to detection outliers or special variations is to use robust estimators of mean vector and covariance matrix that are less sensitive to extreme observations than are the standard estimators $\bar{x}$ and $\textbf{S}$. We applied popular minimum volume ellipsoid(MVE) and minimum covariance determinant(MCD) method to estimate mean vector and covariance matrix and compared its results with standard $T^2$-control chart using simulated multivariate data with outliers. We found that the modified $T^2$-control chart based on the above robust methods were more effective in detecting special variations clearly than the standard $T^2$-control chart.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.97-103
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• 2003

Shewhart-type control charts have historically been used for attribute data, though they have ARL biased property and even are unable to detect the improvement of a process with some process parameters. So far most efforts have been made to improve the performance of attribute control charts in terms of faster detection of special causes without increasing the rates of false alarm. In this paper, control limits are proposed that yield an ARL (nearly) unbiased chart for attributes. Optimal design is also proposed for attribute control charts under a natural sense of criterion.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.217-224
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• 1998

A cost model, controlling multiple dependent subprocesses with minimum cost, is derived by renewal theory approach. The optimal multiple cause-selecting control chart and individual Y control chart are thus constructed to monitor the specific product quality and overall product quality contributed by the multiple dependent subprocesses. They may be used to maintain the process with minimum cost and effectively distinguish which component of the subprocesses is out of control. The optimal design parameters of the proposed control charts can be determined by minimizing the cost model using simple grid search method, An example is given to illustrate the application of the optimal multiple cause-selecting control chart and individual Y control chart.