• Proceedings of the Korean Society for Quality Management Conference
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• 2007.04a
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• pp.287-296
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• 2007

Pearn et al.(2002) supposed the $C_{pp}$ multiple process performance analysis chart. This chart display 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. With bootstrapping method, we can display the distribution of the process variation and process departure on the $C_{pp}$ multiple process performance analysis chart.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.195-198
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• 2006

In this paper, we propose a 2dimension Control Chart. Suggested which Control chart augments Schwart Control chart which is 1-dimensional and Independent variable and dependent variable interrelationship by using control chart. Schwart control chart cannot use input variable and output variable together. In this paper, we try to analysis input variable and output variable dependent and effect. So called 2-dimensional control char.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.109-117
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• 2017

In this article, a control chart based on multiple dependent (or deferred) state sampling for the gamma distributed quality characteristic is proposed using the gamma to normal transformation. The proposed control chart has two pairs of control limits, which can be determined by considering the in-control average run length (ARL). The shift in the scale parameter of a gamma distribution is considered and the out-of-control ARL is evaluated. The performance of the proposed chart has been shown for different levels of the parameters of the proposed control chart. It is also shown that the proposed chart is better than the Shewhart chart in terms of ARLs. A case study with a real data has been included for the practical usage of the proposed scheme.

• Journal of the Ergonomics Society of Korea
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• v.15 no.2
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• pp.1-13
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• 1996

The purpose of this study is to suggest a brassiere sizing chart for elderly women. 2 control dimensions(under bust girth and cup size) were chosen as 2 axes of brassiere size chart. A loss function was used to determined intervals of under bust girth and cup size of size chart, because the loss function introduces the concept of frequency to size chart for better customer's satisfaction. From the dual distribution table whose intervals had been determined by a loss function. The 15 sizs, which had more than 2% of appearance were suggested for brassiere size chart. The suggested brassiere sizes covered 87.6% of all subjects. Considering that KS brassiere size chart consisting of 32 sizes covers 88.5%, the suggested brassiere size chart would be considered quite feasible. Also it is suggested supply reference measurement chart relevant to brassiere manufacturing for 10 most frequent sizes.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.327-336
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• 2012

Purpose: In this study, we research the hurdle rate method to suggest the robust control chart for a contaminated process less vulnerable to fault values than existing control charts. Methods: We produce the results of p, ARL values to compare the performance of two control charts, $\bar{x}-s$ that has been used typically and TM-TS that is suggested by this paper. We implement the simulation focusing on three cases, change of deviation, mean and both of them. Results: We draw a conclusion that the TM-TS control chart has better efficiency than $\bar{x}-s$ control chart over the three cases. Conclusion: We insist that applying TM-TS control chart for a polluted process is more effective than $\bar{x}-s$ control chart.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.50-56
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• 2002

In this study, we establish bootstrap control limits for EWMA chart by applying the bootstrap method, called resampling, which could not demand assumptions about pre-distribution when the process is skewed and/or the normality assumption is doubt. The results obtained in this study are summarized as follows : bootstrap EWMA control chart is developed for applying bootstrap method to EWMA chart, which is more sensitive to small shifts of process. With the purpose of eliminating a skewness of the resampling distribution, the bootstrap control limits are established by using a modified residual, and its performance is analyzed by ARL. It is shown that the bootstrap EWMA control chart developed in this study includes the properties of standard EWMA control chart that is sensitive to a small shift, and detects process in out of control more quickly than standard EWMA chart.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.97-103
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• 2006

Recently, the control chart is developed for monitoring processes with normal short production runs by the coefficient of variation(CV) characteristic for a normal distribution. This control chart does not work well in non-normal short production runs. And most of industrial processes are known to follow the non-normal distribution. Therefore, the control chart is required to be developed for monitoring the processes with non-normal short production runs by the CV characteristics for a non-normal distribution. In this paper, we suggest the control chart for monitoring the processes with a gamma short runs by the CV characteristics for a gamma distribution. This control chart is denoted by the gamma CV control chart. Futhermore evaluated the performance of the gamma CV control chart by average run length(ARL).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.1-8
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• 2011

This paper proposes a variable sampling interval multivariate $T^2$ control chart with sampling at fixed times, where samples are taken at specified equally spaced fixed time points and additional samples are allowed between these fixed times when indicated by the preceding $T^2$ statistics. At fixed sampling points, the $T^2$ statistics are composed of all quality characteristics and a part of quality characteristics are selected to obtain $T^2$ statistics at additional sampling points. A Markov chain approach is used to evaluate the performance of the proposed chart. Numerical studies for the performance of the proposed chart show that the proposed chart reduces the observations obtained from a process and detects the assignable cause of a process with low correlated quality characteristics quickly.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.487-498
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• 2022

Social network analysis (SNA) techniques have recently been developed to monitor and detect abnormal behaviors in social networks. As a useful tool for process monitoring, control charts are also useful for network monitoring. In this paper, the degree and closeness centrality measures, in which each has global and local perspectives, respectively, are applied to an exponentially weighted moving average (EWMA) chart and a multinomial cumulative sum (CUSUM) chart for monitoring undirected weighted networks. In general, EWMA charts monitor only one variable in a single chart, whereas multinomial CUSUM charts can monitor a categorical variable, in which several variables are transformed through classification rules, in a single chart. To monitor both degree centrality and closeness centrality simultaneously, we categorize them based on the average of each measure and then apply to the multinomial CUSUM chart. In this case, the global and local attributes of the network can be monitored simultaneously with a single chart. We also evaluate the performance of the proposed procedure through a simulation study.

• Journal of Integrative Natural Science
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• v.15 no.1
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• pp.1-8
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• 2022

In a multivariate normal production process N_p(µ,Σ), a chart combining three EWMA charts with accumulate-combine method for µ, variance components of Σ, and off-diagonal elements of Σ, into a EWMA (exponentially weighted moving average) chart is considered, which is called a combined EWMA chart. Through simulation work, the proposed combined EWMA chart's numerical performance and properties are examined. The simulation results show that the proposed combined EWMA chart, which is simultaneously monitoring all the process parameters of multivariate normal production process, works effectively in the perspective of means, variances and correlation coefficients. In addition, the combined EWMA chart is extended to VSI chart.