• 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.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.139-147
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• 2009

Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.

• 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.15 no.2
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• pp.285-295
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• 2004

Statistical process control(SPC) and engineering process control(EPC) are two complementary strategies for quality improvement. An integrated process control(IPC) can use EPC to reduce the effect of predictable quality variations and SPC to monitor the process for detection of special causes. In this paper we assume an IMA(1,1) model as a disturbance process and an occurrence of a level shift in the process, and we consider the economic performance for applying an EWMA chart to monitor MMSE-controlled processes. The numerical results suggest that the IPC scheme in an IMA(1,1) disturbance model does not give additional advantages in the economic aspect.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.773-782
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• 2007

The statistical process control charts are very extensively used for monitoring of process mean, deviation, defect rate or reject rate. In this paper we consider a control chart to monitor the process reject rate in the high yield process, which is based on the observed cumulative probability of the number of items inspected until r defective items are observed. We first propose selection of the optimal value of r in the CPC-r charts, and also consider the usefulness of the chart in high yield process such as semiconductor or TFT-LCD manufacturing process.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.497-515
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• 2016

Modern processes often monitor more than one quality characteristic that are referred to as multivariate statistical process control (MSPC) procedures. The MSPC is the most rapidly developing sector of statistical process control and increases interest in the simultaneous inspection of several related quality characteristics. Most multivariate detection procedures based on a multi-normality assumptions are independent, but there are many processes that assume non-normality and correlation. Many multivariate control charts have a lack of related joint distribution. Copulas are tool to construct multivariate modelling and formalizing the dependence structure between random variables and applied in several fields. From copula literature review, there are a few copula to apply in MSPC that have multivariate control charts, and represent a successful tool to identify an out-of-control process. This paper presents various types of copulas modelling for the multivariate control chart. The performance measures of the control chart are the average run length (ARL) and the average number of observations to signal (ANOS). Furthermore, a Monte Carlo simulation is shown when the observations were from an exponential distribution.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.29-35
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• 2011

In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart is often used as an alternative charting scheme in practice. And CUSUM-chart is used when it is desirable to detect out of control situations very quickly because of sensitive to a small or gradual drift in the process. In this paper, I compare CUSUM-chart to X-chart for the Katz family covering equi-, under-, and over-dispersed distributions relative to the Poisson distribution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.3
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• pp.39-49
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• 2003

Statistical process cintrol is intended to assist operators of a stable system in monitoring whether a change has occurred in the process, and it uses several control charts as main tools. In design and use of control chart, it is rational that probability of false alarm is minimized in stable process and probability of detecting shifts is maximized in out-of-control. In this study, we establish bootstrap control limits for robust M-estimator 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 M-estimator control chart is developed for applying bootstrap method to M-estimator chart, which is more robust to keep ARL when process contain contaminate quality characteristic.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.70-88
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• 2000

Short runs where it is neither possible nor practical to obtain sufficient subgroups to estimate accurately the control limit are common in modem business environments. In this study, the standardized control chart, Hillier's exact method, Q chart, EWMA(Exponentially Weighted Moving Average) chart for Q statistics and EWMA chart for mean and absolute deviation among many SPC(Statistical Process Control) techniques for short runs have been reviewed and advantages and disadvantages of these techniques are discussed. The simulation experiments to compare performances of these variable charts for process mean and variations are conducted for combination of subgroup size, scale and timing of shifts of process mean an/or standard deviation. Based upon simulation results, some guidelines for practitioners to choose short run SPC techniques are recommended.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.56-62
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• 2013

Control chart is representative tool of Statistical Process Control (SPC). But, it is not given information about the economic loss that occurs when a product is produced characteristic value does not match the target value of the process. In order to manage the process, we should consider not only stability of the variation also produce products with a high degree of matching the target value that is most ideal quality characteristics. There is a need for process control in consideration of economic loss. In this paper, we design a new control chart using the quadratic loss function of Taguchi. And we demonstrate effectiveness of new control chart by compare its ARL with ${\overline{x}}-R$ control chart.