• 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 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.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.681-688
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• 2010

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 EWMA control 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.

• Journal of the Korea Safety Management & Science
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• v.9 no.2
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• pp.215-233
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• 2007

Even though the ad hoc Shewhart methods remain controversial due to various mathematical flaws, there is little disagreement among researchers and practitioners when a set of process data has a skewness distribution. In the context and language of process control, the error related to the process data shows that time to signal increases when a control parameter shifts to a skewness direction. In real-world industrial settings, however, quality practitioners often need to consider a skewness distribution. To address this situation, we developed an enhanced design method to utilize advantages of the traditional attribute control chart and to overcome its associated shortcomings. The proposed design method minimizes bias, i.e., an average time to signal for the shift of process from the target value (ATS) curve, as well as it applies a variable sampling interval (VSI) method to an attribute control chart for detecting a process shift efficiently. The results of the factorial experiment obtained by various parameter circumstances show that the VSI c control chart using nearly unbiased ATS design provides the smallest decreasing rate in ATS among other charts for all experimental cases.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.265-277
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• 1998

The control chart has been used widely and importantly as a tool for statistical process control(SPC). Most companies are concerned with improving the quality and the productivity as well as reducing the cost, especially in today's highly competitive environment. Though SPC is known as a technique for consistent quality, it is not used properly due to lack of knowledge about it. It is required to develop a support system for control chart selection and interpretation that can be utilized by non-specialist without hard training or experiences. The support system was developed by applying the expert system tool to popular control charts. Though some researches on this area has been performed, the implemented results expose many problems in field applications due to the unsatisfactory explanation of the selected control chart and limited knowledge base for resolving the problems. This thesis presented an expert system for control chart as solution for these problems. The expert system for the control chart selection and interpretation is developed by using Turbo C and EXSYS which is an expert system development tool.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.73-85
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• 1997

In the product liability age the demand on quality is extremely high and inspection and test are automated. The process capability indices $C_p, {\;}C_{pk}$ and p control chart widely used to provide unitless measure of process performance and process control. Traditional process capability indices $C_p, {\;}C_{pk}$ do not represent the process variation from target value. The convention p chart for control of fraction nonconforming becomes inadequate when the fraction nonconforming becomes very small such as PPM level production system. This paper proposes process performance measure considering quadratic loss function and cumulative counts control chart for control of PPM level production system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.45-54
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• 2001

The process capability indices have been widely used in manufacturing industries to provide numerical measures of process potential and performance. This study is concerned with process controls and adjustments by incapability index $C_{pp}$ and its sub-indices. A monitoring for $\^{C}_{pp}$ would provide a convenient way to monitor changes on process capability after statistical control is established, since $C_{pp}$ simultaneously measures process variability and centering. Further, we can separate charting of process location and variability by sub-indices of $C_{pp}$, ($C_{ia}$, $C_{ip}$), without returning to $\={x}$-R chart, even though an out-of-control signals on $\^{C}_{pp}$ control chart is found.

• Journal of Korean Society for Quality Management
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• v.44 no.3
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• pp.587-600
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• 2016

Purpose: This paper evaluates the performance of the pre-control(PC), an alternative to statistical process control techniques and compares with a control chart considering the tolerance of process. Methods: The previous studies for PC have drawbacks that PC with two linked stages, qualification and running, are discussed separately and independently. Hence this paper analyzes the performance of PC by integrating two stages. Results: Average outgoing quality limits to grasp the outcome of PC are provided by computational results for two process capability indexes, $C_p$ and $C_{pk}$ and the usefulness of PC from comparative experiments with modified control charts is commented. Conclusion: Helpful guidelines for quality managers to apply PC in practice and areas of process for PC to be more benefit are presented.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.149-153
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• 2008

$C_p$ and other process capability indices are used extensively in industry, However, They are inadequate and widely misused. In a practical application, process average ${\mu}$ is almost always drifted by various assignable causes in process. And control charts will not detect these shifts in process average. In this study, incorporating these undetected shifts, a new capability analysis method is introduced.