• Journal of the Korea Safety Management & Science
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• v.5 no.3
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• pp.145-156
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• 2003

As we understand it, Process Capability indices are intended to provide single-number assessments of ability to meet specification limits on quality characteristics of interest. As a consequence of the varied ways in which PCIs are used, there have been two natural lines of research work: $\circled1$ studies on the properties of PCIs and their estimators in many different environments; $\circled2$ construction of new PCIs purporting to have better properties in certain circumstances. The most of existing process capability indices are concerned with the single variable. But, in many cases, a quality characteristic is composed with several factors. In that case, we want to know the integrated process capability of a quality characteristic not those of each factor. In this paper, we proposed a new multivariate system process capability index called $MSPCI:SC_{psk}$ which is the geometric mean of performance measure $C_{psk}$'S, and will be used as the criterion to assess multiple response process designs. Numerical illustration is done for $SC_{psk}$, $\overline{C_p}$(f), Cp, Cpk, Cpm, and Cpsk.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.106-114
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• 2019

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as $MC_{pm}$, $MC^+_{pm}$ and $MC_{pl}$. These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.28-36
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• 2007

Process Capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process performance. PCIs have been developed to represent process capability more exactly. In the previous studies, only one designated location on each part is measured. But even though in single process, multiple measurement locations on each part are required to calculate the reliable process capability. In this paper, we propose a new process capability index with multiple measurement locations on each part. We showed numerical examples and sensitivity analysis according to the number of measurement locations.

• Journal of the Korean Society of Systems Engineering
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• v.9 no.2
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• pp.69-82
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• 2013

US DoD operated JCIDS(Joint Capability Integration and Development System) for top down requirement generation. Although the JCIDS can be a good practice for the countries which are trying to shift from bottom up to top down requirement generation, it contains many processes related with review and approval. In this study we structured a joint capability integration and development process from the JCIDS eliminating the organization dependent review or approval process so that it can be applied to any organization with some modification. Furthermore we implemented the process in the computer aided systems engineering tool, Cradle, for convenient use of the process. The result of this study can provide a basic process for top down capability development, and an efficient why of doing each element of the process using CASE tool.

• Journal of the Korea Safety Management & Science
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• v.2 no.2
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• pp.41-55
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• 2000

The main objective of this paper is to propose a measure of evaluation for non-normal process capability. If a process is not normally distributed, but normal-based techniques are used, serious errors can result. Our approach to solve this problem is that the Pearson system, the Johnson system, and the Burr system are selected for estimating a measure of process capability using the percentage nonconforming. In this paper, we found that the Pearson system and the Johnson system were a conparatively reasonable methods to calculate out of specification by example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.109-115
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• 2013

Process Capability Index (PCI) is useful Statistical Process Control (SPC) tool that is measure of process diagnostic and assessment tools widely use in industrial field. It has advantage of easy to calculate and easy to use in the field. $C_p$ and $C_{pk}$ are traditional PCIs. These are only considers of process variation. These are not given information about the characteristic value does not match the target value of the process. Studies of this process capability index by many scholars actively for supplement of its disadvantage. These studies to evaluate the capability of situation of various field has presented a new process capability index. $C_{pm}$ is considers both the process variation and the process deviation from target value. And $C_{pm}{^+}$ is considers economic loss for the process deviation from target value. In this paper development of new process capability index that is Taguchi's quadratic loss function by applying the expected loss. And check the correlation between existing traditional process capability index ($C_{pk}$) and new one. Finally, we propose the criteria for classification about developed process capability index.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.869-878
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• 2007

In this paper a new process capability index $C_{psks}$ is introduced for non-normal process. $C_{psks}$ that is proposed by transformation of the $C_{psks}$ incorporates an additional skewness correction factor in the denominator of $C_{psks}$. The use of each technique is illustrated by reference to a distribution system which includes the Pearson and Johnson functions. Accordingly, $C_{psks}$ is proposed as the process capability measure for non-normal process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.347-356
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• 1998

For assessing the capability of a process, the quantification of process location and variation is central to understanding the quality of units produced from the manufacturing process. Conventional process capability indices is insufficient to drive out the information for process condition, furthermore it is very difficult to evaluate the process capability accurately when the target value is not consistent with the center of specification, and/or the shape of distribution is changed, but the process incapability indices is enable to provide more detailed information to evaluate the process capability by dividing information about the process mean and variance. In this paper, we have a brief review and comparison about these indices, provide an understanding of the relationships between the process capability indices and the incapability indices. And we explore the strengths and weakness of these indices as they apply to normally distributed process, and to examine the effect that non-normality has on these indices.

• Journal of the Korea Safety Management & Science
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• v.3 no.3
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• pp.175-190
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• 2001

We propose, a new process capability index $C_{psk}$(WV) applying the weighted variance control charting method for non-normally distributed. The main idea of the weighted variance method(WVM) is to divide a skewed or asymmetric distribution into two normal distributions from its mean to create two new distributions which have the same mean but different standard deviations. In this paper we propose an example, a distributions generated from the Johnson family of distributions, to demonstrate how the weighted variance-based process capability indices perform in comparison with another two non-normal methods, namely the Clements and the Wright methods. This example shows that the weighted valiance-based indices are more consistent than the other two methods in terms of sensitivity to departure to the process mean/median from the target value for non-normal processes. Second method show using the percentage nonconforming by the Pearson, Johnson and Burr systems. This example shows a little difference between the Pearson system and Burr system, but Johnson system underestimated than the two systems for process capability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.182-187
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• 2015

Process capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance to meet the specification limits on quality characteristics. The most of existing PCIs are concerned with a single variable. But, in many cases, people want to express a integrated PCI which includes a couple of sequential processes. In this paper, we analyzed the characteristics of system PCIs such as ${\bar{Cp}}(f)$, $SC_{pk}$, $SC_{psk}$, $C^T_{psk}(m)$ and $SC_{pm}(m)$.