• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.229-241
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• 2004

Process capability index is used to determine whether a production process is capable of producing items within a specified tolerance. The index $C_{pmk}$ is the third generation process capability index. This index is more powerful than two useful indices $C_p$ and $C_{pk}$. Whether a process distribution is clearly normal or nonnormal, there may be some questions as to which any process index is valid or should even be calculated. As far as we know, yet there is no result for statistical inference with process capability index $C_{pmk}$. However, asymptotic method and bootstrap could be studied for good statistical inference. In this paper, we propose various bootstrap confidence limits for our process capability Index $C_{pmk}$. First, we derive bootstrap asymptotic distribution of plug-in estimator $C_{pmk}$ of our capability index $C_{pmk}$. And then we construct various bootstrap confidence limits of our capability index $C_{pmk}$ for more useful process capability analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.1-10
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• 2002

Process capability indices are widely used in industries and quality assurance system. In past years, process capability analysis have been used to characterize process performance on the basis of univariate quality characteristics. However, in actual manufacturing industrial, statistical process control (SPC) often entails characterizing or assessing processes or products based on more than one engineering specification or quality characteristic. Therefore, the analysis have to be required a multivariate statistical technique. This paper introduces to multivariate capability indices and then selects a multivariate process capability index incorporated both the process variation and the process deviation from target among these indices under the multivariate normal distribution. We propose a new multivariate capability index $MC_{pm}^+$ using quality loss function instead of the process variation and this index is compared with the proposed indices when quality characteristics are independent and dependent of each other.

• 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.35 no.1
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• pp.16-23
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• 2012

Recently, as organizational systems have become larger and more complicated, the evaluation for their efficiency and effectiveness has become more difficult but important. It is essential to understand the current strength and weakness of the organizational process. It can be a starting point for improving the efficiency and effectiveness of the organizational systems, because the quality of system outputs depend greatly on the capability of system process. Particularly in such process-based industries as semiconductor, energy or software industries, an assessment of process capability is more highlighted to gain knowledge of the expected quality and reliability of system outputs. As a result, much attention has been given to the issues of process capability assessment in the process-based industries. However, most of the previous research in those industries is based on case studies, a more generalized method for process capability assessment is in need for help more companies improve their processes. Therefore, this study aims to propose a process capability assessment method and apply the proposed method to an energy company. This research argues that the process capability is composed of individual and organizational capabilities of the process. Then, the concept of Capability Maturity Model Integration, which was initially suggested to evaluate the software development process, was introduced to develop the assessment tools and process. Finally, the proposed method was applied to a Korean company in the energy industry sector to verify its utility. The research outputs are expected to help more firms assess their process capability and ultimately improve the 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.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.599-610
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• 2008

It is an important and urgent issue to improve process capability in quality control. Process capability refers to the uniformity of the process. The variability in the process is a measure of the uniformity of output. A simple, quantitative way to express process capability, the degree of variability from target in specification is defined by process capability index(PCI). Almost process capability indices are defined under normal distribution. However, these indices can not be applied to the process of non-normal distribution including reliability. We investigate current research on the process of non-normal distribution, and advanced method and technology for developing more reliable and efficient PCI. Finally we suggest the perspective for future study.

• IE interfaces
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• v.16 no.spc
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• pp.65-69
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• 2003

Process capability has long been viewed as a critical performance measure to indicate how well a process meet the specifications and customer requirements. Several indices, including $C_p$ and $C_{pk}$, have been proposed and widely implemented to quantify the process capability. However, these indices have been obtained without regard to inspection or screening procedures through which finished products will be truncated at the specifications. Consequently, only a fraction of outgoing products within the specifications will be passed into the customers. From the customer's point of view, it will thus be meaningful to assess the process capability with truncated samples. This article investigates how to estimate the process capability when only incomplete truncated data are available. On the basis of parameter estimation for truncated samples, the proposed methodology may be helpful to evaluate the process capability by examining a sample of items from the lots submitted.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.41-49
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• 2017

Process quality control, which prevents problems and risks that may occur in products and processes, has been recognized as an important issue, and SPC techniques have been used for this purpose. 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 traditional $C_p$ and $C_{pk}$ were used only as a measure of process capability, taking into account the quality variance or the bias of the process mean. These are not given information about the characteristic value does not match the target value of the process and this has the disadvantage that it is difficult to assess the economic losses that may arise in the enterprise. 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 we developed an improved Expected Loss Capability Index using Reflected Normal Loss Function of Spring. This has the advantage that it is easy to realistically reflect the loss when the specification is asymmetric around the target value. 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 Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.114-119
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• 2011

Process capability indices (PCls) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance. The previous studies have measured only one location on each part in the case of single variate. To calculate the reliable process capability, a couple of measuring locations on each part are required. In this paper, we propose a new system process capability index $SC_{pm}$ (m) which is the minimum value of the location PCls.

• 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.