• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.81-100
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• 1999

Process capability indexes are widely used in industries. Nevertheless, important statistical issues that deal with the appropriate use of process capability indexes have only appeared in the technical literature and are thus unknown to many users. In this paper we will first review various kinds of process capability indexes and then try to understand how much uncertainty is associated with them in relation to the assumption of normal distribution, the estimate of ${\sigma}$, the sampling error, the size of number of observations available and the presence of data correlation. Finally, we will propose a proper use of process capability indexes in an effort to assess a complex process.

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.44-57
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• 2002

In this paper we study two vector-valued process capability indices $C_{p}$＝($C_{px}$, $C_{py}$ ) and $C_{pk}$＝( $C_{pkx}$, $C_{pky}$) considering process capability indices $C_{p}$ and $C_{pk}$. First, we derive two asymptotic distributions of plug-in estimators (equation omitted) and (equation omitted) under. some proper. conditions. Second, we examine the performance of asymptotic confidence regions of our process capability indices $C_{p}$＝( $C_{px}$ , $C_{py}$ ) and $C_{pk}$＝( $C_{pkx}$, $C_{pky}$) under BN($\mu$$_{x}$, $\mu$$_{y}$, $\sigma$$^2$$_{x}$, $\sigma$$^2$$_{y}$,$\rho$)$\rho$)EX>)EX>)EX>)

• 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 for Quality Management
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• v.47 no.4
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• pp.713-723
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• 2019

Purpose: The purpose of this study is to develop a statistical program for capability analysis of measuring system and measurement process based upon KS Q ISO 22514-7. Methods: R is a powerful open source functional programming language that provides high level graphics and interfaces to other languages. Therefore, in this study, we will develop the statistical program using R language. Results: The R program developed in this study consists of the following five modules. ① Measuring system capability analysis with Type 1 study data: MSCA_Type1.R ② Measuring system capability analysis with Linearity study(Type 4 study) data: MSCA_Type4.R ③ Measurement process capability analysis with Type 1 study & Gage R&R study data: MPCA_T1GRR.R ④ Measurement process capability analysis with Type 4 study & Gage R&R study data: MPCA_T4GRR.R ⑤ Attribute measurement processes capability analysis : AttributeMP.R Conclusion: KS Q ISO 22514-7 evaluates measuring systems and measurement processes on the basis of the measurement uncertainty that was determined according to the GUM(KS Q ISO/IEC Guide 98-3). KS Q ISO 22514-7 offers precise procedures, however, computations are more intensive. The R program of this study will help to evaluate the measurement process.

• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.60-82
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• 1997

The purpose of this study is to empirically examine the quality competence as a source of competitive advantages. The relationship between quality priority and competitive priority, and the relationship between quality competence and competitive capability are investigated using data collected from 114 manufacturing business units of 75 manufacturing companies. Several measures are used to characterize competitive strategy and value chain, and quality factors including conformance, reliability, durability, performance, and customization. The result indicates that the quality competence paired with competitive strategy positively influences the level of competitiveness, but management performances are not strongly correlated with quality competence.

• Journal of the Korea Safety Management & Science
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• v.1 no.1
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• pp.123-134
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• 1999

There are several models for process quality assurance by quality system (ISO 9000), process capability analysis, acceptance control chart and so on. When a high level process capability has been achieved, it takes a long time to monitor the process shift, so it is sometimes necessary to develop a quicker monitoring system. To achieve a quicker quality assurance model for high-reliability process, this paper presents a model for process quality assurance when the fraction nonconforming is very small. We design an acceptance control chart based on variable quality characteristic and time-censored accelerated testing. The distribution of the characteristics is assumed to be normal or lognormal with a location parameter of the distribution that is a linear function of a stress. The design parameters are sample size, control limits and sample proportions allocated to low stress. These paramaters are obtained under minimization of the relative variance of the MLE of location parameter subject to APL and RPL constraints.

• Management & Information Systems Review
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• v.32 no.2
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• pp.259-287
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• 2013

This study is to comparate on impact factors and moderator of incentives of knowledge transfer between Nonprofit and Profit organization. In nonprofit organization, the results founded that individual information capability, quality of knowledge, and quality of construct were statistically significant in knowledge acquisition, informal communication and quality of construct were statistically significant knowledge sharing, and individual information capability, informal communication, quality of knowledge and quality of construct were statistically significant knowledge use. Also interactive effect of knowledge sharing and Psychological Physical incentive was statistically significant knowledge use. In profit organization, the results founded that individual information capability, informal communication, quality of knowledge, and quality of construct were statistically significant in knowledge acquisition, quality of knowledge and quality of construct were statistically significant knowledge sharing, and individual information capability, informal communication, and formal communication were statistically significant knowledge use. Also interactive effect of knowledge acquisition and Psychological Physical incentive was statistically significant knowledge use.

• International Journal of Quality Innovation
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• v.9 no.1
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• pp.41-54
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• 2008

The process capability indices have been widely used to measure process capability and performance. In this paper, we proposed a new process capability index which is based on an actual dollar loss by defects. The new index is similar to the Taguchi's loss function and fully incorporates the distribution of quality attribute in a process. The strength of the index is to apply itself to non-normal or asymmetric distributions. Numerical examples were presented to show superiority of the new index against $C_p$, $C_{pk}$, and $C_{pm}$ which are the most widely used process capability indices.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.115-123
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• 2018

This study is empirically intended to put forward the modeling and process improvement measures for the SCM capability in small venture firms. The findings are summarized as follows. There were strategic alliance, technological development and centralization in the modeling of strategic planning for supply chain, not the least of which is strategic alliance, followed by centralization and technological development. There were routing scheduling, network integration and third party logistics outsourcing in decision making, not the least of which was network integration. There were customer service management, productivity management and quality management in management control, not the least of which was quality management. And there were order management choice, pricing demand, shipment delivery and customer management in transaction support system, not the least of which was order management choice. As for the above-mentioned findings, to maximize the SCM capability and operate the optimized process in small venture firms, the existing strategic alliances can optimize the quality management and stabilize the transaction support system through the network sharing and integration from the perspective of relevant organizational members' capability and process improvement. And the strategic linkage between firms can maximize the integrated capability of information system beyond the simple exchange relation between electronic data, achieving a differentiated competitive advantage. Consequently, the systematization and centralization for the maximization of SCM capability, including the infrastructure construction based on the system compatibility and reliability for information integration, should be preceded before the modeling of the integrated capability for optimum supply chain and the best process management in the smart era.

• Journal of Korean Society for Quality Management
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• v.42 no.4
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• pp.685-700
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• 2014

Purpose: The purpose of this paper is to develop a systematic weighting procedure based on process capability indices method applying weighted mean squared error minimization (WMSE) approach to dual response surface optimization. Methods: The proposed procedure consists of 5 steps. Step 1 is to prepare the alternative vectors. Step 2 is to rank the vectors based on process capability indices in a pairwise manner. Step 3 is to transform the pairwise rankings into the inequalities between the corresponding WMSE values. Step 4 is to obtain the weight value by calculating the inequalities. Or, step 5 is to obtain the weight value by minimizing the total violation amount, in case there is no weight value in step 4. Results: The typical 4 process capability indices, namely, $C_p$, $C_{pk}$, $C_{pm}$, $C_{pmk}$ are utilized for the proposed procedure. Conclusion: The proposed procedure can provide a weight value in WMSE based on the objective quality performance criteria, not on the decision maker's subjective judgments or experiences.