• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.513-522
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• 2006

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.129-135
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• 2006

In the advance of computer technology, it is possible to keep all the related informations for monitoring equipments in control and huge amount of real time manufacturing data in a data base. Thus, the statistical analysis of large data sets with hundreds of thousands observations and hundred of independent variables whose some of values are missing at many observations is needed even though it is a formidable computational task. A tree structured approach to classification is capable of screening important independent variables and their interactions. In a Six Sigma project handling large amount of manufacturing data, one of the goals is to screen vital few variables among trivial many variables. In this paper we have reviewed and summarized CART, C4.5 and CHAID algorithms and proposed a simple method of screening vital few variables by selecting common variables screened by all the three algorithms. Also how to develop a logistics regression model on a large data set is discussed and illustrated through a large finance data set collected by a credit bureau for th purpose of predicting the bankruptcy of the company.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.184-195
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• 2004

Current companies 집중 on how to operate and select projects to achieve the best result. 6sigma projects are chosen in the best suitable concept, which are solved by the 6Sigma experts according to the priority. And every project has to be launched not the view of individual management factors but the total factors, Big Y. Therefore, a process needs to be treated to connect the vital few factors in various processes to improve the yield, which is the main performance criteria in a manufacturing industry This report is to make the total optimization through the Vital-Few mapping between quality characteristics and process factors in a manufacturing line. Accordingly, it means to secure lower variance by making the CTP(Critical To Process) optimization and finally to improve the yield.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.425-440
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• 2022

Purpose: The purpose of this paper is to improve the chromaticity coordinate quality of white LED packages for automobiles that require high quality and reliability. Methods: The project follows the structured methodology of the Six Sigma DMAIC Roadmap, which consists of Define, Measure, Analyze, Improve and Control phases. Results: A CTQ is determined based on COPQ analysis, and a process map and a XY matrix are utilized for selecting process input variables. Three vital Few Xs are identified through data analysis; amount to mix at one time, deviation by head pumps, and deviation by production magazines, and process improvements are performed for each of the three vital Few Xs. Conclusion: The improved process conditions for the three vital Few Xs are applied to the production line, and the results show that the percent defective of chromaticity coordinate has improved from 1.59% to 0.63% and a financial effect of about 50 million won per year is obtained.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.487-493
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• 2004

This paper consider a six sigma project for improving the TIP life time in a spot welding process. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Nine key process input variables are selected by using C&E matrix and FMEA, and finally four vital few input variables are selected from analyze phase. The optimum process conditions of the four vital few input variables are jointly obtained by maximizing TIP life time using DOE.

• Journal of Korean Society for Quality Management
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• v.33 no.3
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• pp.135-148
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• 2005

This paper considers a six sigma project for reducing the project costs through project risk management. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A risk management process map is used to identify process input and output variables. Seven key process input variables are selected by using C&E diagram and X-Y matrix and finally four vital few input variables are selected by the related statistical analysis. The optimum alternatives of the vital few input variables are obtained by the method of PUGH matrix. The process is running on control plan and we obtained substantial project cost reductions in early stage of the control phase.

• Journal of Korean Society for Quality Management
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• v.39 no.4
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• pp.500-506
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• 2011

Evolutionary operation (EVOP) proposed by Box(1957) is a method for continuous monitoring and improvement of a full-scale manufacturing process with the objective of moving the current operating conditions toward the better ones. EVOP in experiments with mixtures consists of screening vital few components and making small changes in the current operating condition by making small increments in the proportion of the screened component. In this paper, how to determine operating conditions in EVOP in experiments with mixtures around the current operating condition is proposed. The proposed methods are illustrated with the simulated data based on the well known flare experimental data described by McLean and Anderson(1966).

• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.88-98
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• 2005

This paper considers a six sigma project for improving the TIP life time in a spot welding process. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Nine key process input variables are selected by using C&E matrix and FMEA, and finally four vital few input variables are selected from analyze phase. The optimum process conditions of the vital few input variables are jointly obtained by maximizing TIP life time using DOE and alternative selection method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.9-17
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• 2006

This paper considers a six sigma project for improving productivity of the brace complement center pillar. The project follows a disciplined process of fife phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Eleven key process input variables are selected by using X&Y matrix and FMEA, and finally eight vital few input variables are selected from analyze phase. The optimum process conditions of the vital few input variables are jointly obtained by maximizing productivity of the brace complement center pillar using DOE and alternative selection method.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2888-2898
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• 2021

Vital area identification (VAI) is an essential procedure for the design of physical protection systems (PPSs) for nuclear power plants (NPPs). The purpose of PPS design is to protect vital areas. VAI has been improved continuously to overcome the shortcomings of previous VAI generations. In first-generation VAI, a sabotage fault tree was developed directly without reusing probabilistic safety assessment (PSA) results or information. In second-generation VAI, VAI model was constructed from all PSA event trees and fault trees. While in third-generation VAI, it was developed from the simplified PSA event trees and fault trees. While VAIs have been performed for NPPs in full-power operations, VAI for NPPs in low-power and shutdown (LPSD) operations has not been studied and performed, even though NPPs in LPSD operations are very vulnerable to sabotage due to the very crowded nature of NPP maintenance. This study is the first to research and apply VAI to LPSD operation of NPP. Here, the third-generation VAI method for full-power operation of NPP was adapted to the VAI of LPSD operation. In this study, LPSD VAI for a few plant operational states (POSs) was performed. Furthermore, the operation strategy of vital areas for both full-power and LPSD operations was discussed. The LPSD VAI method discussed in this paper can be easily applied to all POSs. The method and insights in this study can be important for future LPSD VAI that reflects various LPSD operational states. Regulatory bodies and electric utilities can take advantage of this LPSD VAI method.