• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.174-192
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• 1998

These day in the Quality Assurance System, it required that computer system is able to utilize SQC and ISO 9000 series, just then. However. because SQC is necessary to special branch of statistical experience, it is very hard to use and have a bound in that apply to practical business. Therefor in this paper, we proposed the software system which is control, at the same time, SQC and ISO 9000 series, in addition to avail without statistical knowledge. For the shake of this point, the substance of this study is largely separated dual part that ISO 9000 system for which is limited to use design of basic system, test, experiment, tracking incongruent part, analysis condemned goofs, and SQC system for which is to limited to use control chart, estimate, statistical testing, design of experiment(DOE) which used to commonly in Quality Control System. Expected efficiency of this paper is to set Qualify Management System, transfer qualify consciousness, satisfy the customer, increase reliability, in particular, it is expected that it is very useful to minor enterprises of manufacturing-centered.

• Proceedings of the Safety Management and Science Conference
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• 2010.04a
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• pp.253-258
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• 2010

The paper reviews the methodologies of confirmatory data analysis(CDA) and exploratory data analysis(EDA) in statistical quality control(SQC), design of experiment(DOE) and reliability engineering(RE). The study discusses the properties of flexibility, openness, resistance and reexpression for EDA.

• The Korean Journal of Applied Statistics
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• v.1 no.1
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• pp.29-38
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• 1987

This article deals with statistical quality control (SQC) activities in manufacturing processes, First of all, the sources of variatin in product quality are listed and studied. Next, a typical SQC activity system in manufacturing processes is provided with a flow-chart, and each step in the system is explained. A real case example of a domestic company which follows the SQC activity system is illustrated. Finally, some approaches to activate SQC in manufacturing companies are suggested.

• International Journal of Quality Innovation
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• v.2 no.2
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• pp.127-135
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• 2001

Personnel engaged in the medical field have implemented continual improvement by team activities in an effort to construct a system that reduces the risks involved in medical care. Knowledge in total quality management (TQM), especially statistical quality control (SQC) developed for industry, seems to be applicable to medical care. This paper describes the application of SQC to continual improvement in medical care.

• Journal of Korean Society for Quality Management
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• v.41 no.3
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• pp.487-494
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• 2013

Purpose: In this study, we propose the precision guided munitions verification methodology using the statistical analysis method has been proposed. and it can be applied to the precision guided munitions quality assurance work. Methods: This modeling is based on Failure Mode and Effects Analysis, Statistical Process Control, Defense Quality Managerment System, Production Readiness Review, Manufacturing Readiness Assesment and so on. Results: The Process Control Modeling that has the following procedures ; searching the critical to quality, statistical analysis by process, verify process. Moreover, the effectiveness of the methodology is verified by applying to the precision guided munitions. Conclusion: To achieve a analysis methods of statistical process control and verify process for precision guided munitions.

• Journal of Information Technology Application
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• v.3 no.4
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• pp.119-134
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• 2001

Improving software product quality is a key to increasing user satisfaction and to achieving competitive edge. There are two approaches to assure high software product quality; development process-oriented and product-oriented. There have been many efforts for improving software quality through process certification, for example, CMM, ISO 9000 family, ISO/IEC 12207, SPICE and Bootstrap. However, a good process alone cannot guarantee good product quality. A need for the evaluation of software product quality by an independent third party is growing rapidly for several reasons. We are concerned with an application of Statistical Quality Control (SQC) to the evaluation of software product quality to obtain the efficiency of evaluation processes and the objectivity of evaluation results. Methods for selecting test cases using a random sampling approach have been discussed and methods for selecting acceptance criteria with respect to software product quality have also been suggested.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.134-144
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• 2014

With the advent of lean-six sigma era, an extensive use of analytic tools such as control charts is required in the field of manufacturing. In relation to statistical quality control (SQC) or process control (SPC), the Korean standards have undergone a meaningful change. In this study, the theoretic backgrounds for evaluating the control limits in connection with the variable control charts are examined in view of better understanding the related constants and coefficients. This paper is intended to help the quality control practitioners understand the mathematical backgrounds by comparing related quality control constants and also to encourage them to make use of and to take the advantage of the variable control charts which are very useful for implementing the concept of lean-six sigma in many industrial sites.

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.86-93
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• 2002

There are many different factors used in calculating control limits of control chart for variable data. Specially these factors are divided into two groups such as "no given standard" and "given standard"(namely, for analysis and management), but many kind of factors give rise to confusion. Therefore, It is necessary to manifest relationships among factors for easy application regardless of subgroup size. Many SQC textbooks show us plainly these factors, but do not have enough for adequate explanation of relationships among factors. Besides, notation of these factors of SQC textbook isn't coincide with another one, so necessity to the coincidence for the notation of the factors is highlighted during my work recently. In this study, the close examination about relationships among various factors (A-A3, B3-B6, D1-D4, C4, d2, d3 etc) was carried out. Spread sheet results are presented for getting factors according to subgroup size, by grouping as the case of "no given standard" and "given standard". How are these factors to be applied in statistical package (ex, Minitab) have been analyzed using a series of sample data. analyzed using a series of sample data.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.663-675
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• 2023

Purpose: Skewness is an indicator used to measure the asymmetry of data distribution. In the past, product quality was judged only by mean and variance, but in modern management and manufacturing environments, various factors and volatility must be considered. Therefore, skewness helps accurately understand the shape of data distribution and identify outliers or problems, and skewness can be utilized from this new perspective. Therefore, we would like to propose a statistical quality control method using skewness. Methods: In order to generate data with the same mean and variance but different skewness, data was generated using normal distribution and gamma distribution. Using Minitab 18, we created 20 sets of 1,000 random data of normal distribution and gamma distribution. Using this data, it was proven that the process state can be sensitively identified by using skewness. Results: As a result of the analysis of this study, if the skewness is within ± 0.2, there is no difference in judgment from management based on the probability of errors that can be made in the management state as discussed in quality control. However, if the skewness exceeds ±0.2, the control chart considering only the standard deviation determines that it is in control, but it can be seen that the data is out of control. Conclusion: By using skewness in process management, the ability to evaluate data quality is improved and the ability to detect abnormal signals is excellent. By using this, process improvement and process non-sub-stitutability issues can be quickly identified and improved.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.37-46
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• 2016

Quality has been a key issue to manufacturers. Many distinguished scholars have defined quality with profound insight. Korean firms struggle to make better products to fulfil requirements and satisfy customers. Korean industries have implemented quality management from Japan in early 70s. Statistical quality control, QCC (Quality Control Circle), and total quality management have also been introduced in succession. Chief executive officers, managers, and field employees have been aware of the importance of quality since then. This quality movement force workers to improve quality. They have to maintain the quality of products and compete with foreign products. Korean industries were able to compete with foreign industries in price. However, Korean firms now have to compete in quality as well as price. ISO (International Organization for Standardization) was established and industries around world have started to implement standardized systems depending on their need. ISO 9000 has continuously been revised and firms around world started to register a ISO 9000 certificate. Today's quality competitiveness gets more deeply involved. KSA (Korean Standard Association) have launched QCAS (Quality Competitiveness Assessment System) since 1997. Up until now recent status of QCAS have been reported but the characteristics of QCAS results have not been analyzed. In this article we examine the QCAS results of 41 firms in 2014. QCAS consisted of 13 subsections : strategy and management system, organization culture and development of human resource, information management, quality system, customer satisfaction, management achievement, TPM, logistics, product development and technology, PL, QCC, SQC/SPC, and reliability. We performed one way ANOVA to discover the difference among the levels of firm size, business type, and quality hall of fame using the total scores of 13 subsections resulted from QCAS. We also analyzed the scores of 13 individual subsections of QCAS to see if there is any differences based on firm size and business type. We interpret the results and implication of analysis and finally draw a conclusion.