• Journal of Korean Society for Quality Management
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• v.31 no.2
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• pp.194-204
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• 2003

It is very important to understand and interpret the meaning of the sigma level correctly through the Six Sigma project. Especially, the confusion over the relation between sigma level from the short-term point of view and defective proportion or DPMO from the long-term point of view may make a big gap between expected results of the Six Sigma project and real results in the field. The one-tail approximation is commonly used to calculate the sigma level both in most literatures introducing Six Sigma and actual cases of the Six Sigma project. Since the one-tail approximation undervalues the sigma level of the fields such as business and service of which the sigma level is generally low, however. there can be misleading results of the explanation of the sigma level and inappropriate project evaluation. This paper describes the relation between sigma level and defective proportion in detail and clears the difference between the one-tail and two-tail approximation.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1585-1588
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• 2008

조도계수를 산정하는데는 수많은 방법이 있다. 그러나 일반적으로 하천정비기본계획(또는 댐실시설계보고서 등 각종보고서)의 조도계수를 그대로 적용하거나, 기준이 모호한 조도계수정보테이블(Roughness Coefficients Information Table)을 이용하고 있다. 하천의 이용은 시대에 따라 달라지며 최근 하천정비 사업의 급속한 진행으로 하천정비기본계획의 조도계수 또한 하천형상을 정확히 반영하기 어려운 현실이다. 소하천의 경우 하천정비기본계획 마저 수립되어 있지 않은 경우도 있어 조도계수로 인한 하천모의에 영향은 매우 크다. 따라서 소하천의 정확한 모의를 위해서는 소하천에 적합한 조도계수 산정방법에 대한 비교분석이 선행되어야 한다. 그러므로 본 연구에서는 실제 소하천에서 획득한 실측자료로부터 다양한 조도계수 산정방법을 적용하여 그 결과를 비교 분석하였다.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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• pp.110-124
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• 2006

There have been various papers about service quality. This article is one of them. This is about the measurement of service quality in the competitive structure between department and discount store. In this paper, we tried to measure the service quality and overall satisfaction by using 6 sigma, degree of combination and top2box which is a little bit different methodology from traditional ones. The data were collected by the internet survey from 1428 and 1605 department and discount store customers respectively. The result shows the different patterns in the each retail industry. Also, there is a significant difference in terms of sigma level in the each retail industry. Finally, we showed the summarized result as the 6 Sigma Portfolio Matrix.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.57-64
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• 2010

Metal Injection Molding (MIM) is attractive because it produces consistent, complex-geometry components for high-volume, high-strength, and high-performance applications. Also MIM using in optical communication field, display field, and semi-conductor field is a cost-effective alternative to metal machining or investment casting parts. It offers tremendous single-step parts consolidation potential and design flexibility. The objective of this paper is to study the suitability of design, flow analysis, debinding and sinterin processes, and capability analysis. The suitable injection conditions were 0.5~1.5 second filling time, 11.0~12.5 MPa injection pressure derived from flow analysis. The gravity of the product is measured after debinding an sintering. The maximum and minimum gravity levels are 7.5939 and 7.5097. the average and standard deviation are 7.5579 and 0.0122; when converted into density, the figure stands at 98.154%. According to an analysis of overall capacity, PPM total, which refers to defect per million opportunities(DPMO), stands at 166,066.3 Z.Bench-the sum of defect rates exceeding the actual lowest and highest limits-is 0.97, which translates into the good quality rate of around 88.4% and the sigma level of 2.47.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.823-834
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• 2020

In the past, when the personal information leakage incident of the three card companies, the computer program development was followed by the same strict electronic financial supervision regulations as the operating environment. However, when developing a computerized program, the application data is being verified with the integrity of the test data being compromised because the identification of the scope of conversion of the test data associated with the application is unclear. Therefore, in this paper, we proved by presenting a process and algorithm for selecting a range of sufficient test data conversion targets associated with a specific application.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.169-176
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• 2014

This research discusses the characteristics and the implementation strategies for two types of quality metrics to analyze innovation effects in six sigma projects: fixed specification type and moving specification type. $Z_{st}$, $P_{pk}$ are quality metrics of fixed specification type that are influenced by predetermined specification. In contrast, the quality metrics of moving specification type such as Strictly Standardized Mean Difference(SSMD), Z-Score, F-Statistic and t-Statistic are independent from predetermined specification. $Z_{st}$ sigma level obtains defective rates of Parts Per Million(PPM) and Defects Per Million Opportunities(DPMO). However, the defective rates between different industrial sectors are incomparable due to their own technological inherence. In order to explore relative method to compare defective rates between different industrial sectors, the ratio of specification and natural tolerance called, $P_{pk}$, is used. The drawback of this $P_{pk}$ metric is that it is highly dependent on the specification. The metrics of F-Statistic and t-Statistic identify innovation effect by comparing before-and-after of accuracy and precision. These statistics are not affected by specification, but affected by type of statistical distribution models and sample size. Hence, statistical significance determined by above two statistics cannot give a same conclusion as practical significance. In conclusion, SSMD and Z-Score are the best quality metrics that are uninfluenced by fixed specification, theoretical distribution model and arbitrary sample size. Those metrics also identify the innovation effects for before-and-after of accuracy and precision. It is beneficial to use SSMD and Z-Score methods along with popular methods of $Z_{st}$ sigma level and $P_{pk}$ that are commonly employed in six sigma projects. The case studies from national six sigma contest from 2011 to 2012 are proposed and analyzed to provide the guidelines for the usage of quality metrics for quality practitioners.

• Korean Journal of Clinical Laboratory Science
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• v.36 no.2
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• pp.121-126
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• 2004

The primary goal of six sigma is to improve patient satisfaction, and thereby profitability, by reducing and eliminating defects. Defects may be related to any aspect of customer satisfaction: high product quality, schedule adherence, cost minimization, process capability indices, defects per unit, and yield. Many six sigma metrics can be mathematically related to the others. Literally, six means six standard deviations from the mean or median value. As applied to quality metrics, the term indicates that failures are at least six standard deviations from the mean or norm. This would mean about 3.4 failures per million opportunities for failure. The objective of six sigma quality is to reduce process output variation so that on a long term basis, which is the customer's aggregate experience with our process over time, this will result in no more than 3.4 defect Parts Per Million(PPM) opportunities (or 3.4 Defects Per Million Opportunities. For a process with only one specification limit (upper or lower), this results in six process standard deviations between the mean of the process and the customer's specification limit (hence, 6 Sigma). The results of applicative six sigma experiment studied on 18 items TP, ALB, T.B, ALP, AST, ALT, CL, CK, LD, K, Na, CRE, BUN, T.C, GLU, AML, CA tests in clinical chemistry were follows. Assessment of process performance fits within six sigma tolerance limits were TP, ALB, T.B, ALP, AST, ALT, CL, CK, LD, K, Na, CRE, BUN, T.C, GLU, AML, CA with 72.2%, items that fit within five sigma limits were total bilirubin, chloride and sodium were 3 sigma. We were sure that the goal of six sigma would reduce test variation in the process.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.2
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• pp.65-70
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• 2005

Those specifications of the CLIA analytical tolerance limits are consistent with the performance goals in Six Sigma Quality Management. Six sigma analysis determines performance quality from bias and precision statistics. It also shows if the method meets the criteria for the six sigma performance. Performance standards calculates allowable total error from several different criteria. Six sigma means six standard deviations from the target value or mean value and about 3.4 failures per million opportunities for failure. Sigma Quality Level is an indicator of process centering and process variation total error allowable. Tolerance specification is replaced by a Total Error specification, which is a common form of a quality specification for a laboratory test. The CLIA criteria for acceptable performance in proficiency testing events are given in the form of an allowable total error, TEa. Thus there is a published list of TEa specifications for regulated analytes. In terms of TEa, Six Sigma Quality Management sets a precision goal of TEa/6 and an accuracy goal of 1.5 (TEa/6). This concept is based on the proficiency testing specification of target value +/-3s, TEa from reference intervals, biological variation, and peer group median mean surveys. We have found rules to calculate as a fraction of a reference interval and peer group median mean surveys. We studied to develop total error allowable from peer group survey results and CLIA 88 rules in US on 19 items TP, ALB, T.B, ALP, AST, ALT, CL, LD, K, Na, CRE, BUN, T.C, GLU, GGT, CA, phosphorus, UA, TG tests in chematology were follows. Sigma level versus TEa from peer group median mean CV of each item by group mean were assessed by process performance, fitting within six sigma tolerance limits were TP ($6.1{\delta}$/9.3%), ALB ($6.9{\delta}$/11.3%), T.B ($3.4{\delta}$/25.6%), ALP ($6.8{\delta}$/31.5%), AST ($4.5{\delta}$/16.8%), ALT ($1.6{\delta}$/19.3%), CL ($4.6{\delta}$/8.4%), LD ($11.5{\delta}$/20.07%), K ($2.5{\delta}$/0.39mmol/L), Na ($3.6{\delta}$/6.87mmol/L), CRE ($9.9{\delta}$/21.8%), BUN ($4.3{\delta}$/13.3%), UA ($5.9{\delta}$/11.5%), T.C ($2.2{\delta}$/10.7%), GLU ($4.8{\delta}$/10.2%), GGT ($7.5{\delta}$/27.3%), CA ($5.5{\delta}$/0.87mmol/L), IP ($8.5{\delta}$/13.17%), TG ($9.6{\delta}$/17.7%). Peer group survey median CV in Korean External Assessment greater than CLIA criteria were CL (8.45%/5%), BUN (13.3%/9%), CRE (21.8%/15%), T.B (25.6%/20%), and Na (6.87mmol/L/4mmol/L). Peer group survey median CV less than it were as TP (9.3%/10%), AST (16.8%/20%), ALT (19.3%/20%), K (0.39mmol/L/0.5mmol/L), UA (11.5%/17%), Ca (0.87mg/dL1mg/L), TG (17.7%/25%). TEa in 17 items were same one in 14 items with 82.35%. We found out the truth on increasing sigma level due to increased total error allowable, and were sure that the goal of setting total error allowable would affect the evaluation of sigma metrics in the process, if sustaining the same process.