• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.48-53
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• 2002

품질보증 부분에서 대부분 통계적 수법들은 기지분포를 가정하여 데이터를 분석하고 있다. 예를 들면 공정능력에서는 정규분포를 가정하고 있고, 신뢰성 분석에서는 지수분포, 대수정규분포 또는 와이블분포 등을 가정하고 있다. 만약 이러한 것들이 가정하는 확률분포와 크게 편의될 때 도출된 결론은 그 유효성을 상실하게 된다. 따라서 공정이 정규분포를 하지 않는다면 정규분포에 기초한 공정능력지수의 사용은 부정확한 공정의 정보를 제시하게 된다. 이러한 문제를 해결하기 위하여 그 사례로 소표본일 경우 정규성의 유무를 Lilliefors 검정통계량으로 검정하였다. 그리고 최우추정량(MLE), 수정적률추정량(MME), 및 적률추정량(ME)을 사용하여 모수추정을 하여 그 각각의 경우에 공정능력지수로 평가하였다. 공정능력지수의 평가는 공정이 지수분포를 하는 경우에 적합한 공정능력지수($C_{pe}$)를 제안하고, 이것의 대안으로써 Pearson 시스템, Johnson 시스템 및 Burr 시스템과도 비교평가 하였다.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.377-389
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• 2011

A higher quality level is generally perceived by customers as improved performance by assigning a correspondingly higher satisfaction score. The third generation index $C_{pmk}$ is more powerful than two useful indices $C_p$ and $C_{pk}$ that have been widely used in six sigma industries to assess process performance. In actual manufacturing industries, process capability analysis often entails characterizing or assessing processes or products based on more than one engineering specification or quality characteristic. Since these characteristics are related, it is a risky undertaking to represent the variation of even a univariate characteristic by a single index. Therefore, the desirability of using vector-valued process capability index(PCI) arises quite naturally. In this paper, we consider more powerful vector-valued process capability index $C_{pmk}$ = ($C_{pmkx}$, $C_{pmky}$)$^t$ that consider the univariate process capability index $C_{pmk}$. First, we examine the process capability index $C_{pmk}$ and plug-in estimator $\hat{C}_{pmk}$. In addition, we derive its asymptotic distribution and variance-covariance matrix $V_{pmk}$ for the vector valued process capability index $C_{pmk}$. Under the assumption of bivariate normal distribution, we study asymptotic confidence regions of our vector-valued process capability index $C_{pmk}$ = ($C_{pmkx}$, $C_{pmky}$)$^t$.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.465-475
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• 2013

Higher quality level is generally perceived by customers as improved performance by assigning a correspondingly higher satisfaction score. Usually, the quality level is measured by process capability indices. The index is used to determine whether a production process is capable of producing items within a specified tolerance. Some useful process capability indices $C_p$, $C_{pk}$ and $C_{pm}$ have been widely used in six sigma industries to assess process performance. Most evaluations on process capability indices focus on point estimates, which may result in unreliable assessments of process performance. It is necessary to investigate their asymptotic correlationship among process capability indices $\hat{C}_p$, $\hat{C}_{pk}$ and $\hat{C}_{pm}$. In this paper, we study their asymptotic correlationship for some process capability indices $\hat{C}_p$, $\hat{C}_{pk}$ and $\hat{C}_{pm}$ under the normal process.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.449-461
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• 2009

Higher sigma quality level is generally perceived by customers as improved performance by assigning a correspondingly higher satisfaction score. The process capability indices and the sigma level $Z_{st}$ ave been widely used in six sigma industries to assess process performance. Most evaluations on process capability indices focus on statistical estimation under normal process which may result in unreliable assessments of process performance. In this paper, we consider statistical estimation for bivariate VPCI(Vector-valued Process Capability Index) $C_{pkl}=(C_{pklx},\;C_{pklx})$ under Marshall and Olkin (1967)'s bivariate exponential process. First, we derive some limiting distribution for statistical inference of bivariate VPCI $C_{pkl}$. And we propose two asymptotic normal confidence regions for bivariate VPCI $C_{pkl}$. The proposed method may be very useful under bivariate exponential process. A numerical result based on our proposed method shows to be more reliable.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.459-471
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• 2010

Higher quality level is generally perceived by customers as improved performance by assigning a correspondingly higher satisfaction score. Usually, the quality level is measured by process capability indices. The index is used to determine whether a production process is capable of producing items within a specified tolerance. The third generation index $C_{pmk}$ is more powerful than two useful indices $C_p$ and $C_{pk}$. which have been widely used in six sigma industries to assess process performance. Most evaluations on process capability indices focus on point estimates, which may result in unreliable assessments of process performance. In this paper, we consider better testing procedure on assessing process capability index $C_{pmk}$ for practitioners to use in determining whether a given process is capable. It is easy to use the proposed method for assessing process capability index $C_{pmk}$. Whether a process is clearly normal or nonnormal, our bootstrap testing procedure could be applied effectively without the complexity of calculation. A numerical result based on our proposed method is illustrated.

• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.45-65
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• 1999

공정능력지구 $C_{pk}$는 제조공정이 제품을 제대로 생산하고 있는지를 평가하기 위하여 널리 사용되고 있는 측도이다. 최근까지 공정능력지수 $C_{pk}$에 관한 추정문제들이 만히 연구되었는 바, 대부분의 이러한 연구들은 공정분포가 정규분포임을 가정하였다. 하지만 실제 품질관리 현장의 공정으로부터 얻어지는 특성치들이 정규분포를 따르지 않는 경우가 많이 발생하며, 이를 감지하기가 어려울 수 있다. 따라서 본 논문에서는 공정능력지수 $C_{pk}$에 대한 바람직한 구간추정 방법을 제안하기 위하여 6가지 형태의 비모수적인 붓스트랩 신뢰구간을 설정하고 세 가지 공정분포에 대하여 다양하고 포괄적인 모의실험을 통하여 그 효율성에 관하여 비교연구를 하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.100-102
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• 2022

데이터의 품질을 파악하고 그 기준을 선정하는 것은 해양 항로 표지와 같은 분석에 있어서 중요한 역할을 한다. 본 연구에서는 해양 분야에서 디지털 항로표지 데이터의 품질 진단을 위해 공정능력지수를 이용하여 데이터의 품질을 정량적으로 산출하고 그 결과에 대한 판정 기준을 명확히 하여 데이터에 대한 품질을 판단할 수 있는 척도를 제시하였다.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.301-308
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• 2016

The process capability index is used to determine whether a production process is capable of producing items within a specified tolerance. Some process capability indices $C_p$, $C_{pk}$ and $C_{pm}$ have been of particular interest as useful management tools for tracking process performance. Most evaluations on process capability indices focus on statistical estimation and test of hypothesis. It is necessary to investigate their asymptotic correlationship among basic estimators ${\hat{C}}_p$, ${\hat{C}}_{pk}$ and ${\hat{C}}_{pm}$ of process capability indices $C_p$, $C_{pk}$ and $C_{pm}$. In this paper, we study their asymptotic correlationship for three process capability indices ${\hat{C}}_p$, ${\hat{C}}_{pk}$ and ${\hat{C}}_{pm}$ under bivariate normal distribution BN(${\mu}_x,{\mu}_y,{\sigma}^2_x,{\sigma}^2_y,{\rho}$). With some nonnormal processes, the asymptotic correlation coefficient of any two respective process capability index estimators could be established.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.451-464
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• 2008

Six sigma is the rating that signifies "best in clas", with only 3.4 defects per million units or operations. Higher sigma quality level is generally perceived by customers as improved performance by assigning a correspondingly higher satisfaction score. The process capability indices and the sigma level $Z_{st}$ have been widely used in six sigma industries to assess process performance. Most evaluations on process capability indices focus on point estimates, which may result in unreliable assessments of process performance. In this paper, we consider statistical inference for process capability indices $C_p$, $C_{pk}$ and $C_{pm}$. Also, we study better testing procedure on assessing sigma level $Z_{st}$ and capability index $C_{pm}$, for practitioners to use in determining whether a given process is capable. The proposed method is easy to use and the decision making is more reliable. Whether a process is clearly normal or nonnormal, our bootstrap testing procedure could be applied effectively without the complexity of calculation. A numerical result based on our proposed method is illustrated.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.58
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• pp.81-87
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• 2000

대수정규분포와 와이블분포는 비정규분포를 하는 공정특성에 반영된다. Hahn 과 Shapir(1967)의 수치 예를 통해 이러한 대수정규분포와 와이블분포를 식별하는 검정방법으로 최우비 검정(RML)을 사용하였다. 검정결과 와이블분포를 하는 공정으로 판단되어, 와이블분포 의 모수 추정은 실무에서 그래프와 수식으로도 쉽게 추정할 수 있는 수정적률추정량(MME)를 이용하여 공정 능력을 평가하였다. 공정능력의 평가 측도로 공정능력지수와 불량률(PPM)의 측도와 병행하여 나타내었다. 공정능력 평가의 결과로 와이블분포를 하는 공정을 대수정규분포로 잘못 판단함으로써 발생하는 오류를 2가지 측도로 표현함으로써 올바르게 공정능력을 판단할 수 있도록 종합적인 정보를 제시하였다.