Modified weighted standard deviation index for adequately interpreting a supplier’s lognormal process capability

2018 ◽  
Vol 233 (3) ◽  
pp. 999-1008 ◽  
Author(s):  
Mou-Yuan Liao ◽  
WL Pearn
Keyword(s):  
Standard Deviation ◽  
Coverage Probability ◽  
Process Capability ◽  
Process Capability Index ◽  
Process Performance ◽  
Normal Process ◽  
Efficient Tool ◽  
Process Yield ◽  
Capability Index ◽  
Normality Assumption

The process capability index has become an efficient tool for measuring a supplier’s process performance. [Formula: see text] is one popularly used index for assessing non-normal process capability when the process violates the normality assumption. Unfortunately, this index cannot accurately reflect the process yield, so it may produce a serious result if the practitioner compares the calculated [Formula: see text] value with the capability requirement to determine whether the process meets that requirement. Hence, this study modifies [Formula: see text] to provide an adequate measure of lognormal process capability. In addition, an estimator of this modified index is also provided. Simulations show that the bias of this estimator is slight, and the coverage probability of capability testing is close to the nominal confidence. This means that our proposed method is adaptable for use.

scholarly journals Confidence interval for the process capability index based on the bootstrap-t confidence interval for the standard deviation

2014 ◽  
Vol 11 (2) ◽  
Author(s):  
Wararit Panichkitkosolkul
Keyword(s):  
Standard Deviation ◽  
Confidence Interval ◽  
Coverage Probability ◽  
Process Capability ◽  
Process Capability Index ◽  
Skewed Distributions ◽  
Monte Carlo Simulation Study ◽  
Nominal Level ◽  
R Language ◽  
Capability Index

This paper proposes a confidence interval for the process capability index based on the bootstrap-t confidence interval for the standard deviation. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence interval with the existing confidence interval based on the confidence interval for the standard deviation. Simulation results show that the proposed confidence interval performs well in terms of coverage probability in case of more skewed distributions. On the other hand, the existing confidence interval has a coverage probability close to the nominal level for symmetrical or less skewed distributions. The code to estimate the confidence interval in R language is provided.

scholarly journals The Capability Index when Some Assumptions are not Satisfied: Analysis and Empirical Comparisons

2020 ◽  
Vol 34 (3) ◽  
pp. 639
Author(s):  
Pablo José Moya Fernández ◽  
Juan Francisco Muñoz Rosas ◽  
Encarnación Álvarez Verdejo
Keyword(s):  
Standard Deviation ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Process Capability ◽  
Process Capability Index ◽  
Sample Standard Deviation ◽  
Quality Requirements ◽  
Capability Index ◽  
Usual Estimator ◽  
Process Standard Deviation

The process capability index (PCI) evaluates the ability of a process to produce items with certain quality requirements. The PCI depends on the process standard deviation, which is usually unknown and estimated by using the sample standard deviation. The construction of confidence intervals for the PCI is also an important topic. The usual estimator of the PCI and its corresponding confidence interval are based on various assumptions, such as normality, the fact that the process is under control, or samples selected from infinite populations. The main aim of this paper is to investigate the empirical properties of estimators of the PCI, and analyze numerically the effect on confidence intervals when such assumptions are not satisfied, since these situations may arise in practice.

scholarly journals Evaluation of Modified Non-Normal Process Capability Index and Its Bootstrap Confidence Intervals

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12135-12142 ◽  
Author(s):  
Muhammad Kashif ◽  
Muhammad Aslam ◽  
Ali Hussein Al-Marshadi ◽  
Chi-Hyuck Jun ◽  
Muhammad Imran Khan
Keyword(s):  
Confidence Intervals ◽  
Process Capability ◽  
Process Capability Index ◽  
Normal Process ◽  
Bootstrap Confidence Intervals ◽  
Capability Index

The Nonparametric Confidence Interval for the Process Capability Index C*pmk

2013 ◽  
Vol 404 ◽  
pp. 520-525 ◽  
Author(s):  
Shu Fei Wu
Keyword(s):  
Confidence Interval ◽  
Coverage Probability ◽  
Process Capability ◽  
Process Capability Index ◽  
The Other ◽  
Interval Estimate ◽  
Jackknife Method ◽  
Capability Index ◽  
Simulation Results ◽  
Bootstrap Interval

The process capability index which is a generalization of is defined by the use of the idea of Chan et al. [ for asymmetric tolerance. In this paper, we proposed a Jackknife confidence interval for and compare its coverage probability with the other three Efron and Tibshiranis [ bootstrap interval estimate techniques. The simulation results show that the Jackknife method has higher chance of reaching the nominal confidence coefficient for all cases considered in this paper. Therefore this method is recommended for used. One numerical example to demonstrate the construction of confidence interval for the process capability index is also given in this paper.

scholarly journals Performance of a Robust Confidence Interval for the Process Capability Index Cp based on the Modified Trimmed Standard Deviation Under Non-Normal Distributions

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Standard Deviation ◽  
Confidence Interval ◽  
Process Capability ◽  
Process Capability Index ◽  
Robust Estimator ◽  
Average Width ◽  
Normal Distributions ◽  
Capability Index ◽  
Simulation Results ◽  
Interval Estimator

In this paper, a robust interval estimator for the classical process capability index (Cp) based on the modified trimmed standard deviation (MTSD = ST ∗ ) is considered under both normal and non-normal distributions. The performance of the newly proposed process capability index interval estimator over the existing method is compared using a simulation study. As a performance criterion, we consider both simulated coverage probability and average width. Simulation results evident that the proposed confidence interval based on the robust estimator performed well for most of cases. For illustration purposes, two real-life data from industry are analyzed which supported our simulation results to some extent. As a result, the proposed method can be recommend to be used by the practitioners in various fields of industry, engineering and physical sciences.

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