On Generalizing Process Capability Indices

Maiti, Sudhansu S.; Saha, Mahendra; Nanda, Asok K.

doi:10.1080/16843703.2010.11673233

Cited by 49 publications

(38 citation statements)

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“…By suitable analysis of the PCIs, a production department can trace and improve a poor process to enhance the quality level and satisfy customer requirements (the reader can see Castagliola & Castellanos, 2005;Kotz & Lovelace, 1998;Maiti, Saha, & Nanda, 2010;Pearn & Kotz, 2006;Pearn & Wu, 2013;Pearn, Wu, & Wang, 2004;Spiring, Leung, Cheng, & Yeung, 2003;V€ annman, 2006;Wu, Pearn, & Kotz, 2009;Wu, Liao, & Yang, 2013 for more information about the theoretical issues and practical applications of PCIs).…”

Section: Introductionmentioning

confidence: 98%

Process capability control chart for non-normal data – evidence of on-going capability assessment

Liao

2016

Quality Technology & Quantitative Management

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Process capability indices are widely used for various quality assurance activities in many high-tech industries. By a suitable analysis of an index, a production department can trace and improve a poor process to enhance the quality level and satisfy customer requirements. Recently, some practical process capability control charts were developed for illustrating the continuous assessments of normal process capability. These charts contain information regarding the ability of the process to meet or exceed customer requirement over the life of a process. Monitoring the behaviour of the process's ability to meet customer requirement can provide managers with feedback and evidence of the process's ability in terms they understand. However, process distribution is often ab normal. Therefore, this study incorporates bootstrap procedure to construct capability control chart for non-normal processes. The simulations show that the empirical coverage probability of this chart is close to the desired coverage probability as the number of subgroup is large enough.

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Section: Introductionmentioning

confidence: 98%

Process capability control chart for non-normal data – evidence of on-going capability assessment

Liao

2016

Quality Technology & Quantitative Management

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“…Pearn, Kotz, and Johnson (1992) introduced another process capability index, C pmk , which considers the difference between process mean and specification center. Recently, some researchers, for example Grau (2010Grau ( , 2012, Maiti, Saha, and Nanda (2010), and Pearn and Wu (2013), proposed the generalization and application of the aforementioned capability indices. Kargar, Mashinchi, and Parchami (2013) adopted a Bayesian approach based on subsamples to check process capability.…”

Section: Introductionmentioning

confidence: 99%

Tolerance design based on specified process capability value

Chen

Kan

2014

International Journal of Management Science and Engineering Man

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In this paper, the authors propose a modification of the model, presented by Kapur and Wang in 1987, for obtaining optimal product specification limits (tolerance) with minimum expected total cost per unit of product under the satisfaction of specified process capability values, i.e. assuming the product target value is equal to the specification center, that the value of the specified process capability index, C p or C pk , is given, and that 100% inspection is executed before the product is shipped to the customer. The asymmetric quadratic quality loss function presented by Taguchi in 1986 is applied for evaluating product quality. The mathematical optimization model and its solution procedure are formulated. Finally, a numerical example and a sensitivity analysis of the parameters are provided for illustration.

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“…Besides, there are numerous PCIs, and their analysis for different conditions are available in the literature for both normal and non-normal process quality characteristic(s) (see Clements, 6 Gunter, 5 Rodriguez, 7 Pearn & Chen, 8 Yeh & Bhattarchya, 9 Shore, 10 Polansky, 11 and Perakis & Xekalaki 12 ). In the recent past, Maiti et al 13 developed a GPCI C py that is directly or indirectly related with most of the PCIs defined in the literature. Besides, it covers normal as well as non-normal and continuous as well as discrete random variables and is defined as:…”

Section: Introductionmentioning

confidence: 99%

Classical and Bayesian inference ofC_pyfor generalized Lindley distributed quality characteristic

Saha

Dey²,

Yadav

et al. 2019

Quality & Reliability Eng

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The process capability index (PCI) is a quality control–related statistic mostly used in the manufacturing industry, which is used to assess the capability of some monitored process. It is of great significance to quality control engineers as it quantifies the relation between the actual performance of the process and the preset specifications of the product. Most of the traditional PCIs performed well when process follows the normal behaviour. However, using these traditional indices to evaluate a non‐normally distributed process often leads to inaccurate results. In this article, we consider a new PCI, Cpy, suggested by Maiti et al, which can be used for normal as well as non‐normal random variables. This article addresses the different methods of estimation of the PCI Cpy from both frequentist and Bayesian view points of generalized Lindley distribution suggested by Nadarajah et al. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, least square and weighted least square estimators, and maximum product of spacings estimators. Next, we consider Bayes estimation under squared error loss function using gamma priors for both shape and scale parameters for the considered model. We use Tierney and Kadane's method as well as Markov Chain Monte Carlo procedure to compute approximate Bayes estimates. Besides, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with highest posterior density credible intervals. Furthermore, Monte Carlo simulation study has been carried out to compare the performances of the classical and the Bayes estimates of Cpy in terms of mean squared errors along with the average width and coverage probabilities. Finally, two real data sets have been analysed for illustrative purposes.

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On Generalizing Process Capability Indices

Cited by 49 publications

References 1 publication

Process capability control chart for non-normal data – evidence of on-going capability assessment

Process capability control chart for non-normal data – evidence of on-going capability assessment

Tolerance design based on specified process capability value

Classical and Bayesian inference ofC_pyfor generalized Lindley distributed quality characteristic

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On Generalizing Process Capability Indices

Cited by 49 publications

References 1 publication

Process capability control chart for non-normal data – evidence of on-going capability assessment

Process capability control chart for non-normal data – evidence of on-going capability assessment

Tolerance design based on specified process capability value

Classical and Bayesian inference ofCpyfor generalized Lindley distributed quality characteristic

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Product

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Classical and Bayesian inference ofC_pyfor generalized Lindley distributed quality characteristic