scholarly journals Bayesian Estimation of Generalized Process Capability Indices

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Sudhansu S. Maiti ◽  
Mahendra Saha
Keyword(s):  
Bayesian Estimation ◽  
Process Capability ◽  
Life Time ◽  
Quality Characteristic ◽  
Process Capability Indices ◽  
Data Sets ◽  
Squared Error Loss Function ◽  
Specification Limit ◽  
Capability Indices ◽  
Lower Specification Limit

Process capability indices (PCIs) aim to quantify the capability of a process of quality characteristic (X) to meet some specifications that are related to a measurable characteristic of its produced items. One such quality characteristic is life time of items. The specifications are determined through the lower specification limit (L), the upper specification limit (U), and the target value (T). Maiti et al. (2010) have proposed a generalized process capability index that is the ratio of proportion of specification conformance to proportion of desired conformance. Bayesian estimation of the index has been considered under squared error loss function. Normal, exponential (nonnormal), and Poisson (discrete) processes have been taken into account. Bayes estimates of the index have been compared with the frequentist counterparts. Data sets have been analyzed.

UNIVARIATE PROCESS CAPABILITY INDICES FOR UNILATERAL SPECIFICATION REGION — A REVIEW AND SOME MODIFICATIONS

2012 ◽  
Vol 19 (04) ◽  
pp. 1250020 ◽  
Author(s):  
MOUTUSHI CHATTERJEE ◽  
ASHIS KUMAR CHAKRABORTY
Keyword(s):  
Manufacturing Industry ◽  
Process Capability ◽  
Extensive Study ◽  
Quality Characteristic ◽  
Process Capability Indices ◽  
Practical Application ◽  
Specification Limit ◽  
Capability Indices ◽  
Lower Specification Limit ◽  
Upper Specification Limit

In a manufacturing industry, often the quality characteristic under study is found to have only one of the two specification limits viz., upper specification limit (USL) or lower specification limit (LSL). In such cases the process capability indices (PCI) designed for bilateral specifications become inappropriate. However, in the literature only a few indices are available to address this problem. In the present paper, we have made an extensive study of the PCI's for unilateral specifications with a brief discussion of their possible fields of applications and drawbacks, if any. We have also proposed a logical formulation of a parameter of one of these indices which reduces the subjectivity of the index and hence makes it more suitable for practical application. An example, with the computed values of the various PCI's, is discussed to make a comparative study of the performance and inter-relationship between these PCI's. We have concluded the paper with a discussion on the future scope of study in this field.

scholarly journals Performance Evaluation Using Multivariate Non-Normal Process Capability

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 833 ◽  
Author(s):  
Alatefi ◽  
Ahmad ◽  
Alkahtani
Keyword(s):  
Manufacturing Industry ◽  
Process Capability ◽  
Quality Characteristic ◽  
Process Capability Indices ◽  
Manufacturing Processes ◽  
Capability Indices ◽  
Skewness Reduction ◽  
Quality Of Products

Process capability indices (PCIs) have always been used to improve the quality of products and services. Traditional PCIs are based on the assumption that the data obtained from the quality characteristic (QC) under consideration are normally distributed. However, most data on manufacturing processes violate this assumption. Furthermore, the products and services of the manufacturing industry usually have more than one QC; these QCs are functionally correlated and, thus, should be evaluated together to evaluate the overall quality of a product. This study investigates and extends the existing multivariate non-normal PCIs. First, a multivariate non-normal PCI model from the literature is modeled and validated. An algorithm to generate non-normal multivariate data with the desired correlations is also modeled. Then, this model is extended using two different approaches that depend on the well-known Box–Cox and Johnson transformations. The skewness reduction is further improved by applying heuristics algorithms. These two approaches outperform the investigated model from the literature because they can provide more precise results regardless of the skewness type. The comparison is made based on the generated data and a case study from the literature.

A note on Bayesian estimation of process capability indices

1999 ◽  
Vol 45 (3) ◽  
pp. 215-224 ◽  
Author(s):  
Jyh-Jen Horng Shiau ◽  
Hui-Nien Hung ◽  
Chun-Ta Chiang
Keyword(s):  
Bayesian Estimation ◽  
Process Capability ◽  
Process Capability Indices ◽  
Capability Indices

Measuring the Capability for One Process with Spherical Tolerance

2013 ◽  
Vol 284-287 ◽  
pp. 3717-3726
Author(s):  
Liang Chyau Sheu ◽  
Chi Huang Yeh ◽  
Ching Ho Yen ◽  
Chia Hao Chang
Keyword(s):  
Manufacturing Industry ◽  
Process Capability ◽  
Quality Characteristic ◽  
Process Capability Indices ◽  
Measuring Process ◽  
Process Yield ◽  
Effective Measure ◽  
Capability Indices ◽  
Process Loss ◽  
Loss Index

Process capability indices, Cp, Cpk, and Cpm, are well-known indices used widely in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications, but limited to cases with single engineering specification. Therefore, for processes where the quality characteristic is the location relative to a specific location, they can not provide an effective measure. In this paper, we propose a process loss index LG to evaluate the process capability for this issue. Based on the index, we provide the corresponding transformation for production yield. In addition, we tabulate some critical values for process loss index LG to judge if the process capability is capable. The proposed method is useful for the practitioners to measure the process loss and determine whether a process meets the present process yield requirement.

New multivariate process capability indices for measuring the performance of multivariate processes subject to non-normal distributions

2015 ◽  
Vol 33 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Jeh-Nan Pan ◽  
Chung-I Li ◽  
Wei-Chen Shih
Keyword(s):  
Process Capability ◽  
Process Capability Indices ◽  
Process Data ◽  
Multivariate Process ◽  
Multivariate Processes ◽  
Content Type ◽  
Normal Distributions ◽  
Normality Assumption ◽  
Capability Indices ◽  
Multivariate Manufacturing

Purpose – In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions. Design/methodology/approach – In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC p , RNMC pm and RNMC pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC p and NMC pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case. Findings – A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices. Practical implications – Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system. Originality/value – Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.

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