New capability indices for evaluating the performance of multivariate manufacturing processes

2009 ◽  
Vol 26 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Jeh-Nan Pan ◽  
Chun-Yi Lee
Keyword(s):  
Manufacturing Processes ◽  
Capability Indices ◽  
Multivariate Manufacturing

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.

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.

scholarly journals Process Capability Indices as Fuzzy Numbers

2016 ◽  
Vol 34 (4) ◽  
Author(s):  
Abbas Parchami ◽  
Mashaallah Mashinchi ◽  
Ali Reza Yavari ◽  
Hamid Reza Maleki
Keyword(s):  
Fuzzy Numbers ◽  
Process Capability ◽  
Process Capability Indices ◽  
Manufacturing Processes ◽  
Membership Functions ◽  
Specification Limits ◽  
Fuzzy Process ◽  
Capability Indices ◽  
Fuzzy Specification Limits ◽  
Fuzzy Process Capability

Most of the traditional methods for assessing the capability of manufacturing processes are dealing with crisp quality. In this paper we discuss the fuzzy quality and introduce fuzzy process capability indices, where instead of precise quality we have two membership functions for specification limits. These indices are necessary when the specification limits are fuzzy and they are helpful for comparing manufacturing processes with fuzzy specification limits. Some interesting relations among the introduced indices are obtained. Numerical examples are given to clarify the method.

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