A mulvariate measure of process Capability

Chan, Lai K.; Cheng, Smiley W.; Spiring, Fred

doi:10.1080/02286203.1991.11760112

Cited by 102 publications

(91 citation statements)

References 6 publications

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“…Our simulation study compares the performance of our two multivariate process capability indices with the three multivariate process capability indices proposed by Chan et al 2 , Hubele et al 3 and Taam et al 4 . The specifications and the target values for a hypothetical bivariate process are listed in Table I.…”

Section: Comparison Of Various Multivariate Process Capability Indicesmentioning

confidence: 99%

“…To establish performance measures for evaluating the capability of a multivariate manufacturing process, several multivariate process capability indices have been developed based on the further extensions from the univariate domain Unfortunately, the engineering tolerance region in a multivariate case is not simply represented by end points on a number line.The actual process region is not a straightforward function of the process standard deviation (Chan et al 2 ). Thus, one of the major problems in developing multivariate process capability indices is to establish an engineering tolerance region and a process region.…”

Section: Introductionmentioning

confidence: 99%

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New capability indices for evaluating the performance of multivariate manufacturing processes

Pan

Lee

2009

Quality & Reliability Eng

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Generally, an industrial product has more than one quality characteristic. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, several multivariate process capability indices have been developed in the past few years. Among them, Taam's MC p and MC pm indices have the drawback of overestimation and Hubele's three-component capability vector lacks simplicity in practice. In this article, taking the correlation among multiple quality characteristics into account, we develop two novel indices; NMC p and NMC pm . Using two numerical examples we demonstrate that the true performance of multivariate processes are accurately reflected in our NMC p and NMC pm indices and in their associated interval estimates. Finally, simulation results show that our indices outperform both those of Taam and Hubele.

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Section: Comparison Of Various Multivariate Process Capability Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New capability indices for evaluating the performance of multivariate manufacturing processes

Pan

Lee

2009

Quality & Reliability Eng

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“…In the literature, supply chain management and quality have been approached upon different perspectives, such as product quality and service quality [9][10][11][12][13], reprocessing and reworking in inventory models [14], quality costs [15], quality as a strategy [16][17][18], selection of suppliers [19], and the integration of capability indexes for the evaluation of products and performance of organizations [20][21][22][23][24][25].…”

Section: Quality and Process Capability Index (Cp And Cpk) In The Supplymentioning

confidence: 99%

Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four‐Echelon Supply Chain

et al. 2018

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This paper develops a location-allocation model to optimize a four-echelon supply chain network, addressing manufacturing and distribution centers location, supplier selection and flow allocation for raw materials from suppliers to manufacturers, and finished products for end customers, while searching for system profit maximization. A fractional-factorial design of experiments is performed to analyze the effects of capacity, quality, delivery time, and interest rate on profit and system performance. The model is formulated as a mixed-integer linear programming problem and solved by using well-known commercial software. The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems. The application of the proposed model to a case study shows that this combination of techniques yields satisfying results in terms of both its behavior and the obtained managerial insights. An ANOVA analysis is executed to quantify the effects of each factor and their interactions. In the analyzed case study, the transportation cost is the most relevant cost component, and the most relevant opportunity for profit improvement is found in the factor of quality. The proposed combination of methods can be adapted to different problems and industries.

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“…Chan, Cheng and Spiring [52] proposed using an ellipsoidal shape to represent the SR. Taam, Subbaiah and Liddy [53] improved the approach presented in Chan, Cheng and Spiring [52] and described the modied SR as the biggest ellipsoid (parallel to the Cartesians axes) that can be tted into the original SR and which is centered at the target value (see gure 11). We can see that the modied SR is tangential to the original rectangular one.…”

Section: Relation Between Original Pr and Modied Srmentioning

confidence: 99%

“…Here, and Spiring [52]. and in Braun [54] a modied SR, which is also inclined but it is now centered in the target value (which cannot be the center of the SR) is proposed.…”

Section: Relation Between Original Pr and Modied Srmentioning

confidence: 99%

A review of univariate and multivariate process capability indices

de-Felipe

Benedito

2017

Int J Adv Manuf Technol

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This paper oers a review of univariate and multivariate Process Capability Indices (PCIs). PCIs are used in the industry to quantify how well a process can meet customer requirements. Univariate PCIs describe the capability of one single product characteristic. Multivariate PCIs deal with the multivariate case in which the measures of all multiple product characteristics must be within specication limits to be conforming. When analyzing the capability of processes, decision makers of the industry may choose one PCI among all the PCIs existing in the literature, depending on dierent capability criteria. The aim of the review is to describe, cluster and discuss univariate and multivariate PCIs. This review may help researchers and decision makers to identify univariate and multivariate PCIs that can be used when monitoring univariate and multivariate production processes. On the one hand, the authors of this article suggest using PCIs obtained through the alternative denition for the C pk index when analyzing the capability of production processes, in which the estimation of the proportion of nonconforming parts is rated as crucial. On the other hand, all other multivariate PCIs presented in the literature can be applicable in capability analysis of production processes in which a direct relation to the proportion of nonconforming parts is not needed.

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A mulvariate measure of process Capability

Cited by 102 publications

References 6 publications

New capability indices for evaluating the performance of multivariate manufacturing processes

New capability indices for evaluating the performance of multivariate manufacturing processes

Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four‐Echelon Supply Chain

A review of univariate and multivariate process capability indices

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