An optimal design for process quality improvement: modelling and application

Chen, Jen Ming; Tsou, Jia-Chi

doi:10.1080/09537280310001626197

Cited by 43 publications

(14 citation statements)

References 11 publications

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“…Under the normality-based investment strategy, both the process mean (location parameter) and standard deviation (scale parameter) of the normal distribution obviously depend on the investment level I, according to Chen and Tsou (2003) and Abdul-Kader et al (2010). For the baseline of SND, the location parameter n and scale parameter x are used to replace the process mean and standard deviation of the normal distribution to revise the formulas that were proposed by Chen and Tsou (2003). To simplify the optimization method, the skewness parameter k of the SND is assumed to be free of the quality investment.…”

Section: Quality Investment Modelmentioning

confidence: 99%

“…If the parameters a and b are unknown, they can be estimated by using regression analysis methods. The working process of estimating parameters a and b through the use of the regression method can be found in Chen and Tsou (2003). The expressions of n I and x I are revised formulas of the process mean and standard deviation functions under the investment level I, which were proposed by Chen and Tsou (2003 ; À1 < y < 1:…”

Section: Quality Investment Modelmentioning

confidence: 99%

See 1 more Smart Citation

An integrated approach for the optimization of tolerance design and quality cost

Chiang

Tsai

Lio

et al. 2015

Computers & Industrial Engineering

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Section: Quality Investment Modelmentioning

confidence: 99%

Section: Quality Investment Modelmentioning

confidence: 99%

An integrated approach for the optimization of tolerance design and quality cost

Chiang

Tsai

Lio

et al. 2015

Computers & Industrial Engineering

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“…Addey, 2001;Chen and Tsou, 2003;Leonard and McAdam, 2002;Gingele et al, 2002). This generates constant pressures on companies to change their organizational culture, attitude and behaviour (both at strategic and operational levels) (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Addey, 2001;Chen and Tsou, 2003). In other words, projects related to QMSs implementation should consider from the early stages of their lifecycle the right activities, in the right order and with the right resource involvement, for faster generation of the expected benefits (e.g.…”

Section: Introductionmentioning

confidence: 99%

Vectors of innovation to support quality initiatives in the framework of ISO 9001:2000

Brad

2008

International Journal of Quality &Amp; Reliability Management

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Purpose -The purpose of this paper is to investigate hidden/less tangible dimensions of ISO 9001:2000 standard for better understanding where there are potentials for designing and implementing highly mature quality management systems (QMSs). Design/methodology/approach -Based on advanced quality tools, 80-20 rule and inventive problem-solving algorithms, a novel methodology is developed and applied for quantifying the value weight of ISO 9001:2000 requirements in the equation of business competitiveness, for identifying critical issues, conflicts and barriers within projects dealing with quality management system (QMS) design and implementation, as well as for defining in a systematic way innovative solutions to the challenges arising from these analyses. Research limitations/implications -The set of criteria and their ranks that were used to determine the value weights of the requirements within ISO 9001:2000 have been established only with a focus group of stakeholders; an extensive survey might reveal slight differences of results. Practical implications -ISO 9001:2000 provides only a generic framework for bringing quality to life. Both careful performance planning and process innovation are required to elaborate a mature QMS. Originality/value -The paper reveals a novel tool for quantifying the gap between a given quality management system and ISO 9001:2000 requirements. A comprehensive set of innovative solutions for approaching the design and implementation of a QMS in a competitive way are also provided.

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“…Chang et al [10] consider the trade-off problem between quality and cost for the case when the input quality characteristic is normally distributed. According to the research of Chen et al [11], the management can adjust the investment on prevention and appraisal costs on quality improvement that enhances process capability, reduces product defect rate and, as a result, generates remarkable financial return. Ram Ganeshan et al [12] established a model which determines the optimal levels of inventory, and the production lot-size that minimizes the sum of inventory and quality-related costs.…”

Section: Introductionmentioning

confidence: 99%

A Method to Optimize Geometric Errors of Machine Tool based on SNR Quality Loss Function and Correlation Analysis

et al. 2017

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Abstract. Instead improving the accuracy of machine tool by increasing the precision of key components level blindly in the production process, the method of combination of SNR quality loss function and machine tool geometric error correlation analysis to optimize five-axis machine tool geometric errors will be adopted. Firstly, the homogeneous transformation matrix method will be used to build five-axis machine tool geometric error modeling. Secondly, the SNR quality loss function will be used for cost modeling. And then, machine tool accuracy optimal objective function will be established based on the correlation analysis. Finally, ISIGHT combined with MATLAB will be applied to optimize each error. The results show that this method is reasonable and appropriate to relax the range of tolerance values, so as to reduce the manufacturing cost of machine tools.

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An optimal design for process quality improvement: modelling and application

Cited by 43 publications

References 11 publications

An integrated approach for the optimization of tolerance design and quality cost

An integrated approach for the optimization of tolerance design and quality cost

Vectors of innovation to support quality initiatives in the framework of ISO 9001:2000

A Method to Optimize Geometric Errors of Machine Tool based on SNR Quality Loss Function and Correlation Analysis

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