Modified economic production and raw material model with quality loss for conforming product

Chen, Chung-Ho; Chou, Chao‐Yu; Kan, Chao-Chin

doi:10.1080/21681015.2015.1029548

Cited by 11 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Process parameters settings including process mean and economic specification limits by adopting Taguchi's quality loss function Springer (1951), Kapur and Wang (1987), Kapur (1988), Kapur andCho (1994, 1996), Rahim and Tuffaha (2004), Chen (2005), Kapur (2006a, 2006b), Chen and Lai (2007), Darwish (2009), Darwish and Duffuaa ( 2010), Darwish and Abdulmalek (2012), Darwish et al (2013), El-Ga'aly (2013a, 2013b), and Chen et al (2014aChen et al ( , 2014b) Integrated inventory model including economic manufacturing quantity setting by adopting Taguchi's quality loss function Chen and Lai (2007), Jeang (2010Jeang ( , 2011aJeang ( , 2011b, Jeang and Lin (2014), Chen et al (2014bChen et al ( , 2015aChen et al ( , 2015b EC 34,1…”

Section: Research Topic Referencesmentioning

confidence: 99%

“…Taguchi (1986) proposed the quadratic quality loss function for measuring the product quality. This loss function with screening inspection has been well used in the problems of determining the economic manufacturing quantity, parameters of control charts, parameters of variable sampling inspection plans and specification limits settings (Kapur and Wang, 1987;Kapur, 1988;Kapur andCho, 1994, 1996;Rahim and Tuffaha, 2004;Chen, 2005;Kapur, 2006a, 2006b;Chen and Lai, 2007;Darwish, 2009;Darwish and Duffuaa, 2010;Darwish and Abdulmalek, 2012;Jeang, 2010Jeang, , 2011aJeang, , 2011bEl-Ga'aly, 2013a, 2013b;Darwish et al, 2013;Chen et al, 2014aChen et al, , 2014bJeang and Lin, 2014;Chen et al, 2015aChen et al, , 2015b, etc. Table I summarizes the aforementioned research references.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimum process mean, standard deviation and specification limits settings under the Burr distribution

Chen

Chou

2017

Self Cite

View full text Add to dashboard Cite

Purpose The quality level setting problem determines the optimal process mean, standard deviation and specification limits of product/process characteristic to minimize the expected total cost associated with products. Traditionally, it is assumed that the product/process characteristic is normally distributed. However, this may not be true. This paper aims to explore the quality level setting problem when the probability distribution of the process characteristic deviates from normality. Design/methodology/approach Burr developed a density function that can represent a wide range of normal and non-normal distributions. This can be applied to investigate the effect of non-normality on the studies of statistical quality control, for example, designs of control charts and sampling plans. The quality level setting problem is examined by introducing Burr’s density function as the underlying probability distribution of product/process characteristic such that the effect of non-normality to the determination of optimal process mean, standard deviation and specification limits of product/process characteristic can be studied. The expected total cost associated with products includes the quality loss of conforming products, the rework cost of non-conforming products and the scrap cost of non-conforming products. Findings Numerical results show that the expected total cost associated with products is significantly influenced by the parameter of Burr’s density function, the target value of product/process characteristic, quality loss coefficient, unit rework cost and unit scrap cost. Research limitations/implications The major assumption of the proposed model is that the lower specification limit must be positive for practical applications, which definitely affects the space of feasible solution for the different combinations of process mean and standard deviation. Social implications The proposed model can provide industry/business application for promoting the product/service quality assurance for the customer. Originality/value The authors adopt the Burr distribution to determine the optimum process mean, standard deviation and specification limits under non-normality. To the best of their knowledge, this is a new method for determining the optimum process and product policy, and it can be widely applied.

show abstract

Section: Research Topic Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimum process mean, standard deviation and specification limits settings under the Burr distribution

Chen

Chou

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the following paragraphs, a discussion of some of the most important models in the literature is provided. [39] Selim and Al-Zu'bi (2011) [40] Darwish et al (2013) [41] Duffuaa and El-Ga'aly (2013a) [42] Duffuaa and El-Ga'aly (2013b) [43] Peng and Khasawneh (2014) [44] Chen et al (2015a) [45] Chen et al (2015b) [46] Chen and Kan (2015) [47] Dodd et al (2015) [48] Duffuaa and El-Ga'aly (2015) [49] Raza and Turiac (2016) [50] Raza et al (2016) [51] This paper (General model)…”

Section: Introductionmentioning

confidence: 99%

A Generalized Process Targeting Model and an Application Involving a Production Process with Multiple Products

Abdel-Aal

Selim

2019

Mathematics

View full text Add to dashboard Cite

This paper presents a generalized targeting model that subsumes most known targeting problems. In this paper, a recurrent state is defined as a condition that requires reprocessing or rework. The generalized model can accommodate one or two specifications limits and can be used for the following quality characteristics: The nominal-the-better, the larger-the-better, and the smaller-the-better. This model can be used to find the optimal mean of a quality characteristic, as well as the optimal specification limits. In addition, the paper studies the conditions under which the solution to the proposed model can provide a global solution. The paper shows that, for some of the special cases and under very general conditions, the optimal lower limit should be zero and the optimal upper limit should be infinity. This paper proves that the expected profits improve for the case where only a lower limit on the quality characteristic is used, if a recurrent state is included by adding an optimized upper limit. A special case of the model is used to study the problem of determining a common mean for multiple products, as well as the optimal upper specification limits for each product. A solution procedure for maximizing the expected profits and obtaining the optimal solution is introduced. A numerical example is presented.

show abstract

“…The word target was later interpreted as the target value for quality characteristics, by Taguchi (Phadke, 1995). In fact, Taguchi defined quality through the lack of quality and believed that any deviation of the quality characteristic from its target value causes a loss of quality for both the manufacturer and the customer (Chen et al , 2015; Geramian et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy inference system application for failure analyzing in automobile industry

Geramian

Mehregan

Mokhtarzadeh

et al. 2017

IJQRM

View full text Add to dashboard Cite

Purpose Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry. Design/methodology/approach Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran. Findings Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization. Practical implications This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system. Originality/value This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.

show abstract

Modified economic production and raw material model with quality loss for conforming product

Cited by 11 publications

References 27 publications

Optimum process mean, standard deviation and specification limits settings under the Burr distribution

Optimum process mean, standard deviation and specification limits settings under the Burr distribution

A Generalized Process Targeting Model and an Application Involving a Production Process with Multiple Products

Fuzzy inference system application for failure analyzing in automobile industry

Contact Info

Product

Resources

About