Developing an integrated model for the evaluation and selection of six sigma projects based on ANFIS and fuzzy goal programming

Saghaei, Abbas; Didehkhani, Hosein

doi:10.1016/j.eswa.2010.07.024

Cited by 43 publications

(20 citation statements)

References 16 publications

(13 reference statements)

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“…The minimization of quality variations and production cost reduction are achieved as the benefit, to a local Japanese Steel manufacturing industry using this model [18] .The productivity and quality of steel billets and slabs manufacturing process is improved with the application of a developed genetic algorithm and maximum rate of casting and defect-free products is achieved by controlling the process parameters [19] . The yield of an electronics manufacturing process is improved using enhanced rolled throughput yield(ERTY) method and overall production cost is minimized by controlling scrap to minimum level [20] . In a steel industry, lean manufacturing principles are applied to enhance yield using value stream mapping and simulation of the proposed system is carried out to aid management in decision making [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of steel bar manufacturing process using six sigma

Naeem

Ullah

Tariq

et al. 2016

Chin. J. Mech. Eng.

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Optimization of a manufacturing process results in higher productivity and reduced wastes. Production parameters of a local steel bar manufacturing industry of Pakistan is optimized by using six Sigma-Define, measure, analyze, improve, and controlmethodology. Production data is collected and analyzed. After analysis, experimental design result is used to identify significant factors affecting process performance. The significant factors are controlled to optimized level using two-level factorial design method. A regression model is developed that helps in the estimation of response under multi variable input values. Model is tested, verified, and validated by using industrial data collected at a local steel bar manufacturing industry of Peshawar(Khyber Pakhtunkhwa, Pakistan). The sigma level of the manufacturing process is improved to 4.01 from 3.58. The novelty of the research is the identification of the significant factors along with the optimum levels that affects the process yield, and the methodology to optimize the steel bar manufacturing process.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of steel bar manufacturing process using six sigma

Naeem

Ullah

Tariq

et al. 2016

Chin. J. Mech. Eng.

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“…BAHP kriterler arasında ikili karşılaştırmalardan faydalanmaktadır. İki kriter birbirine göre kıyaslanırken karar vericinin göreceli değerlendirmesi gerekmektedir (Saghaei ve Didehkhani, 2011). Hiyerarşi n adet kriter içeriyor ise n x n boyutunda oluşacak ikili karşılaştırma matrisi için toplam yapılması gereken değerlendirme sayısı şu şekilde hesaplanır (6):…”

Section: Bulanık Analitik Hiyerarşi Prosesiunclassified

Proje Portföyü Seçiminde Çok Boyutlu Sırt Çantası Modeli ve Bulanık Analitik Hiyerarşi Prosesi

Kocamaz¹

2014

eab

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“…Organizations are faced with a myriad of potential projects to choose from, including six sigma projects. Winning six sigma projects are a major factor in the acceptance of six sigma within the organization [5].…”

Section: Introductionmentioning

confidence: 99%

“…COPQ [5] Probability of Implementation [9] contexts [27]. DEA is described as a nonparametric technique that aims at comparing different entities, known as Decision Making Units (DMUs), relying solely on inputs and outputs of the DMUs [28].…”

Section: Introductionmentioning

confidence: 99%

Selecting the Six Sigma Project: A Multi Data Envelopment Analysis Unified Scoring Framework

Arafeh¹

2015

AJOR

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The importance of the project selection phase in any six sigma initiative cannot be emphasized enough. The successfulness of the six sigma initiative is affected by successful project selection. Recently, Data Envelopment Analysis (DEA) has been proposed as a six sigma project selection tool. However, there exist a number of different DEA formulations which may affect the selection process and the wining project being selected. This work initially applies nine different DEA formulations to several case studies and concludes that different DEA formulations select different wining projects. Also in this work, a Multi-DEA Unified Scoring Framework is proposed to overcome this problem. This framework is applied to several case studies and proved to successfully select the six sigma project with the best performance. The framework is also successful in filtering out some of the projects that have "selective" excellent performance, i.e. projects with excellent performance in some of the DEA formulations and worse performance in others. It is also successful in selecting stable projects; these are projects that perform well in the majority of the DEA formulations, even if it has not been selected as a wining project by any of the DEA formulations.

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Developing an integrated model for the evaluation and selection of six sigma projects based on ANFIS and fuzzy goal programming

Cited by 43 publications

References 16 publications

Optimization of steel bar manufacturing process using six sigma

Optimization of steel bar manufacturing process using six sigma

Proje Portföyü Seçiminde Çok Boyutlu Sırt Çantası Modeli ve Bulanık Analitik Hiyerarşi Prosesi

Selecting the Six Sigma Project: A Multi Data Envelopment Analysis Unified Scoring Framework

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