The extension of fuzzy QFD: From product planning to part deployment

Liu, Hao-Tien

doi:10.1016/j.eswa.2009.02.070

Cited by 85 publications

(41 citation statements)

References 29 publications

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“…Theoretical knowledge about QFD and modelling has also increased significantly during the last two decades [2,13,14]. Unfortunately, the diverse papers on QFD as an advanced quality tool for improving the performance of manufacturing systems seems to be scattered in several publishing outlets [15][16][17][18][19]. Also, efforts made in the present decade to understand how such knowledge could be integrated so as to benefit investigators seeking to extend the QFD knowledge frontier seem to have produced studies published years ago, which exclude recent literature on the subject.…”

Section: Qfd Historical Developmentmentioning

confidence: 99%

“…A family of algorithms under this fuzzy c-mean that utilizes entropy or entropy-regularized methods could be applied to the QFD model. It may be important to compare results produced by employing (i) the methodologically different Dunn and Bezdez traditional method (ii) entropy based method and (iii) the original method proposed by Liu [16]. Further exploration of the subsequent phases (process planning and production planning) of QFD would benefit the academic community.…”

mentioning

confidence: 99%

“…Liu [16] The author developed an extended fuzzy quality function deployment approach (E-QFD) with expanded research scope from product planning to part deployment. An advanced method in product planning ensures proper collection of customer requirements with an integrated competitive analysis system.…”

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confidence: 99%

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Manufacturing Quality Function Deployment: Literature Review and Future Trends

Oke

2013

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Abstract.A comprehensive review of the Quality Function Deployment (QFD) literature is made using extensive survey as a methodology. The most important results of the study are: (i) QFD modelling and applications are one-sided; prioritisation of technical attributes only maximise customer satisfaction without considering cost incurred (ii) we are still missing considerable knowledge about neural networks for predicting improvement measures in customer satisfaction (iii) further exploration of the subsequent phases (process planning and production planning) of QFD is needed (iv) more decision support systems are needed to automate QFD (v) feedbacks from customers are not accounted for in current studies.Keywords: QFD, literature review, house of quality, voice of the customer, customer satisfaction, improvement.

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Section: Qfd Historical Developmentmentioning

confidence: 99%

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confidence: 99%

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Manufacturing Quality Function Deployment: Literature Review and Future Trends

Oke

2013

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“…the extraction of customer requirements and the waterfall-type decomposition process of customer requirements [3]. Among them, the importance of customers' requirements is an important input in the building process of house of quality (HoQ).…”

Section: Introductionmentioning

confidence: 99%

Rating the Customer Requirements based on DEMATEL and Entropy

Yang¹,

Ting²,

Liang³

et al. 2013

Int. J. Onl. Eng.

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Abstract-In the configuration procedure of QFD Quality Function Deployment , the importance rating of customer requirements as one of the important input parameters is very significant to the determination of the technical characteristics and even to the optimum decision to the whole quality of house as well as the following step of allocation decision-making. In this paper, firstly the drawbacks of the existing methods of determining the importance rating of customer requirements are analyzed. Then with the influence of customer requirement interdependence and market competitive evaluations sufficiently considered, DEMATEL is introduced to deal with self-correlations between customer requirements and Entropy is used to dispose the market competitive evaluations to modify the fundamental importance ratings of the customer requirements, thus a more objective comprehensive fundamental importance rating of the customer requirements can be gained. Finally, one example is given to verify the effectiveness of the presented method.

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“…Zhang & Chu [11] proposed the fuzzy based linear programming method as an effective solution for the calculations of fuzzy RPNs which resolves the vagueness and uncertainty existing in the evaluating process of the traditional FMEA. Hao Liu [12] proposed an extended fuzzy QFD from product planning to part deployment through a modified fuzzy k-means clustering method with fuzzy inference method for FMEA and α-cut operations to calculate the fuzzy sets in QFD. [7] and Sellapan & Karuppusami [8].…”

Section: Introductionmentioning

confidence: 99%

Failure Effects and Resolution of Modes: A Novel Fmea Treatise for Finalizing Mould Designs in Foundries

Selvan¹,

Jegadheesan²,

Varthanan³

et al. 2013

sajie

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This paper proposes a novel strategy to finalize the mould design of a specific cast component through the failure analysis using case study data of a foundry. Traditional failure mode and effect analysis (FMEA) is one of the effective tools for prioritizing the possible failure modes by calculating the Risk priority Number (RPN) of a process/design. But in foundries, prioritizing the failures through the traditional FMEA produces unmatched results when RPN values are identical during preproduction trials. Hence it is very difficult to finalize moulds design of a specific cast component using traditional FMEA approach. This research paper addresses an alternate FMEA approach named FEAROM (Failure Effects And Resolution of Modes) to resolve the difficulty in finalizing the mould designs. Analytical Hierarchy Process (AHP) is used for validating the results obtained using FEAROM method. The results presented are based on an experimental study carried out for a specific component in a foundry using the sand casting method. It is found that proposed FEAROM model matches well in practice and produces quality castings. OPSOMMINGHierdie artikel stel 'n nuwe strategie voor om die gietvormontwerp van 'n spesifieke gietstuk te finaliseer deur 'n gevallestudie van 'n falingsanalise van 'n smeltery. Tradisionele faal-en-effek-analise is een van die effektiewe hulpmiddels om die moontlike falings te prioritiseer deur die Risiko Prioriteitsnommer van 'n proses of ontwerp te bereken. In smelterye gee die prioritisering van faalings deur die Risiko Prioriteitsnommers onvergelykbare resultate wanneer die Risiko Prioriteitsnommers identies is tydens voorproduksieproewe. Gevolglik is dit baie moeilik om gietvormontwerpe van spesifieke gietstukke te finaliseer deur van die tradisionele faal-en-effek-analise benaderings gebruik te maak. Hierdie artikel stel 'n alternatiewe faal-en-effek-analise benadering voor, genaamd FEAROM ('Failure Effects And Resolution of Modes'), om die probleme met gietvormontwerpfinalisering aan te spreek. 'n Analitiese Hiërargieproses word gebruik om die resultate, met die FEAROM benadering bepaal, te valideer. Die resultate is gegrond op 'n gevallestudie van 'n spesifieke gietstuk in 'n smeltery wat gegiet is met die sandgietmetode. Daar word gevind dat die voorgestelde FEAROM model goed ooreenstem met die praktyk en hoë gehalte gietvorms as gevolg het.

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The extension of fuzzy QFD: From product planning to part deployment

Cited by 85 publications

References 29 publications

Manufacturing Quality Function Deployment: Literature Review and Future Trends

Manufacturing Quality Function Deployment: Literature Review and Future Trends

Rating the Customer Requirements based on DEMATEL and Entropy

Failure Effects and Resolution of Modes: A Novel Fmea Treatise for Finalizing Mould Designs in Foundries

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