Technical attribute prioritisation in QFD based on cloud model and grey relational analysis

Promptly development of new products can be achieved through quality function deployment (QFD) process, which is critical to companies' survival. Since the multi-criteria decision-making problem involved in QFD, a novel method integrating cloud model and grey relational analysis is put forward...

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Vydáno v:International journal of production research Ročník 58; číslo 19; s. 5751 - 5768
Hlavní autoři: Wang, Xu, Fang, Hong, Song, Wenyan
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 01.10.2020
Taylor & Francis LLC
Témata:
Ambiguity
cloud model theory
Clouds
Content analysis
Correlation analysis
Decision making
Fuzzy sets
grey relational analysis
Multiple criterion
Quality function deployment
Randomness
Sensitivity analysis
Stability analysis
vagueness and randomness
ISSN:0020-7543, 1366-588X
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Shrnutí:Promptly development of new products can be achieved through quality function deployment (QFD) process, which is critical to companies' survival. Since the multi-criteria decision-making problem involved in QFD, a novel method integrating cloud model and grey relational analysis is put forward in this paper. Taking into account the subjectivity and ambiguity in linguistic evaluations, some scholars utilise fuzzy theory, rough theory, interval-valued fuzzy-rough sets and MCDM methods to improve traditional QFD. However, much priori information requirements, inability to handle subjectivity and randomness, and lack of mechanism to overcome small sample size problem are some inevitable drawbacks in these methods. To solve these deficiencies, a hybrid methodology is proposed in this paper, integrating the fortes of cloud model in processing ambiguity and randomness, and the merits of grey relational analysis in overcoming small sample size error as well as revealing the inner correlations. The comparative analysis of different approaches as well as the sensitivity analysis of criteria weights is implemented to prove the stability of the novel method. The results obtained in this paper shows that the proposed method can be a practical tool for improving the efficiency and accuracy of traditional QFD in reality management.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2019.1657246