Fuzzy hierarchical analysis: the Lambda-Max method
Fuzzy Sets and Systems - Special issue on clustering and learning
A methodology of determining aggregated importance of engineering characteristics in QFD
Computers and Industrial Engineering
Telecommunications Policy
A systematic methodology to deal with the dynamics of customer needs in Quality Function Deployment
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Estimating the quality of process yield by fuzzy sets and systems
Expert Systems with Applications: An International Journal
An integrated linguistic-based group decision-making approach for quality function deployment
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
| Hi-index | 12.06 |
By focusing on listening to the customers, quality function deployment (QFD) has been a successful analysis tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have attempted to apply the fuzzy set theory to QFD and have developed various fuzzy QFD approaches. Their models usually concentrate on product planning, the first phase of QFD. The subsequent phases (part deployment, process planning, and production planning) of QFD are seldom addressed. Moreover, their models often use algebraic operations of fuzzy numbers to calculate the fuzzy sets in QFD. Biased results are easily produced after several multiplicative or divisional operations. Aiming to solve these two issues, the objective of this study is to develop an extended fuzzy quality function deployment approach (E-QFD) which expands the research scope, from product planning to part deployment. In product planning, a more advanced method for collecting customer requirements is developed while the competitive analysis is also considered. In part deployment, the original part deployment table is enhanced by including the importance of part characteristics (PCs) and the bottleneck level of PCs. A modified fuzzy k-means clustering method is proposed to classify various bottleneck (or importance) groups of PCs. The failure mode and effects analysis (FMEA) is conducted for the high bottleneck (or high importance) group of PCs through the fuzzy inference approach. Moreover, E-QFD employs a more precise method, @a-cut operations, to calculate the fuzzy sets in QFD instead of algebraic operations of fuzzy numbers. Finally, a case study is given to explain the analysis process of the proposed method.