The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems
The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems
A new approach to quality function deployment planning with financial consideration
Computers and Operations Research
A structural component-based approach for designing product family
Computers in Industry
Estimating the functional relationships for quality function deployment under uncertainties
Fuzzy Sets and Systems
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
A hybrid approach to concept selection through fuzzy analytic network process
Computers and Industrial Engineering
The extension of fuzzy QFD: From product planning to part deployment
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Integrating preference analysis and balanced scorecard to product planning house of quality
Computers and Industrial Engineering
Determining the final priority ratings of customer requirements in product planning by MDBM and BSC
Expert Systems with Applications: An International Journal
Green supply implementation based on fuzzy QFD: an application in GPLM system
WSEAS TRANSACTIONS on SYSTEMS
Analyzing the ranking method for L-R fuzzy numbers based on deviation degree
Computers and Industrial Engineering
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Quality function deployment (QFD) is a planning and problem-solving tool that is gaining acceptance for translating customer requirements (CRs) into engineering characteristics (ECs) of a product. Deriving the importance of ECs is a crucial step of applying QFD. However, the inherent fuzziness in QFD presents a special challenge to effectively evaluate the importance of ECs and correlation among them. Furthermore, degree of impact of an engineering characteristic (EC) on the other ECs also reflects the importance of the ECs. In previous studies, those impacts were neglected or simply represented using a linear combination in determining the importance of ECs. To address this issue, in this paper, a new methodology of determining aggregated importance of ECs is presented which involves the consideration of conventional meaning of importance of ECs as well as the impacts of an EC on other ECs. In the proposed methodology, fuzzy relation measures between CRs and ECs as well as fuzzy correlation measures among ECs are determined based on fuzzy expert systems approach. These two types of measures are then used to determine the aggregated importance of ECs. An example of design of a digital camera is used to illustrate the proposed methodology.