Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

• Published in: International journal of production research Vol. 43; no. 17; pp. 3583 - 3604
• Main Authors: Chen, Y., Fung, R. Y. K., Tang, J.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis Group 01.09.2005; Washington, DC Taylor & Francis; Taylor & Francis LLC
• Subjects: Applied sciences; Customer requirements; Design engineering; Engineering; Engineering characteristics; Exact sciences and technology; Expected values; Fuzzy expected value operator; Fuzzy sets; Operational research and scientific management; Operational research. Management science; Operations research; Product design; Product development; Quality function deployment; Reliability theory. Replacement problems; Studies; Targets setting; Total quality; Engineering characteristics; Fuzzy expected value operator; Quality function deployment; Product development; Fuzzy set theory; Product design; Planning; Customer requirements; Targets setting; Stochastic programming
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207540500032046

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.