Quantification for the importance degree of engineering characteristics with a multi-level hierarchical structure in QFD

Abstract

Quantification for the importance degree of engineering characteristics (ECs) is an essential problem in quality function deployment. In real-world scenario, it is sometimes difficult to directly evaluate the correlation degree between ECs and customer requirements (CRs) as ECs are too abstract. Thus, the target ECs have to be further decomposed into several more detailed basic ECs and organised by a multi-level hierarchical structure. The paper investigates the quantification problem for the importance degree of such target ECs and tackles two critical issues. The first issue is how to deal with the uncertainties including fuzziness and incompleteness involved during the evaluation process. A fuzzy evidential reasoning algorithm-based approach is proposed to tackle this issue and derive the correlation degree between each of the basic ECs and the whole CRs. The second issue is how to deal with the interactions among the basic ECs decomposed from the same target EC during the aggregation process. A λ-fuzzy measure and fuzzy discrete Choquet integral-based approach is proposed to tackle this issue and aggregate these basic ECs. Final importance degree of the target ECs can then be obtained. At the end of this paper, a case study is presented to verify the feasibility and effectiveness of the method we propose.

Keywords:

• quality function deployment
• multi-level hierarchical structure
• fuzzy evidential reasoning
• fuzzy measure
• Choquet integral

Acknowledgement

The authors would like to express their sincere thanks to the Editor-in-Chief, the Associate Editor and the anonymous reviewers for their constructive comments to improve the quality of the paper.

Disclosure statement

The authors declare that there is no conflict of interests.

Additional information

Funding

The work is supported by National Natural Science Foundation of China [grant number 51490663], [grant number 51475417]; National Basic Research Programme of China (973 Programme) [grant number 2011CB706503]; Science Fund for Creative Research Groups of National Natural Science Foundation of China [grant number 51221004].