Name
Abstract | ||
|---|---|---|
Quality function deployment (QFD) is a planning and problem-solving methodology that is widely used for translating customer requirements (CRs) into engineering characteristics (ECs) of a product. Acquiring ECs in product planning house of quality (PPHOQ) is a crucial step of QFD methodology. In previous studies, methods for acquiring ECs are qualitative and subjective. In this paper, a novel quantitative methodology for acquiring ECs in PPHOQ is presented. In the proposed methodology, a hierarchical framework for generating the refined set of EC candidates from the EC candidates obtained by brainstorming is established. A concept of a general relationship between a CR and an EC is introduced, and then a novel approach for identifying the general relationship measures with the help of intelligent characteristics of rough set is proposed. Another concept of the intension threshold of the general relationship corresponding to a CR is introduced, and then a set-operation-based method of acquiring ECs used in PPHOQ is presented. An example of a two-cylinder washing machine is used to demonstrate the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2009.05.006 | Expert Syst. Appl. |
Keywords | Field | DocType |
engineering characteristics,general relationship,general relationship measure,novel approach,novel quantitative methodology,proposed methodology,acquiring ecs,product planning house,quality function deployment (qfd),house of quality,rough set,engineering characteristic,ec candidate,problem-solving methodology,qfd methodology,quality function deployment | Customer requirements,Data mining,Brainstorming,Industrial engineering,House of Quality,Quality function deployment,Computer science,Intension,Rough set,Product planning | Journal |
Volume | Issue | ISSN |
37 | 1 | Expert Systems With Applications |
Citations | PageRank | References |
8 | 0.49 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanlai Li | 1 | 95 | 9.06 |
| Jiafu Tang | 2 | 541 | 49.29 |
| Xing-Gang Luo | 3 | 138 | 14.85 |
| Jianming Yao | 4 | 14 | 2.07 |
| Jie Xu | 5 | 41 | 1.72 |