A rough set approach for estimating correlation measures in quality function deployment

  • Authors:

  • Yan-Lai Li;Jia-Fu Tang;Kwai-Sang Chin;Yi Han;Xing-Gang Luo

  • Affiliations:

  • School of Traffic, Transportation, and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China and Key Lab of Integrated Automation of Process Industry of the ...;Key Lab of Integrated Automation of Process Industry of the Ministry of Education in Northeastern University, School of Information Science and Engineering, Northeastern University, P.O. Box 135, ...;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, People's Republic of China;College of Economics and Management, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, People's Republic of China;Key Lab of Integrated Automation of Process Industry of the Ministry of Education in Northeastern University, School of Information Science and Engineering, Northeastern University, P.O. Box 135, ...

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

Quality function deployment (QFD) is a planning and problem-solving methodology used to translate customer requirements (CRs) into engineering characteristics (ECs) in the course of new product development (NPD). Estimating the correlation measures among ECs is a crucial step in the product planning house of quality (PPHOQ) construction process because these measures seriously affect the planning of development efforts. This study presents a rough set-based approach used to estimate the correlation measures by revealing the knowledge of a QFD team. The approach involves introducing the category factor of a correlation to express the influences of the correlation categories on the corresponding correlation measures. A case study of a two-cylinder washing machine is used to illustrate the proposed approach. The result shows that the novel approach is effective in revealing the related knowledge of the QFD team and facilitating NPD decision making.