Rough set-based approach for modeling relationship measures in product planning

  • Authors:

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

  • Affiliations:

  • School of Traffic, Transportation, and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China and Natural Key Laboratory of Integrated Automation of Process ...;Natural Key Laboratory of Integrated Automation of Process Industry in Northeastern University, School of Information Science and Engineering, Northeastern University, PO 135, Shenyang, Liaoning 1 ...;Department of System Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, People's Republic of China;Natural Key Laboratory of Integrated Automation of Process Industry in Northeastern University, School of Information Science and Engineering, Northeastern University, PO 135, Shenyang, Liaoning 1 ...;School of Economics and Management, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, People's Republic of China

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

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Abstract

Quality function deployment (QFD) provides a planning and problem-solving methodology that is widely renowned for translating customer requirements (CRs) into engineering characteristics (ECs) for new product development. As the first phase of QFD, product planning house of quality (PPHOQ) plays a very important role in this process. The degrees and directions of the relationship measures between CRs and ECs have serious effects on the special planning of ECs, modeling the relationship measures is an important step in constructing PPHOQ. The current paper presents a rough set (RS)-based approach for modeling relationship measures by determining the knowledge and experience of the QFD team, aided by the introduction of the type factor of a relationship used to express the effects of the relationship types. A study of general cases is used to demonstrate the performances and limitations of the proposed RS-based approach. The results show that the novel approach effectively determines the relative knowledge of the QFD team and facilitates decision-making in new product development.