Conference article

Rough Fuzzy Hybridisation in Quality Function Deployment

Lian-Yin Zhai
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore

Li-Pheng Khoo
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore

Zhao-Wei Zhong
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore

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Published in: 10th QMOD Conference. Quality Management and Organiqatinal Development. Our Dreams of Excellence; 18-20 June; 2007 in Helsingborg; Sweden

Linköping Electronic Conference Proceedings 26:71, p.

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Published: 2008-02-15


ISSN: 1650-3686 (print), 1650-3740 (online)


In recent years; customer-oriented product development has become vital for companies to maintain competitive in today’s global marketplace. A key challenge for most designers now is to understand the changing expectations of customers through rapid market analysis and develop new products to meet their needs. In this respect; quality function deployment (QFD) serves as a useful tool for customer-oriented product development. It provides a methodology to aid the planning and realisation of quality products or services that can meet or exceed customer expectations by bridging the communication gap between customers and the design team.

Since its advent; QFD has been successfully applied in many organisations to improve processes and to gain competitive advantages (Guinta and Praizler 1993; Shillito 1994; Prasad 1998; ReVelle et al. 1998). In QFD; customer requirements about a product are taken into account through conducting a survey; and are treated as a set of customer needs (CNs). A number of engineering design requirements (DRs) that can meet these CNs are then identified with the objective to maximise customer satisfactions. Typically; a QFD system comprises four inter-linked phases to fully deploy the CNs phase by phase (Shillito; 1994; Cohen; 1995; Karsak; 2004). The first phase of QFD; usually called the house of quality (HOQ); is of fundamental and strategic importance to the product development process (Hauser and Clausing; 1988). The purpose of developing a HOQ is to prioritise the design objectives and determine the target design objective levels; so as to maximise customer satisfactions in product development (Chan and Wu; 1998).

The HOQ contains important information including the voice of customers (VOC) and the voice of technologists (Moskowitz and Kim; 1997). The VOC typically contains subjective statements of customer perceptions by indicating the relative importance ratings (Don Clausing; 1988). The importance ratings of CNs and the relationships between CNs and DRs are usually expressed by linguistic terms such as ‘low importance’; ‘high importance’; ‘strong relationship’; and ‘weak relationship’; etc. Traditionally; such subjective and imprecise design information is quantified by fuzzy numbers; which can be manipulated using the mathematical operations provided by fuzzy set theory (Temponi et al.; 1999; Chen and Weng; 2003). For example; Khoo and Ho (1996) used symmetrical triangular fuzzy numbers (STFNs) to perform QFD analysis. In traditional fuzzy number based approaches; the boundaries of fuzzy numbers rely on experts’ knowledge and subjective judgments; which may not be in good agreement with actual facts. This may affect the priority analysis in the HOQ and subsequently lead to inappropriate decision making in product development.

In this study; rough set theory is extended to handle the vague and subjective information inherent in the development of QFD. A novel concept called rough numbers is proposed based on the basic principles of rough set theory and is used to develop the HOQ. The rough number based approach proposed is able to facilitate the development of the HOQ through more realistic representation of human’s subjective and vague descriptions to parameters such as importance ratings in QFD. Knowledge representation using rough numbers is able to provide more insights into the rating preferences of customers and design experts; compared to the representation using fuzzy numbers. An illustrative example is used to demonstrate the advantages of the proposed approach.


Product development; Quality function deployment (QFD); House of quality (HOQ); Rough sets


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