Quality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and product performance is improved to optimize customer needs (CNs). For product development, determining the fulfillment levels of design requirements (DRs) is crucial during QFD optimization. However, in real world applications, the values of DRs are often discrete instead of continuous. To the best of our knowledge, there is no mixed integer linear programming (MILP) model in which the discrete DRs values are considered. Therefore, in this paper, a new QFD optimization approach combining MILP model and Kano model is suggested to acquire the optimized solution from a limited number of alternative DRs, the values of which can be discrete. The proposed model can be used not only to optimize the product development but also in other applications of QFD such as quality management, planning, design, engineering and decision-making, on the condition that DR values are discrete. Additionally, the problem of lack of solutions in integer and linear programming in the QFD optimization is overcome. Finally, the model is illustrated through an example.
Quality function deployment (QFD), which is a widely used customer-driven product development method originated in the late 1960s in Japan by Akao (1990). In 1972, QFD was implemented at the Kobe shipyards of Mitsubishi Heavy Industries Ltd. Its application was followed by successful implementations throughout Japan (e.g. at Toyota). QFD was introduced to US with its application in Ford Motor Company, and has played an important role since then at companies (Prasad, 1998).
QFD is a well-known planning methodology for translating customer needs (CNs) into relevant design requirements (DRs). Generally QFD utilizes four sets of matrices: houses of quality (HOQ) to relate the CNs to product planning, parts deployment, process planning and manufacturing operations (Hauser & Clausing, 1998). QFD develops a new product, or a new version of an existing product to maximize customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization considering such criteria as cost and technical difficulty. In addition to these, QFD has increasingly been applied to transportation and communication, electronics and electrical utilities, software systems, manufacturing, services, education and research, and many other industries including aerospace, construction, packaging and textile (Chan & Wu, 2002).
A comprehensive review of the related literature reveals many studies on QFD analysis. However, there are few studies where values of DRs are taken as discrete. What is more, there seems to be a lack of integer and linear programming solutions in the QFD optimization (Lai, Xie, & Tan, 2005). Therefore, the present study proposes a new QFD optimization approach combining mixed integer linear programming (MILP) model and Kano model. The approach is based on the proposed model by Lai, Xie, and Tan (2004), and it focuses on the field of product development.
This paper is organized as follows. Section 2 gives a short literature review. Section 3 presents a new QFD optimization approach and describes its mathematical modeling. An example is given to demonstrate our approach in Section 4. Finally, conclusions and discussions are provided in Section 5. The notations used in the paper are listed in Appendix A.
In this study, a new approach to QFD processes is proposed to obtain the optimal solution from a limited number of DRs alternatives, whose values are discrete. In the proposed approach, MILP model and the Kano model is integrated into the product development problem. The problem of lack of solutions in integer and linear programming in the QFD optimization is overcome by using the proposed model.
To conclude, the present study extends the existing studies by considering the correlation within both CNs and DRs to calculate and by integrating it into the MILP model. Also, importance weights of CNs and NTI ratings of DRs are calculated considering the correlation within both CNs and DRs, and both correlations are included in the model.
Additionally, this paper presents a case study on washing machine development. As a result, a new MILP model can be successfully implemented in the product development process. However, the proposed model can be used not only to optimize the product development but also in other applications of QFD such as quality management, planning, design, engineering and decision-making, on the condition that DR values are discrete.
In this paper, all values (i.e. correlation, relationship, etc.) are accepted as deterministic. In further research, these values can be assumed to be fuzzy numbers. Additionally, the fulfillment levels of DRs are assumed to be in a linear relationship with cost. However, there is rarely a linear relationship in real world applications because of the constraints of technology and production (i.e. the dimensions of computer monitors have changes such as 15″, 17″ and 19″, but the price of these monitors changes sequentially as in 140, 150 and 250 $). For this reason, to achieve greater validity in further research, it is of utmost importance that this piecewise-linear may be taken into account when designing the model. Also, generally, the QFD process may be necessary to optimize more than one conflicting objectives simultaneously. Therefore, the proposed MILP model may be developed with multi-objective decision-making (MODM) approach.
