برنامه ریزی محصول در گسترش کارکرد کیفیت با استفاده از یک فرایند ترکیبی شبکه تحلیلی و رویکرد هدف برنامه ریزی شده

Quality function deployment (QFD) is a customer-oriented design tool with cross-functional team members reaching a consensus in developing a new or improved product to increase customer satisfaction. QFD starts with the house of quality (HOQ), which is a planning matrix translating the customer needs into measurable product technical requirements (PTRs). A robust evaluation method should consider the interrelationships among customer needs and PTRs while determining the importance levels of PTRs in the HOQ. This paper employs the analytic network process (ANP) to fulfill this requirement. Furthermore, the proposed analytic procedure should take into account the multi-objective nature of the problem, and thus, incorporate other goals such as cost, extendibility and manufacturability of PTRs. This paper presents a zero–one goal programming methodology that includes importance levels of PTRs derived using the ANP, cost budget, extendibility level and manufacturability level goals to determine the PTRs to be considered in designing the product. A numerical example is presented to illustrate the application of the decision approach.

Global competitiveness has recently become the biggest concern of both manufacturing and service companies, which seek for higher levels of quality for their products and services and continuous improvement to keep up with the rapid pace of development and change throughout the world. Total quality management, which offers a vast selection of techniques to ensure the improvement of quality and productivity, has been a topic on the research agenda for the last four decades. Quality function deployment (QFD) is one of these techniques that aim the satisfaction of the customer at the very beginning, namely the product design phase. It enables the companies to become proactive to quality problems rather than taking a reactive position by acting on customer complaints. As an interdisciplinary team process, QFD is used to plan and design new or improved products or services. QFD employs a cross-functional team to determine customer needs and translate them into product designs through a structured and well-documented framework. QFD helps the companies to maintain their competitiveness using three strategies: decreasing costs, increasing revenues, and reducing the time to produce new products or services (cycle time reduction). QFD allows for the company to allocate resources and to coordinate skills and functions based on customer needs, and thus, may result in lower production costs by ignoring aspects meaning little or nothing to the customer. Its systematic nature also evaluates the necessary decisions for change and development at the beginning of the design process, reducing and even avoiding the mid-project changes and corrections. Enabling to develop the right product or service for the customers with the lowest possible cost, QFD attracts the customers, which results in higher selling rates, leading to higher revenues. In this way, QFD facilitates the entire development process, minimizing the corrections and waste during this phase, and as a matter of fact, optimizing the time required for introducing a new or improved product or service to the market. After World War II, the concept of product development evolved from copying and imitation to a product development based on originality. The importance of design quality became apparent. This dramatic change entailed the development of a totally new concept, the QFD. QFD was first conceptualized in the late 1960s (Akao, 1997). It was immediately adapted by various companies but it did not draw much public attention. A few years later, in 1972, QFD was implemented at the Kobe shipyards of Mitsubishi Heavy Industries Ltd. Even though its application was followed by successful implementations throughout Japan, e.g. at Toyota, it remained a Japanese tool until the early 1980s. Following the article by Kogure and Akao (1983) and through Ford Motor Company and the Cambridge Corporation, QFD has entered the borders of the US and has started to play an important role at companies such as General Motors, Chrysler, Digital Equipment, Hewlett-Packard, AT&T, Procter and Gamble, and Baxter Healthcare (Prasad, 1998). There are two major organizations as sources of QFD, namely the American Supplier Institute (ASI) and GOAL/QPC that both developed their own models having many similarities to each other. The ASI employs a basic four-matrix method developed by Macabe, a Japanese reliability engineer, while GOAL/QPC uses a multiple matrix developed by Akao that incorporates many disciplines into a less structured format consisting of a matrix of matrices (Shillito, 1994). The basic concept of QFD is to translate the desires of customers, i.e. the voice of customer, into product technical requirements (PTRs) or engineering characteristics, and subsequently into parts characteristics, process plans and production requirements. In order to establish these relationships QFD usually requires four matrices: product planning, parts planning, process planning, and production planning matrices, respectively. Product planning matrix translates customer needs into product design requirements; part planning matrix translates important design requirements into product/part characteristics; process planning matrix translates important product/part characteristics into manufacturing operations; production/operation planning matrix translates important manufacturing operations into day-to-day operations and controls. In this paper, we focus on the first of the four matrices, also called the house of quality (HOQ). There has been some research on quantifying the planning issues in HOQ within the past decade, mainly focusing on customer needs. Khoo and Ho, 1996 and Chan et al., 1999 employ fuzzy set theory for rating the customer needs. Other researchers use the analytic hierarchy process (AHP) to determine the degree of importance of the customer needs (Lu et al., 1994, Armacost et al., 1994 and Park and Kim, 1998). In this paper, we propose a novel approach for determining the PTRs that will be considered in designing the product by integrating two decision making techniques, namely the analytic network process (ANP) and zero–one goal programming (ZOGP). The ANP is a decision aid to incorporate the dependence issues into the analysis. Hence it enables us to take into account the degree of the interdependence between the customer needs and PTRs, and the inner dependence among them. Although the ANP facilitates the analysis involving dependence of PTRs on customer needs, inner dependence within customer needs and inner dependence within PTRs, it falls short of taking into account resource limitations and other metrics required in the determination of the PTRs for product design. Wasserman (1993) develops a linear programming model for maximizing customer satisfaction subject to a budget constraint in QFD planning process. Considering the multi-objective nature of the design problem and having calculated the final relative importance of the PTRs via the ANP, we use the highest possible consideration of these PTRs in the design phase as a goal to be satisfied along with other goals such as cost budget, extendibility, and manufacturability of the PTRs. Extendibility incorporates to what degree improvements in one PTR can be extended to other PTRs, and thus, enables a PTR with a high cost but also with high extendibility to be considered as worthwhile in product improvement. Manufacturability highlights the difficulties, which are efforts required to implement the desired improvement. For instance, one PTR could require a unique technology for improvement, whereas another PTR could be easily improved with the present technology. The relative importance weights of these goals are determined by pairwise comparisons. The ZOGP model is solved to determine the PTRs, which will be taken into account in the design phase, in a way to minimize the deviations from the prioritized goals. The paper is organized in the following order. In Section 2, we present a brief description of the HOQ with a concise treatment of its steps. Section 3 describes the basics of the ANP and presents the supermatrix approach developed by Saaty (Saaty and Takizawa, 1986 and Saaty, 1996). In Section 4, the decision methodology to determine the PTRs to be considered in product design is presented. In Section 5, the design of a pencil is selected as an example to apply the developed procedure. Section 6 provides the concluding remarks.

The QFD approach, which enables companies to translate customer needs to relevant product design requirements, is a design tool of vital importance. In this paper, we present a systematic decision procedure to be used in QFD product planning, which has been traditionally based on expert opinions. The decision approach aims to consider the interdependence between the customer needs and PTRs, and the inner dependence within themselves, along with resource limitations, and design metrics such as extendibility and manufacturability. In a period of intensifying competition, the interaction of different approaches should be embraced and incorporated within the QFD process in order to realize its full potential. This paper employs a combined ANP and ZOGP approach to incorporate the customer needs and the PTRs systematically into the product design phase in QFD. The dependencies inherent in the QFD process are taken into account using the ANP approach. Considering resource limitations and multi-objective nature of the problem, a ZOGP model is constructed to determine the set of PTRs that will be taken into account in the product design phase. The use of ANP weights, resource limitations, and other design metrics such as extendibility and manufacturability in the ZOGP model provides feasible and more consistent solutions. The application of the decision procedure is demonstrated via an illustrative example. The proposed framework adds quantitative precision to an otherwise judgmental decision process. The decision approach presented in this paper can be easily extended for real-world applications of QFD by considering additional resource limitations and design metrics.