رویکرد مبتنی بر استدلال مستدل برای گسترش کارکرد کیفیت تحت عدم قطعیت
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|7064||2009||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 5684–5694
Quality function deployment (QFD) is a methodology for translating customer wants (WHATs) into relevant engineering design requirements (HOWs) and often involves a group of cross-functional team members from marketing, design, quality, finance and production and a group of customers. The QFD team is responsible for assessing the relationships between WHATs and HOWs and the interrelationships between HOWs, and the customers are chosen for assessing the relative importance of each customer want. Each member and customer from different backgrounds often demonstrates significantly different behavior from the others and generates different assessment results, complete and incomplete, precise and imprecise, known and unknown, leading to the QFD with great uncertainty. In this paper, we present an evidential reasoning (ER) based methodology for synthesizing various types of assessment information provided by a group of customers and multiple QFD team members. The proposed ER-based QFD methodology can be used to help the QFD team prioritize design requirements with both customer wants and customers’ preferences taken into account. It is verified and illustrated with a numerical example.
Quality function deployment (QFD) is a cross-functional planning methodology commonly used to ensure that customer expectations or requirements, often referred to as the voice of the customer (VoC) or WHATs, are deployed through product planning, part development, process planning and production planning. It is a team-based and disciplined approach to product design, engineering and production and provides in-depth evaluation of a product. An organization that correctly implements QFD can improve engineering knowledge, productivity and quality and reduce costs, product development time and engineering changes (Besterfield, Besterfield-Michna, Besterfield, & Besterfield-Sacre, 2003). QFD has now become a standard practice by most leading organizations and has been successfully implemented world widely. A comprehensive literature review of QFD and its extensive applications is provided by Chan and Wu (2002). The successful implementation of QFD requires a significant number of subjective judgments from both customers and QFD team members. Customers are selected for assessing the relative importance of customer expectations or requirements (WHATs). The QFD team is set up to identify customer wants, map them into relevant engineering requirements, which are often called the HOWs, meaning how the WHATs are to be met, develop the relationship matrix between WHATs and HOWs and the interrelationship matrix between HOWs, and prioritize the HOWs. As two of the key issues of QFD, prioritization methods for WHATs and HOWs have been extensively researched and quite a number of approaches have been suggested in the QFD literature. For example, the analytic hierarchy process (AHP), a well-known and commonly used multi-criteria decision making method, and its variants:fuzzy AHP, analytic network process (ANP) and fuzzy ANP have been suggested and widely applied to prioritize customer requirements (Akao, 1990, Armacost et al., 1994, Büyüközkan et al., 2004, Ertay et al., 2005, Fung et al., 1998, Hanumaiah et al., 2006, Kahraman et al., 2006, Karsak et al., 2003, Kim et al., 2005, Kwong and Bai, 2003, Kwong and Bai, 2002, Lu et al., 1994, Park and Kim, 1998, Partovi, 2007 and Wang et al., 1998). Fuzzy and entropy method (Chan et al., 1999 and Chan and Wu, 2005) have also been proposed to rate the importance of customer needs. The weighted sum method (Chen and Weng, 2003 and Wasserman, 1993), fuzzy weighted average (FWA) (Chen et al., 2006, Chen and Weng, 2006, Liu, 2005 and Vanegas and Labib, 2001), fuzzy outranking approach (Wang, 1999) and grey model (Wu, 2006) have all been suggested for prioritizing engineering design requirements. Fuzzy logic and fuzzy inference have been extensively applied to assess the importance of WHATs and prioritize HOWs (Bottani and Rizzi, 2006, Chen et al., 2004, Chen et al., 2005, Karsak, 2004, Karsak, 2004, Khoo and Ho, 1996, Ramasamy and Selladurai, 2004, Shen et al., 2001 and Temponi et al., 1999). To reduce the heavy burden of customers and QFD team members in making their judgments, Franceschini and Rossetto (2002) develop an interactive algorithm for prioritizing product’s technical design characteristics, called IDCR (interactive design characteristic ranking), which allows the QFD team to determine a ranking order for design characteristics without using subjective rating scales and explicitly knowing the relative degree of importance of customer requirements. The IDCR algorithm avoids an inappropriate conversion from qualitative information to a relationship matrix. Han, Kim, and Choi (2004) suggest a linear partial ordering approach for assessing the information in QFD and prioritizing engineering characteristics. The linear partial information is used to extract the weights of customer wants and the relationship values between WHATs and HOWs. Four types of dominance relations that are frequently used in multi-attribute decision making with incomplete information are used to determine the priorities of engineering characteristics when the linear partial orderings of customers and QFD team members are given. The dominance relations between engineering characteristics are established through the solution of a number of linear programming models. Considering the fact that people contributing to the QFD process may give their preferences in different formats, numerically or linguistically, depending on their backgrounds, Büyüközkan and Feyzioğlu (2005) present a fuzzy logic based group decision making approach with multiple expression formats for QFD with the hope to better capture and analyze the demand of customers, where different expressions are aggregated into one collaborative decision by using fuzzy set theory. Their approach is illustrated with a software development example. Ho, Lai, and Chang (1999) also discuss group behaviors in QFD and present an integrated group decision making approach for aggregating team members’ opinions in the case where some members in a team have an agreed criteria set while others prefer individual criteria sets. By using voting and linear programming techniques, their integrated approach consolidates individual preferences into a group consensus and is used for determining the relative importance of customer requirements. The above literature review clearly shows that quite a lot of efforts have been made to deal with fuzziness in the process of QFD. However, no attempt has been made to address the issue of how to deal with incomplete, imprecise and missing (ignorance) information in QFD, which is essentially inherent and sometimes inevitable in human being’s subjective judgments. Fuzzy logic based approaches have been extensively used to model vagueness and ambiguity, but it cannot deal with such uncertainties as incomplete, imprecise and missing information. The purpose of this paper is to develop a rigorous and systematic methodology, on the basis of the ER approach (Wang et al., 2006b, Wang et al., 2006a, Xu et al., 2006, Yang and Singh, 1994, Yang and Sen, 1994, Yang, 2001, Yang and Xu, 2002, Yang and Xu, 2002 and Yang et al., 2006), for synthesizing various types of assessment information provided by a group of customers and multiple QFD team members, which is referred to as the evidential reasoning (ER) based QFD methodology, in order to handle various types of possible uncertainties that may occur in the implementation process of QFD. The proposed ER-based QFD methodology can be used to help the QFD team to prioritize design requirements with both customer wants and customers’ preferences taken into account. It is capable of modeling various types of uncertainties using a unified belief structure in a pragmatic, rigorous, reliable, systematic, transparent and repeatable way. The rest of the paper is organized as follows:in Section 2, we develop the ER-based QFD methodology and describe in detail its modeling mechanism and steps. The methodology is then verified and illustrated with a numerical example in Section 3. Comparisons with other QFD methodologies are provided in Section 4. The paper is concluded in Section 5.
نتیجه گیری انگلیسی
Uncertainty is inherent in human being’ subjective judgments and needs to be taken into account properly in human decision making. In this paper, we developed an ER-based QFD methodology for dealing with various types of uncertainties such as incomplete, imprecise and missing information that may occur in the implementation process of QFD. The proposed methodology allows customers and QFD team members to express their opinions using a unified belief structure independently, can accommodate judgments that may be complete or incomplete, precise or imprecise like intervals, and can also handle situations where customers and QFD team members do not provide any assessment information either because they feel difficult to make proper judgment or simply do not want to make any judgment due to whatever reasons they may have. This provides desirable flexibility for customers and QFD team members to make their true judgments and freely express their opinions yet still within a rigorous and systematic framework. A numerical example was also examined to illustrate the implementation process of the ER-based QFD methodology. It is shown that uncertainties associated with incompleteness, imprecision or ignorance can all be well modeled using the belief structures. The interval ER algorithm provides a rigorous, reliable and systematic way to aggregate multiple belief structures. Perhaps, a drawback to use the ER-based QFD methodology is its complexity. However, such a drawback can be overcome when the interval ER algorithm is implemented recursively on a Microsoft Excel worksheet.