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A systematic decision procedure in this study aims at considering the interdependencies between customersÕ needs and product technical requirements, and the inner dependencies within themselves

A fuzzy optimization model for QFD planning process using analytic network approach

European Journal of Operational Research, no. 2 (2007): 390-411

Cited by: 633|Views7
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Abstract

In both the quality improvement and the design of a product, the engineering characteristics affecting product performance are primarily identified and improved to optimize customer needs (CNs). Especially, the limited resources and increased market competition and product complexity require a customer-driven quality management and produc...More

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Introduction
  • Business decisions in many companies involve selecting the products providing a high degree of customer satisfaction to meet multiple objectives.
  • The authors extend these studies by employing linguistic parameters to emphasize impreciseness and vagueness in ANP because of human judgmentsÕ subjectivity on the importance of PTRs related to CNs. Besides, in order to determine the set of PTRs, which will be considered in product design, the authors construct a mixed integer linear programming model to optimize target improvements.
  • The first step of the network representation in QFD model is the identification of the CNs and PTRs. the determination of the importance of the CNs, which corresponds to the first step of the matrix manipulation concept of the ANP, follows (Lee and Kim, 2000; Saaty and Takizawa, 1986).
Highlights
  • Today, business decisions in many companies involve selecting the products providing a high degree of customer satisfaction to meet multiple objectives
  • Using crisp analytic network process (ANP) in Quality function deployment (QFD) has been considered by Partovi (2001), Partovi and Corredoira (2002), and Karsak et al (2002). We extend these studies by employing linguistic parameters to emphasize impreciseness and vagueness in ANP because of human judgmentsÕ subjectivity on the importance of product technical requirements (PTRs) related to customersÕ needs (CNs)
  • A systematic decision procedure in this study aims at considering the interdependencies between CNs and PTRs, and the inner dependencies within themselves
  • Since in a product design linguistic expressions like a strongly more important CN or PTR than another CN or PTR may be used in case of incomplete information, the fuzzy model in this paper can capture this vagueness
  • Taking into account the proposed fuzzy optimization framework, a budget planning which used the weights of fuzzy ANP given in Table 10 was made
  • Different fuzzy analytic hierarchy process (AHP) approaches and fuzzy optimization models may be used to compare with the results obtained in this paper
Results
  • Fung et al (1998) propose a hybrid system that incorporates the principles of QFD, analytic hierarchy process, and fuzzy set theory to tackle the complex and often imprecise problem domain encountered in customer requirement management.
  • First of all, fuzzy ANP model is used for the prioritizing of PTRs in QFD.
  • Determining the importance degrees of CNs with linguistic data by assuming that there is no dependence among the CNs: Calculation of w1 Step 3.
  • Determining the importance degrees of PTRs with respect to each CN with linguistic data by assuming that there is no dependence among the PTRs: Calculation of W2 Step 4.
  • The steps of the proposed fuzzy ANP methodology for HoQ according to Table 1 are described as follows: Table 3 Relative importance of the PTRs for sound insulating (SI)
  • Assuming that there is no dependence among the CNs, the following eigenvector for the CNs is obtained by performing the extent analysis of fuzzy AHP methodology with respect to the goal of achieving the best
  • The authors deal with the dependence among the PTRs. As previously accomplished for CNs, the inner dependencies are determined and required pairwise linguistic comparisons are performed.
  • With respect to the results of fuzzy ANP, the most important PTR is PVC conductivity coefficient.
  • The crisp data for crisp AHP and crisp ANP methods were obtained using the largest possible values of fuzzy numbers.
Conclusion
  • This paper includes a combined ANP and a fuzzy logic approach to incorporate the CNs and the PTRs systematically into the product design phase in QFD.
  • Since in a product design linguistic expressions like a strongly more important CN or PTR than another CN or PTR may be used in case of incomplete information, the fuzzy model in this paper can capture this vagueness.
  • Different fuzzy AHP approaches and fuzzy optimization models may be used to compare with the results obtained in this paper
Summary
  • Business decisions in many companies involve selecting the products providing a high degree of customer satisfaction to meet multiple objectives.
  • The authors extend these studies by employing linguistic parameters to emphasize impreciseness and vagueness in ANP because of human judgmentsÕ subjectivity on the importance of PTRs related to CNs. Besides, in order to determine the set of PTRs, which will be considered in product design, the authors construct a mixed integer linear programming model to optimize target improvements.
  • The first step of the network representation in QFD model is the identification of the CNs and PTRs. the determination of the importance of the CNs, which corresponds to the first step of the matrix manipulation concept of the ANP, follows (Lee and Kim, 2000; Saaty and Takizawa, 1986).
  • Fung et al (1998) propose a hybrid system that incorporates the principles of QFD, analytic hierarchy process, and fuzzy set theory to tackle the complex and often imprecise problem domain encountered in customer requirement management.
  • First of all, fuzzy ANP model is used for the prioritizing of PTRs in QFD.
  • Determining the importance degrees of CNs with linguistic data by assuming that there is no dependence among the CNs: Calculation of w1 Step 3.
  • Determining the importance degrees of PTRs with respect to each CN with linguistic data by assuming that there is no dependence among the PTRs: Calculation of W2 Step 4.
  • The steps of the proposed fuzzy ANP methodology for HoQ according to Table 1 are described as follows: Table 3 Relative importance of the PTRs for sound insulating (SI)
  • Assuming that there is no dependence among the CNs, the following eigenvector for the CNs is obtained by performing the extent analysis of fuzzy AHP methodology with respect to the goal of achieving the best
  • The authors deal with the dependence among the PTRs. As previously accomplished for CNs, the inner dependencies are determined and required pairwise linguistic comparisons are performed.
  • With respect to the results of fuzzy ANP, the most important PTR is PVC conductivity coefficient.
  • The crisp data for crisp AHP and crisp ANP methods were obtained using the largest possible values of fuzzy numbers.
  • This paper includes a combined ANP and a fuzzy logic approach to incorporate the CNs and the PTRs systematically into the product design phase in QFD.
  • Since in a product design linguistic expressions like a strongly more important CN or PTR than another CN or PTR may be used in case of incomplete information, the fuzzy model in this paper can capture this vagueness.
  • Different fuzzy AHP approaches and fuzzy optimization models may be used to compare with the results obtained in this paper
Tables
  • Table1: The evaluation algorithm steps for determining the overall priorities of the PTRs
  • Table2: Linguistic scales for difficulty and importance
  • Table3: Relative importance of the PTRs for sound insulating (SI)
  • Table4: The inner dependence matrix of the CNs with respect to heat insulating (HI)
  • Table5: The column eigenvectors with respect to each CN
  • Table6: The inner dependency matrix among the customer needs
  • Table7: The inner dependency matrix among CNs
  • Table8: The inner dependency matrix among PTRs
  • Table9: The inner dependency matrix of the PTRs
  • Table10: The crisp data for crisp AHP and crisp ANP methods were obtained using the largest possible values of fuzzy numbers. Comparison of the used methods
  • Table11: Data for the case study
Download tables as Excel
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