Fuzzy Nonlinear Models for New Product Development Using Four-Phase Quality Function Deployment Processes

Quality function deployment (QFD) frameworks are useful tools for constructing a new product development (NPD) plan that enables the clear itemization of customer needs and the systematic evaluation of each solution to maximize customer satisfaction. A complete QFD process includes four sequential phases in which four important decision outcomes are determined for NPD, namely, the fulfillment levels of design requirements (DRs), part characteristics, process parameters, and production requirements. Unlike prior studies which have focused only on determining DRs, this paper extends Chen and Ko's models to consider the close link between the four phases in NPD using the means-end chain concept to build up a series of fuzzy nonlinear programming models for determining the fulfillment levels of each decision outcome for customer satisfaction. In addition, this paper incorporates risk analysis, which is treated as the constraint in the models, into the QFD process. To deal with the vague nature of product development processes, fuzzy sets are applied for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model.

[1]  Liang-Hsuan Chen,et al.  An evaluation approach to engineering design in QFD processes using fuzzy goal programming models , 2006, Eur. J. Oper. Res..

[2]  Ronald R. Yager,et al.  Weighted Maximum Entropy OWA Aggregation With Applications to Decision Making Under Risk , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[3]  Dinesh Kumar,et al.  Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling , 2005 .

[4]  J. Gutman.  A Means-End Chain Model Based on Consumer Categorization Processes , 1982 .

[5]  Paul Kauffmann,et al.  Integration of Kano's Model Into QFD for Multiple Product Design , 2007, IEEE Transactions on Engineering Management.

[6]  Liang-Hsuan Chen,et al.  A fuzzy model for exploiting quality function deployment , 2003 .

[7]  Susan Carlson Skalak.  House of Quality , 2002 .

[8]  Yu-Cheng Lee,et al.  Quality function deployment implementation based on Fuzzy Kano model: An application in PLM system , 2008, Comput. Ind. Eng..

[9]  Z. S. Xu,et al.  An overview of operators for aggregating information , 2003, Int. J. Intell. Syst..

[10]  Mohamed Zairi,et al.  Key enablers for the effective implementation of QFD: a critical analysis , 2005, Ind. Manag. Data Syst..

[11]  Cher Ming Tan,et al.  Customer‐focused build‐in reliability: a case study , 2003 .

[12]  Liang-Hsuan Chen,et al.  A fuzzy nonlinear model for quality function deployment considering Kano's concept , 2008, Math. Comput. Model..

[13]  Biren Prasad,et al.  A concurrent function deployment technique for a workgroup-based engineering design process , 2000 .

[14]  Kosuke Ishii,et al.  Design Process Error Proofing: Failure Modes and Effects Analysis of the Design Process , 2007 .

[15]  Jin Wang,et al.  Modified failure mode and effects analysis using approximate reasoning , 2003, Reliab. Eng. Syst. Saf..

[16]  John J. Cristiano,et al.  Application of multiattribute decision analysis to quality function deployment for target setting , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[17]  Liang-Hsuan Chen,et al.  An approximate approach for ranking fuzzy numbers based on left and right dominance , 2001 .

[18]  Dimitar Filev,et al.  SLIDE: A simple adaptive defuzzification method , 1993, IEEE Trans. Fuzzy Syst..

[19]  Kuo-Ning Chiang,et al.  Coplanarity analysis and validation of PBGA and T 2 -BGA packages , 2002 .

[20]  Glenn H. Mazur,et al.  The leading edge in QFD: past, present and future , 2003 .

[21]  Jiafu Tang,et al.  Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator , 2006, Eur. J. Oper. Res..

[22]  Liang-Hsuan Chen,et al.  Fuzzy linear programming models for new product design using QFD with FMEA , 2009 .

[23]  H. Schneider.  Failure mode and effect analysis : FMEA from theory to execution , 1996 .

[24]  Thomas Lager,et al.  The industrial usability of quality function deployment: a literature review and synthesis on a meta‐level , 2005 .

[25]  Victor B. Kreng,et al.  QFD-based modular product design with linear integer programming—a case study , 2004 .

[26]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[27]  Gary S. Wasserman,et al.  ON HOW TO PRIORITIZE DESIGN REQUIREMENTS DURING THE QFD PLANNING PROCESS , 1993 .

[28]  Jesús Picó,et al.  Analysis of linear systems with fuzzy parametric uncertainty , 2003, Fuzzy Sets Syst..

[29]  C K Kwong,et al.  Determining the Importance Weights for the Customer Requirements in QFD Using a Fuzzy AHP with an Extent Analysis Approach , 2003 .

[30]  Ibo van de Poel,et al.  Methodological problems in QFD and directions for future development , 2007 .

[31]  Loon Ching Tang,et al.  Fuzzy assessment of FMEA for engine systems , 2002, Reliab. Eng. Syst. Saf..

[32]  Min Xie,et al.  Ranking of customer requirements in a competitive environment , 2008, Comput. Ind. Eng..

[33]  Zheng Wang,et al.  Computing Completion Time and Optimal Scheduling of Design Activities in Concurrent Product Development Process , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[34]  Manbir S. Sodhi,et al.  A conceptual QFD planning model , 2001 .

[35]  Richard Greenough,et al.  A decision support tool based on QFD and FMEA for the selection of manufacturing automation technologies , 2008 .

[36]  George L. Vairaktarakis,et al.  Optimization tools for design and marketing of new/improved products using the house of quality , 1999 .

[37]  Irem Y. Tumer,et al.  Linking product functionality to historic failures to improve failure analysis in design , 2005 .

[38]  San Myint.  A framework of an intelligent quality function deployment (IQFD) for discrete assembly environment , 2003, Comput. Ind. Eng..

[39]  Jeffrey K. Liker,et al.  Customer-driven product development through quality function deployment in the u.s. and japan , 2000 .

[40]  Jian-Bo Yang,et al.  Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean , 2009, Expert Syst. Appl..

[41]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[42]  N. Kano,et al.  Attractive Quality and Must-Be Quality , 1984 .

[43]  E. Ertugrul Karsak,et al.  Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment , 2004 .

[44]  Liang-Hsuan Chen,et al.  Fuzzy approaches to quality function deployment for new product design , 2009, Fuzzy Sets Syst..

[45]  Don Gunther,et al.  Quality Function Deployment - How to Make QFD Work for You , 2000 .

[46]  Ching-Torng Lin,et al.  A fuzzy-logic-based approach for new product Go/NoGo decision at the front end , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[47]  Ali A. Ghorbani,et al.  Astrolabe: A Collaborative Multiperspective Goal-Oriented Risk Analysis Methodology , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[48]  Hsien-Chung Wu.  Linear regression analysis for fuzzy input and output data using the extension principle , 2003 .