Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept

被引:61
作者
Chen, Liang-Hsuan [1 ]
Ko, Wen-Chang [1 ,2 ]
机构
[1] Natl Cheng Kung Univ, Dept Ind & Informat Management, Tainan 701, Taiwan
[2] Kun Shan Univ, Dept Informat Management, Tainan, Taiwan
关键词
Fuzzy sets; Fuzzy linear programming; Quality function deployment (QFD); Means-end Chain (MEC); Risk analysis; QUALITY FUNCTION DEPLOYMENT; FAILURE MODE; REQUIREMENTS; DESIGN;
D O I
10.1016/j.ejor.2009.03.010
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Quality function deployment (QFD) is a customer-driven approach in processing new product development (NPD) to maximize customer satisfaction. Determining the fulfillment levels of the "hows", including design requirements (DRs), part characteristics (PCs), process parameters (PPs) and production requirements (PRs), is an important decision problem during the four-phase QFD activity process for new product development. Unlike previous studies, which have only focused on determining DRs, this paper considers the close link between the four phases using the means-end chain (MEC) concept to build up a set of fuzzy linear programming models to determine the contribution levels of each "how" for customer satisfaction. In addition, to tackle the risk problem in NPD processes. 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 approaches are used for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:619 / 632
页数:14
相关论文
共 29 条
[1]   A decision support tool based on QFD and FMEA for the selection of manufacturing automation technologies [J].
Ahnannai, B. ;
Greenough, R. ;
Kay, J. .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2008, 24 (04) :501-507
[2]   Key enablers for the effective implementation of QFD: a critical analysis [J].
Al-Mashari, M ;
Zairi, M ;
Ginn, D .
INDUSTRIAL MANAGEMENT & DATA SYSTEMS, 2005, 105 (09) :1245-1260
[3]   Analysis of linear systems with fuzzy parametric uncertainty [J].
Bondia, J ;
Picó, J .
FUZZY SETS AND SYSTEMS, 2003, 135 (01) :81-121
[4]   Quality function deployment: A literature review [J].
Chan, LK ;
Wu, ML .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2002, 143 (03) :463-497
[5]   A systematic approach to quality function deployment with a full illustrative example [J].
Chan, LK ;
Wu, ML .
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2005, 33 (02) :119-139
[6]   Coplanarity analysis and validation of PBGA and T2 -BGA packages [J].
Chen, KM ;
Horng, KH ;
Chiang, KN .
FINITE ELEMENTS IN ANALYSIS AND DESIGN, 2002, 38 (12) :1165-1178
[7]   An evaluation approach to engineering design in QFD processes using fuzzy goal programming models [J].
Chen, LH ;
Weng, MC .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2006, 172 (01) :230-248
[8]  
Chen LH, 2003, MATH COMPUT MODEL, V38, P559, DOI 10.1016/S0895-7177(03)00251-6
[9]   A fuzzy nonlinear model for quality function deployment considering Kano's concept [J].
Chen, Liang-Hsuan ;
Ko, Wen-Chang .
MATHEMATICAL AND COMPUTER MODELLING, 2008, 48 (3-4) :581-593
[10]   Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator [J].
Chen, Yizeng ;
Fung, Richard Y. K. ;
Tang, Jiafu .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2006, 174 (03) :1553-1566