Prioritizing quality characteristics in dynamic quality function deployment

被引:63
作者
Raharjo, H. [1 ]
Xie, M.
Brombacher, A. C.
机构
[1] Natl Univ Singapore, Dept Ind & Syst Engn, Singapore 119260, Singapore
[2] Tech Univ Eindhoven, NL-5600 MB Eindhoven, Netherlands
[3] Design Technol Inst, Singapore, Singapore
[4] Design Technol Inst, Eindhoven, Netherlands
关键词
dynamic quality function deployment; future voice of the customer; prioritization; user preference; quality loss function; zero one goal programming;
D O I
10.1080/00207540600547414
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Due to the combination of rapid influx of new technology, high pressure on time-to-market and increasing globalization, the number of products that have highly uncertain and dynamic specifications or customer requirements might significantly increase. In order to deal with these inherently volatile products or services, we need to adopt a more pro-active approach in order not to produce an unwanted product or service. Thus, based on the idea of the quality loss function and the zero-one goal programming, an intuitively simple mathematical model is developed to prioritize the quality characteristics (QCs) in the dynamic quality function deployment (QFD). It incorporates a pro-active approach towards providing products and services that meet the future voice of the customer (FVOC). The aim is to determine and prioritize only the 'important' QCs with a greater confidence in meeting the FVOC. It is particularly useful when the number of the potentially dominant QCs is very large so that, by using the prioritization, the size of the QFD can be effectively reduced. Some constraints, such as minimum customer satisfaction level and limitation on budget are also taken into consideration. A sensitivity analysis is suggested to give an insight to the QFD users in the change of parameters of the proposed model.
引用
收藏
页码:5005 / 5018
页数:14
相关论文
共 40 条
[1]   Quality loss functions for optimization across multiple response surfaces [J].
Ames, AE ;
Mattucci, N ;
MacDonald, S ;
Szonyi, G ;
Hawkins, DM .
JOURNAL OF QUALITY TECHNOLOGY, 1997, 29 (03) :339-346
[2]  
[Anonymous], QUALITY ENG
[3]   AN AHP FRAMEWORK FOR PRIORITIZING CUSTOMER REQUIREMENTS IN QFD - AN INDUSTRIALIZED HOUSING APPLICATION [J].
ARMACOST, RL ;
COMPONATION, PJ ;
MULLENS, MA ;
SWART, WW .
IIE TRANSACTIONS, 1994, 26 (04) :72-79
[4]  
Askin RG, 2000, IIE SOLUTIONS, V32, P9, DOI 10.1080/07408170008963875
[5]   Inexact genetic algorithm approach to target values setting of engineering requirements in QFD [J].
Bai, H ;
Kwong, CK .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2003, 41 (16) :3861-3881
[6]   Cost engineering with quality function deployment [J].
Bode, J ;
Fung, RYK .
COMPUTERS & INDUSTRIAL ENGINEERING, 1998, 35 (3-4) :587-590
[7]  
Bouchereau V., 2000, BENCHMARKING, V7, P8, DOI DOI 10.1108/14635770010314891
[8]   Identifying environmental improvement options by combining life cycle assessment and fuzzy set theory [J].
Bovea, MD ;
Wang, B .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2003, 41 (03) :593-609
[9]   Managing product reliability in business processes 'under pressure' [J].
Brombacher, AC ;
Sander, PC ;
Sonnemans, PJM ;
Rouvroye, JL .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2005, 88 (02) :137-146
[10]   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