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Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept. (English) Zbl 1175.90435
Summary: 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.
90C70Fuzzy programming
90C05Linear programming
[1]Almannai, B.; Greenough, R.; Kay, J.: A decision support tool based on QFD and FMEA for the selection of manufacturing automation technologies, Robotics and computer integrated manufacturing 24, 501-507 (2008)
[2]Al-Mashari, M.; Zairi, M.; Ginn, D.: Key enablers for the effective implementation of QFD: A critical analysis, Industrial management and data systems 105, No. 9, 1245-1260 (2005)
[3]Bondia, J.; Picó, J.: Analysis of linear systems with fuzzy parametric uncertainty, Fuzzy sets and systems 135, 81-121 (2003) · Zbl 1024.93033 · doi:10.1016/S0165-0114(02)00251-8
[4]Chan, L. K.; Wu, M. L.: Quality function deployment: A literature review, European journal of operational research 143, 463-497 (2002) · Zbl 1082.90022 · doi:10.1016/S0377-2217(02)00178-9
[5]Chan, L. K.; Wu, M. L.: A systematic approach to quality function deployment with a full illustrative example, Omega – the international journal of management science 33, No. 2, 119-139 (2005)
[6]Chen, L. H.; Ko, W. C.: A fuzzy nonlinear model for quality function deployment considering kano’s concept, Mathematical and computer modeling 48, 581-593 (2008) · Zbl 1145.90468 · doi:10.1016/j.mcm.2007.06.029
[7]Chen, L. H.; Weng, M. C.: A fuzzy model for exploiting quality function deployment, Mathematical and computer modeling 38, 559-570 (2003) · Zbl 1080.90542 · doi:10.1016/S0895-7177(03)90027-6
[8]Chen, L. H.; Weng, M. C.: An evaluation approach to engineering design in QFD processes using fuzzy goal programming models, European journal of operational research 172, 230-248 (2006) · Zbl 1116.90067 · doi:10.1016/j.ejor.2004.10.004
[9]Chen, K. M.; Horng, K. H.; Chiang, K. N.: Coplanarity analysis and validation of PBGA and T2-BGA packages, Finite elements in analysis and design 38, 1165-1178 (2002) · Zbl 1155.74405 · doi:10.1016/S0168-874X(02)00057-4
[10]Chen, Y.; Fung, R. Y. K.; Tang, J.: Rating technical attributes in fuzzy QFD by integrating fuzzy weight average method and fuzzy expected value operator, European journal of operational research 174, 1553-1566 (2006) · Zbl 1103.90329 · doi:10.1016/j.ejor.2004.12.026
[11]Cristiano, J. J.; Iii, C. C. White; Liker, J. K.: Application of multiattribute decision analysis to quality function deployment for target setting, IEEE transactions on man, and cybernetics – part C: Applications and reviews 31, No. 3, 366-382 (2001)
[12]J., J. J. Cristiano; J., J. K. Liker; Iii, C. C. White: Key factors in the successful application of quality function deployment (QFD), IEEE transactions on engineering management 48, No. 1, 81-95 (2001)
[13]Guimarães, A. C. F.; Lapa, C. M. F.: Fuzzy FMEA applied to PWR chemical and volume control system, Progress in nuclear energy 44, No. 3, 191-213 (2004)
[14]Gutman, J.: A means-end chain model based on consumer categorization processes, Journal of marketing 46, No. 1, 60-72 (1982)
[15]Kahraman, C.; Ertay, T.; Büyüközkan, G.: A fuzzy optimization model for QFD planning process using analytic network approach, European journal of operational research 171, 390-411 (2006) · Zbl 1090.90016 · doi:10.1016/j.ejor.2004.09.016
[16]Klir, G., Yuan, B., 2003. Fuzzy sets and fuzzy logic: Theory and application. 3rd ed. Pearson Education, Taiwan.
[17]Kwong, C. K.; Bai, H.: Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach, IIE transactions 35, 619-626 (2003)
[18]Kwong, C. K.; Chen, Y.; Bai, H.; Chan, D. S. K.: A methodology of determining aggregated importance of engineering characteristics in QFD, Computers and industrial engineering 53, 667-679 (2007)
[19]Lager, T.: The industrial usability of quality function deployment: A literature review and synthesis on a meta-level, R&D management 35, No. 4, 409-426 (2005)
[20]Myint, S.: A framework of an intelligent quality function deployment (IQFD) for discrete assembly environment, Computers and industrial engineering 45, 269-283 (2003)
[21]Pillay, A.; Wang, J.: Modified failure mode and effects analysis using approximate reasoning, Reliability engineering and system safety 79, 69-85 (2003)
[22]Sharma, R. K.; Kumar, D.; Kumar, P.: Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modeling, The international journal of quality and reliability management 22, No. 9, 986-1004 (2005)
[23]Stamatis, D. H.: Failure mode and effect analysis – FMEA from theory to execution, (1995)
[24]Tan, C. M.: Customer-focused build-in reliability: A case study, The international journal of quality and reliability management 20, No. 2/3, 378-397 (2003)
[25]Wasserman, G. S.: On how to prioritize design requirements during the QFD planning process, IIE transactions 25, No. 3, 59-65 (1993)
[26]Wu, H. C.: Linear regression analysis for fuzzy input and output data using the extension principle, Computers and mathematics with applications 45, 1849-1859 (2003) · Zbl 1043.62062 · doi:10.1016/S0898-1221(03)90006-X
[27]Xu, Z. S.; Da, Q. L.: An overview of operators for aggregating information, International journal of intelligent systems 18, 953-969 (2003) · Zbl 1069.68612 · doi:10.1002/int.10127
[28]Zadeh, L. A.: Fuzzy set as a basis for a theory of possibility, Fuzzy sets and systems 1, 3-28 (1978) · Zbl 0377.04002 · doi:10.1016/0165-0114(78)90029-5
[29]Zimmermann, H. J.: Fuzzy set theory and its applications, (1991)