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Fuzzy regression-based mathematical programming model for quality function deployment. (English) Zbl 1069.90118

Summary: Quality function deployment (QFD) is becoming a widely used customer-driven approach and tool in product design. The inherent fuzziness in QFD modelling makes fuzzy regression more appealing than classical statistical tools. A new fuzzy regression-based mathematical programming approach for QFD product planning is presented. First, fuzzy regression theories with symmetric and nonsymmetric triangular fuzzy coefficients are discussed to identify the relational functions between engineering characteristics and customer requirements and among engineering characteristics. By embedding the relational functions obtained by fuzzy regression, a mathematical programming model is developed to determine targets of engineering characteristics, taking into consideration the fuzziness, financial factors and customer expectations among the competitors in product development process. The proposed modelling approach can help design team assess relational functions in QFD effectively and reconcile tradeoffs among the various degree of customer satisfaction and determine a set of the level of attainment of engineering characteristics for the new/improved product towards a higher customer expectation within design budget. The comparison results under symmetric and non-symmetric cases and the simulation analysis are made when the approach is applied to a quality improvement problem for an emulsification dynamite packing machine.

MSC:

90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
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References:

[1] Akao Y, Quality Function Deployment: Integrating Customer Requirements into Product Design (1990)
[2] DOI: 10.1080/07408179408966620 · doi:10.1080/07408179408966620
[3] DOI: 10.1080/002075499190635 · Zbl 0949.90564 · doi:10.1080/002075499190635
[4] DOI: 10.1016/S0377-2217(02)00178-9 · Zbl 1082.90022 · doi:10.1016/S0377-2217(02)00178-9
[5] DOI: 10.1108/09576069910293040 · doi:10.1108/09576069910293040
[6] DOI: 10.1080/002075498193912 · Zbl 0951.90533 · doi:10.1080/002075498193912
[7] DOI: 10.1080/00207540110061634 · Zbl 1060.90559 · doi:10.1080/00207540110061634
[8] Hauser JR, Harvard Business Review pp 63– (1988)
[9] DOI: 10.1016/S0377-2217(99)00048-X · Zbl 0968.90045 · doi:10.1016/S0377-2217(99)00048-X
[10] DOI: 10.1016/S0360-8352(96)00309-9 · doi:10.1016/S0360-8352(96)00309-9
[11] DOI: 10.1016/S0272-6963(97)00029-6 · doi:10.1016/S0272-6963(97)00029-6
[12] Takeuchi H, Harvard Business Review pp 137– (1986)
[13] DOI: 10.1016/0165-0114(88)90054-1 · Zbl 0662.93066 · doi:10.1016/0165-0114(88)90054-1
[14] DOI: 10.1016/S0305-0548(01)00041-7 · Zbl 0994.90059 · doi:10.1016/S0305-0548(01)00041-7
[15] DOI: 10.1016/S0377-2217(98)00275-6 · Zbl 0998.90523 · doi:10.1016/S0377-2217(98)00275-6
[16] DOI: 10.1016/S0272-6963(99)00020-0 · doi:10.1016/S0272-6963(99)00020-0
[17] DOI: 10.1080/00207540010005079 · Zbl 0976.90500 · doi:10.1080/00207540010005079
[18] DOI: 10.1080/07408179308964291 · doi:10.1080/07408179308964291
[19] DOI: 10.1016/S0165-0114(97)00269-8 · Zbl 0931.62055 · doi:10.1016/S0165-0114(97)00269-8
[20] DOI: 10.1016/S0360-8352(98)00073-4 · doi:10.1016/S0360-8352(98)00073-4
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