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research-article

Quantification and integration of an improved Kano model into QFD based on multi-population adaptive genetic algorithm

Authors:

Xinguo MingAuthors Info & Claims

Computers and Industrial Engineering, Volume 114, Issue C

Pages 183 - 194

https://doi.org/10.1016/j.cie.2017.10.009

Published: 01 December 2017 Publication History

Abstract

A Kano model based on customer requirement frequency and importance is proposed.The refined Kano model and QFD are integrated with an optimization model.A multi-population adaptive genetic algorithm is designed.Appropriate Kano categories and engineering characteristics target value are selected. In an effort to address the inherent deficiencies of traditional Kano model and quality function deployment (QFD), this paper proposes an improved Kano model named as importance-frequency Kano (IF-Kano) model and integrates IF-Kano model into QFD. Considering the interaction between frequencies and importance weights of customer requirements (CRs), the IF-Kano model adopts the logical Kano classification criteria to categorize CRs. Then, both qualitative and quantitative results derived from IF-Kano model are integrated into QFD with a non-linear programming model. The model aims to determine appropriate Kano categories of CRs and target values of engineering characteristics (ECs) with a view to achieving an optimal design solution under the best balance between enterprise satisfaction and customer satisfaction (CS). To solve the presented model, a multi-population adaptive genetic algorithm (MPAGA) is designed. Finally, an example of a home elevator design is given to demonstrate the feasibility and effectiveness of the developed approach and algorithm.

References

[1]

K. Altun, T. Dereli, Even easier multi-issue negotiation through Modified Even-Swaps considering practically dominated alternatives, Computers & industrial engineering, 76 (2014) 307-317.

Digital Library

[2]

K. Altun, T. Dereli, A. BaykasoLu, Development of a framework for customer co-creation in NPD through multi-issue negotiation with issue trade-offs, Expert Systems with Applications, 40 (2013) 873-880.

Digital Library

[3]

K. Altun, M. von Zedtwitz, T. Dereli, Multi-issue negotiation in quality function deployment: Modified Even-Swaps in new product development, Computers & industrial engineering, 92 (2016) 31-49.

Digital Library

[4]

C. Berger, R. Blauth, D. Boger, C. Bolster, G. Burchill, W. DuMouchel, D. Shen, Kano's methods for understanding customer-defined quality, Center for Quality Management Journal, 2 (1993) 3-35.

[5]

Blecker, T., Abdelkafi, N., Kreutler, G. & Friedrich, G. (2004). Product configuration systems: state of the art, conceptualization and extensions.

[6]

Blickle, T. & Thiele, L. (1995). A comparison of selection schemes used in genetic algorithms. In: TIK-Report.

[7]

A. Chaudha, R. Jain, A.R. Singh, P.K. Mishra, Integration of Kano's Model into quality function deployment (QFD), The International Journal of Advanced Manufacturing Technology, 53 (2011) 689-698.

[8]

C.-T. Chen, Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems, 114 (2000) 1-9.

Digital Library

[9]

L.-F. Chen, A novel approach to regression analysis for the classification of quality attributes in the Kano model: an empirical test in the food and beverage industry, Omega, 40 (2012) 651-659.

[10]

C.-C. Chen, M.-C. Chuang, Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design, International journal of production economics, 114 (2008) 667-681.

[11]

J.J. Cristiano, J.K. Liker, C.C. White, Key factors in the successful application of quality function deployment (QFD), IEEE Transactions on Engineering Management, 48 (2001) 81-95.

[12]

E.K. Delice, Z. Gngr, A new mixed integer linear programming model for product development using quality function deployment, Computers & Industrial Engineering, 57 (2009) 906-912.

Digital Library

[13]

T. Dereli, K. Altun, Modified Even-Swaps: A novel, clear, rational and an easy-to-use mechanism for multi-issue negotiation, Computers & Industrial Engineering, 63 (2012) 1013-1029.

Digital Library

[14]

R. Florez-Lopez, J.M. Ramon-Jeronimo, Managing logistics customer service under uncertainty: An integrative fuzzy Kano framework, Information Sciences, 202 (2012) 41-57.

Digital Library

[15]

R.Y. Fung, Y. Chen, L. Chen, J. Tang, A fuzzy expected value-based goal programing model for product planning using quality function deployment, Engineering Optimization, 37 (2005) 633-645.

[16]

D.-W. Gong, G.-S. Hao, Y. Zhou, X.-Y. Sun, Interactive genetic algorithms with multi-population adaptive hierarchy and their application in fashion design, Applied Mathematics and Computation, 185 (2007) 1098-1108.

Digital Library

[17]

He, L., Ming, X., Li, M., Zheng, M. & Xu, Z. (2015). Understanding customer requirements through quantitative analysis of an improved fuzzy Kanos model. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 0954405415598894.

[18]

Ji, P., Jin, J., Wang, T. & Chen, Y. (2014). Quantification and integration of Kanos model into QFD for optimising product design. International journal of production research(ahead-of-print), 114.

[19]

G. Jia, M. Bai, An approach for manufacturing strategy development based on fuzzy-QFD, Computers & Industrial Engineering, 60 (2011) 445-454.

Digital Library

[20]

Kano, N. (2001). Life cycle and creation of attractive quality. In: 4th QMOD Conference, Linkping, Sweden (pp. 1836).

[21]

N. Kano, N. Seraku, F. Takahashi, S. Tsuji, Attractive quality and must-be quality, The Journal of the Japanese Society for Quality Control, 14 (1984) 39-48.

[22]

C. Kwong, Y. Chen, K.Y. Chan, A methodology of integrating marketing with engineering for defining design specifications of new products, Journal of Engineering Design, 22 (2011) 201-213.

[23]

Lai, X., Xie, M. & Tan, K. (2004). Optimizing product design using the Kano model and QFD. In: Engineering Management Conference, 2004. Proceedings. 2004 IEEE International (Vol. 3, pp. 10851089): IEEE.

[24]

Y.-C. Lee, L.-C. Sheu, Y.-G. Tsou, Quality function deployment implementation based on Fuzzy Kano model: An application in PLM system, Computers & industrial engineering, 55 (2008) 48-63.

Digital Library

[25]

Y.-L. Li, M. Huang, K.-S. Chin, X.-G. Luo, Y. Han, Integrating preference analysis and balanced scorecard to product planning house of quality, Computers & industrial engineering, 60 (2011) 256-268.

Digital Library

[26]

Y. Li, J. Tang, X. Luo, J. Xu, An integrated method of rough set, Kanos model and AHP for rating customer requirements final importance, Expert Systems with Applications, 36 (2009) 7045-7053.

Digital Library

[27]

X. Luo, C. Kwong, J. Tang, Determining optimal levels of engineering characteristics in quality function deployment under multi-segment market, Computers & industrial engineering, 59 (2010) 126-135.

Digital Library

[28]

X. Luo, C. Kwong, J. Tang, F. Sun, QFD-based product planning with consumer choice analysis, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45 (2015) 454-461.

[29]

K. Matzler, H.H. Hinterhuber, How to make product development projects more successful by integrating Kano's model of customer satisfaction into quality function deployment, Technovation, 18 (1998) 25-38.

[30]

Michalewicz, Z. (1996). Genetic algorithms+ data structures= evolution programs: springer.

Digital Library

[31]

Mizuno, S. & Akao, Y. (1990). Quality function deployment: Integrating customer requirements into product design.

[32]

Y.-E. Nahm, A novel approach to prioritize customer requirements in QFD based on customer satisfaction function for customer-oriented product design, Journal of Mechanical Science and Technology, 27 (2013) 3765-3777.

[33]

Y. Sireli, P. Kauffmann, E. Ozan, Integration of Kano's model into QFD for multiple product design, IEEE Transactions on Engineering Management, 54 (2007) 380-390.

[34]

W. Song, X. Ming, Z. Xu, Integrating Kano model and greyMarkov chain to predict customer requirement states, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227 (2013) 1232-1244.

[35]

R.B. Stone, R. Kurtadikar, N. Villanueva, C.B. Arnold, A customer needs motivated conceptual design methodology for product portfolio planning, Journal of Engineering Design, 19 (2008) 489-514.

[36]

G. Tontini, Integrating the Kano model and QFD for designing new products, Total Quality Management, 18 (2007) 599-612.

[37]

C.-H. Wang, Incorporating customer satisfaction into the decision-making process of product configuration: a fuzzy Kano perspective, International Journal of Production Research, 51 (2013) 6651-6662.

[38]

T. Wang, P. Ji, Understanding customer needs through quantitative analysis of Kano's model, International Journal of Quality & Reliability Management, 27 (2010) 173-184.

[39]

G.S. Wasserman, On how to prioritize design requirements during the QFD planning process, IIE Transactions, 25 (1993) 59-65.

[40]

M. Wu, L. Wang, A continuous fuzzy Kanos model for customer requirements analysis in product development, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 226 (2012) 535-546.

[41]

Q. Xu, R.J. Jiao, X. Yang, M. Helander, H.M. Khalid, A. Opperud, An analytical Kano model for customer need analysis, Design Studies, 30 (2009) 87-110.

[42]

J.Y. Yang, J. Yoo, Mixed Integer Linear Programming Model to Determine the Optimal Levels of Technical Attributes in QFD under Multi-Segment Market, Korean Management Science Review, 33 (2016) 75-87.

[43]

S. Zhong, J. Zhou, Y. Chen, Determination of target values of engineering characteristics in QFD using a fuzzy chance-constrained modelling approach, Neurocomputing (2014).

Digital Library

[44]

R. Zwick, E. Carlstein, D.V. Budescu, Measures of similarity among fuzzy concepts: A comparative analysis, International Journal of Approximate Reasoning, 1 (1987) 221-242.

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Published In

cover image Computers and Industrial Engineering

Computers and Industrial Engineering Volume 114, Issue C

December 2017

337 pages

ISSN:0360-8352

Issue’s Table of Contents

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 December 2017

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Cited By

Min QZhaoxian RJiang W(2023)An integrated approach to design and evaluate Chinese-style stoolsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23258045:5(8297-8316)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-232580
Ho SChuang W(2023)Identifying and prioritizing the critical quality attributes for business-to-business cross-border electronic commerce platformsElectronic Commerce Research and Applications10.1016/j.elerap.2023.10123958:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.elerap.2023.101239
Karasan AIlbahar ECebi SKahraman C(2022)Customer-oriented product design using an integrated neutrosophic AHP & DEMATEL & QFD methodologyApplied Soft Computing10.1016/j.asoc.2022.108445118:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.108445
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Wang JLi RDing GQin SCai Z(2022)Product-service system engineering characteristics design for life cycle cost based on constraint satisfaction problem and Bayesian networkAdvanced Engineering Informatics10.1016/j.aei.2022.10157352:COnline publication date: 1-Apr-2022
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Shi YPeng Q(2021)Enhanced customer requirement classification for product design using big data and improved Kano modelAdvanced Engineering Informatics10.1016/j.aei.2021.10134049:COnline publication date: 1-Aug-2021
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