Hostname: page-component-77c89778f8-swr86 Total loading time: 0 Render date: 2024-07-21T07:46:12.520Z Has data issue: false hasContentIssue false
  • English
  • Français

Product redesign considering the sensitivity of customer satisfaction

Published online by Cambridge University Press:  17 October 2022

Kaixin Sha ,
Yupeng Li Open the ORCID record for Yupeng Li [Opens in a new window] ,
Zhihua Zhao  and
Na Zhang
Kaixin Sha
Affiliation:
Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
Yupeng Li*
Affiliation:
Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
Zhihua Zhao
Affiliation:
Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
Na Zhang
Affiliation:
Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
*
Author for correspondence: Yupeng Li, E-mail: ypeng_li@163.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Redesign is a widespread strategy for product improvement whose essence is the optimization of design parameters (DPs) considering the trade-off between customer satisfaction and cost concerns. Similar to the relation between customer requirements (CRs) and customer satisfaction, the sensitivity of customer satisfaction is diverse to different DPs. In this study, a sensitivity-enhanced customer satisfaction function is defined for redesign model construction. This fills the research gap in product redesign that lacking of consideration and quantification of customer satisfaction sensitivity. First, a sensitivity index is defined based on Kano indices for analyzing sensitivity of customer satisfaction in different DP categories. Second, traditional customer satisfaction function has been improved by injecting the sensitivity of customer satisfaction to the variations of DPs. Subsequently, a DP optimization model is established to maximize shared surplus between customers and enterprise. Finally, a case study involving the redesign of a braking system of automobile is implemented to demonstrate the effectiveness and rationality of the proposed approach. The results show that the improved customer satisfaction function can reflect a more nuanced relationship between customer satisfaction and fulfilment level of DPs. Additionally, the proposed redesign model helps designers determine the target values of DPs under a better trade-off and enhances enterprise competitiveness.

Keywords

Customer satisfaction modelingdesign parameterproduct redesignsensitivity of customer satisfaction
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e31
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bi, J, Liu, Y, Fan, Z and Cambria, E (2019) Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. International Journal of Production Research 57, 7068–7088. doi:10.1080/00207543.2019.1574989CrossRefGoogle Scholar
Borchardt, M, Wendt, M, Pereira, G and Sellitto, M (2011) Redesign of a component based on ecodesign practices: environmental impact and cost reduction achievements. Journal of Cleaner Production 19, 49–57. doi:10.1016/j.jclepro.2010.08.006CrossRefGoogle Scholar
Bovea, M and Wang, B (2007) Redesign methodology for developing environmentally conscious products. International Journal of Production Research 45, 4057–4072. doi:10.1080/00207540701472678CrossRefGoogle Scholar
Cardin, M, Xie, Q, Ng, T, Wang, S and Hu, J (2016) An approach for analyzing and managing flexibility in engineering systems design based on decision rules and multistage stochastic programming. IISE Transactions 49, 1–12. doi:10.1080/0740817X.2016.1189627CrossRefGoogle Scholar
Chan, K, Kwong, C and Wong, T (2011) Modelling customer satisfaction for product development using genetic programming. Journal of Engineering Design 22, 55–68. doi:10.1080/09544820902911374CrossRefGoogle Scholar
Chen, C and Chuang, M (2008) Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design. International Journal of Production Economics 114, 667–681. doi:10.1016/j.ijpe.2008.02.015CrossRefGoogle Scholar
Dong, Y, Tan, R, Zhang, P, Peng, Q and Shao, P (2021) Product redesign using functional backtrack with digital twin. Advanced Engineering Informatics 49, 101361. doi:10.1016/j.aei.2021.101361CrossRefGoogle Scholar
Du, Y, Cao, H, Chen, X and Wang, B (2013) Reuse-oriented redesign method of used products based on axiomatic design theory and QFD. Journal of Cleaner Production 39, 79–86. doi:10.1016/j.jclepro.2012.08.032CrossRefGoogle Scholar
Farhadloo, M, Patterson, R and Rolland, E (2016) Modeling customer satisfaction from unstructured data using a Bayesian approach. Decision Support Systems 90, 1–11. doi:10.1016/j.dss.2016.06.010CrossRefGoogle Scholar
Felfernig, A and Schubert, M (2011) Personalized diagnoses for inconsistent user requirements. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 25, 175–183. doi:10.1017/S0890060410000612CrossRefGoogle Scholar
Florez-Lopez, R and Ramon-Jeronimo, J (2012) Managing logistics customer service under uncertainty: an integrative fuzzy Kano framework. Information Sciences 202, 41–57. doi:10.1016/j.ins.2012.03.004CrossRefGoogle Scholar
Fornell, C (1992) A national customer satisfaction barometer: the Swedish experience. Journal of Marketing 56, 6–21. doi:10.2307/1252129CrossRefGoogle Scholar
Fornell, C, Johnson, M, Anderson, E, Cha, J and Bryant, B (1996) The American Customer Satisfaction Index: nature, purpose, and findings. Journal of Marketing 60, 7–18. doi:10.2307/1251898CrossRefGoogle Scholar
Geng, X, Chu, X, Xue, D and Zhang, Z (2011) A systematic decision-making approach for the optimal product–service system planning. Expert Systems with Applications 38, 11849–11858. doi:10.1016/j.eswa.2011.03.075CrossRefGoogle Scholar
Gopsill, J, Snider, C, McMahon, C and Hicks, B (2016) Automatic generation of design structure matrices through the evolution of product models. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, 424–445. doi:10.1017/S0890060416000391CrossRefGoogle Scholar
He, L, Song, W, Wu, Z, Xu, Z, Zheng, M and Ming, X (2017) Quantification and integration of an improved Kano model into QFD based on multi-population adaptive genetic algorithm. Computers & Industrial Engineering 114, 183–194. doi:10.1016/j.cie.2017.10.009CrossRefGoogle Scholar
He, L, Wu, Z, Xiang, W, Goh, M and Wu, X (2021) A novel Kano-QFD-DEMATEL approach to optimise the risk resilience solution for sustainable supply chain. International Journal of Production Research 59, 1714–1735. doi:10.1080/00207543.2020.1724343CrossRefGoogle Scholar
Janz, D, Sihn, W and Warnecke, HJ (2005) Product redesign using value-oriented life cycle costing. CIRP Annals Manufacturing Technology 54, 9–12. doi:10.1016/s0007-8506(07)60038-9CrossRefGoogle Scholar
Jiang, H, Kwong, C, Siu, K and Liu, Y (2015) Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design. Advanced Engineering Informatics 29, 727–738. doi:10.1016/j.aei.2015.07.005CrossRefGoogle Scholar
Jiang, X, Song, B, Li, L, Dai, M and Zhang, H (2019) The customer satisfaction-oriented planning method for redesign parameters of used machine tools. International Journal of Production Research 57, 1146–1160. doi:10.1080/00207543.2018.1502483CrossRefGoogle Scholar
Jiao, J and Zhang, Y (2005) Product portfolio planning with customer-engineering interaction. IIE Transactions 37, 801–814. doi:10.1080/07408170590917011CrossRefGoogle Scholar
Kraus, U and Yano, C (2003) Product line selection and pricing under a share-of-surplus choice model. European Journal of Operational Research 150, 653–671. doi:10.1016/S0377-2217(02)00522-2CrossRefGoogle Scholar
Kristensen, K, Martensen, A and Gronholdt, L (2000) Customer satisfaction measurement at post demark: results of application of the European Customer Satisfaction Index methodology. Total Quality Management 11, 1007–1015. doi:10.1080/09544120050135533CrossRefGoogle Scholar
Kwong, C, Won, T and Chan, K (2009) A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications 36, 11262–11270. doi:10.1016/j.eswa.2009.02.094CrossRefGoogle Scholar
Lee, H, Lee, J and Seo, J (2011) Design and improvement of product using intelligent function model based cost estimating. Expert Systems with Applications 38, 3131–3141. doi:10.1016/j.eswa.2010.08.105CrossRefGoogle Scholar
Lee, Y, Wang, Y and Lin, S (2012) The reformed analytical Kano model. ICSSSM12. doi:10.1109/icsssm.2012.6252325CrossRefGoogle Scholar
Li, J, Zhang, X, Wang, K, Zheng, C, Tong, S and Eynard, B (2020) A personalized requirement identifying model for design improvement based on user profiling. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, 55–67. doi:10.1017/S0890060419000301CrossRefGoogle Scholar
Li, Y, Zhang, M and Chen, D (2021 a) Product obsolescence assessment based on the hybrid uncertain information axiom. Journal of Engineering Design 32, 375–396. doi:10.1080/09544828.2021.1913486CrossRefGoogle Scholar
Li, Y, Chen, H and Zhao, Z (2021 b) An integrated identification approach of agile engineering characteristics considering sensitive customer requirements. CIRP Journal of Manufacturing Science and Technology 35, 13–24. doi:10.1016/j.cirpj.2021.05.001CrossRefGoogle Scholar
Liu, E and Hsiao, S (2006) ANP-GP approach for product variety design. International Journal of Advanced Manufacturing Technology 29, 216–225.CrossRefGoogle Scholar
Mukhopadhyay, A and Ameri, F (2016) An ontological approach to engineering requirement representation and analysis. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, 337–352. doi:10.1017/S0890060416000330CrossRefGoogle Scholar
Paras, M, Wang, L, Chen, Y, Curteza, A, Pal, R and Ekwall, D (2018) A sustainable application based on grouping genetic algorithm for modularized redesign model in apparel reverse supply chain. Sustainability 10. doi:10.3390/su10093013CrossRefGoogle Scholar
Poel, I (2007) Methodological problems in QFD and directions for future development. Research in Engineering Design 18, 21–36. doi:10.1007/s00163-007-0029-7CrossRefGoogle Scholar
Ranjan, B, Siddharth, L and Chakrabarti, A (2018) A systematic approach to assessing novelty, requirement satisfaction, and creativity. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 390–414. doi:10.1017/S0890060418000148CrossRefGoogle Scholar
Shin, J, Kiritsis, D and Xirouchakis, P (2015) Design modification supporting method based on product usage data in closed-loop PLM. International Journal of Computer Integrated Manufacturing 28, 551–568. doi:10.1080/0951192X.2014.900866CrossRefGoogle Scholar
Smith, S, Smith, G and Shen, Y (2012) Redesign for product innovation. Design Studies 33, 160–184. doi:10.1016/j.destud.2011.08.003CrossRefGoogle Scholar
Uckun, S, Mackey, R, Do, M, Zhou, R, Huang, E and Shah, J (2014) Measures of product design adaptability for changing requirements. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, 353–368. doi:10.1017/S0890060414000523CrossRefGoogle Scholar
Wang, G (2021) Digital reframing: the design thinking of redesigning traditional products into innovative digital products. Journal of Product Innovation Management. Online. doi:10.1111/jpim.12605Google Scholar
Wang, Y, Elhag, T and Hua, Z (2006) A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process. Fuzzy Sets and Systems 157, 3055–3071. doi:10.1016/j.fss.2006.08.010CrossRefGoogle Scholar
Wang, D, Yu, H, Wu, J, Meng, Q and Lin, Q (2019 a) Integrating fuzzy based QFD and AHP for the design and implementation of a hand training device. Journal of Intelligent and Fuzzy Systems 36, 3317–3331. doi:10.3233/JIFS-181025CrossRefGoogle Scholar
Wang, W, Wei, T, Zhang, Y and Wang, Y (2019 b) A method of intelligent product design cue construction based on customer touchpoint correlation analysis and positive creativity theory. Advances in Mechanical Engineering 11, 1–11. doi:10.1177/1687814018819347Google Scholar
Xu, Q, Jiao, R, Yang, X, Helander, M, Khalid, H and Opperud, A (2009) An analytical Kano model for customer need analysis. Design Studies 30, 87–110. doi:10.1016/j.destud.2008.07.001CrossRefGoogle Scholar
Yan, W, Chen, C and Khoo, L (2002) An integrated approach to the elicitation of customer requirements for engineering design using picture sorts and fuzzy evaluation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, 59–71. doi:10.1017/S0890060402020061CrossRefGoogle Scholar
Yang, Q, Yu, S and Sekhari, A (2011) A modular eco-design method for life cycle engineering based on redesign risk control. The International Journal of Advanced Manufacturing Technology 56, 1215–1233.CrossRefGoogle Scholar
Ye, Y, Jankovic, M, Kremer, G and Bocquet, J (2014) Managing uncertainty in potential supplier identification. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, 339–351. doi:10.1017/S0890060414000511CrossRefGoogle Scholar
Zhang, Z and Chu, X (2009) Fuzzy group decision-making for multi-format and multi-granularity linguistic judgments in quality function deployment. Expert Systems with Applications 36, 9150–9158. doi:10.1016/j.eswa.2008.12.027CrossRefGoogle Scholar
Zhang, L, Chu, X and Xue, D (2019 a) Identification of the to-be-improved product features based on online reviews for product redesign. International Journal of Production Research 57, 2464–2479. doi:10.1080/00207543.2018.1521019CrossRefGoogle Scholar
Zhang, L, Chu, X, Chen, H and Yan, B (2019 b) A data-driven approach for the optimization of product specifications. International Journal of Production Research 57, 703–721. doi:10.1080/00207543.2018.1480843CrossRefGoogle Scholar
Zhang, X, Zhang, S, Zhang, L and Qiu, L (2021) Reverse design for remanufacture based on failure feedback and polychromatic sets. Journal of Cleaner Production 295. doi:10.1016/j.jclepro.2021.126355CrossRefGoogle Scholar
Zhong, S, Zhou, J and Chen, Y (2014) Determination of target values of engineering characteristics in QFD using a fuzzy chance-constrained modelling approach. Neurocomputing 142, 125–135. doi:10.1016/j.neucom.2014.01.052CrossRefGoogle Scholar
Zhu, G, Hu, J, Qi, J, He, T and Peng, Y (2017) Change mode and effects analysis by enhanced grey relational analysis under subjective environments. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, 207–221. doi:10.1017/S0890060417000099CrossRefGoogle Scholar
Zhu, S, Qi, J, Hu, J and Huang, H (2021) Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, 295–315. doi:10.1017/S0890060421000147CrossRefGoogle Scholar