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Iterative product design through group opinion evolution

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  • Runliang Dou
  • Yubo Zhang
  • Guofang Nan

Abstract

More and more industrial products have begun to adopt iterative design modes. Opinions collected from all sides in each round of iterations are used as bases to make products that are able to capture the changes of customer demand dynamically and to respond to these changes in a timely manner. In this context, it becomes important for product designers and manufacturers to study the evolution of opinions as well as the product scheme along with design iteration to determine its regularity. This paper integrates the Hegselmann–Krause model into product improvement process, and develops a dynamic model from the view of opinion dynamics. The dynamic model simultaneously converges the evolution of customers’ opinions, professional designers’ opinions and the product scheme. The paper also analyses the interaction mechanism among different opinions as well as the mechanism between opinions and the product scheme. The experimental results determine that the product scheme, customers’ opinions and professional designers’ opinions can demonstrate differently but regular evolution patterns along with the design iteration according to the difference in confidence intervals or the difference caused by tending to adopt a more customer-driven design strategy or a more professional designer-driven design strategy. Finally, reasonable suggestions are provided to help manufacturers make product design strategies.

Suggested Citation

  • Runliang Dou & Yubo Zhang & Guofang Nan, 2017. "Iterative product design through group opinion evolution," International Journal of Production Research, Taylor & Francis Journals, vol. 55(13), pages 3886-3905, July.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:13:p:3886-3905
    DOI: 10.1080/00207543.2017.1316020
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    Cited by:

    1. Fan Zou & Yupeng Li & Jiahuan Huang, 2022. "Group interaction and evolution of customer reviews based on opinion dynamics towards product redesign," Electronic Commerce Research, Springer, vol. 22(4), pages 1131-1151, December.
    2. Yao Jiao & Yu Yang, 2019. "A product configuration approach based on online data," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2473-2487, August.
    3. Shuangyao Zhao & Qiang Zhang & Zhanglin Peng & Yu Fan, 2020. "Integrating customer requirements into customized product configuration design based on Kano’s model," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 597-613, March.
    4. Biswas, Sumana & Ali, Ismail & Chakrabortty, Ripon K. & Turan, Hasan Hüseyin & Elsawah, Sondoss & Ryan, Michael J., 2022. "Dynamic modeling for product family evolution combined with artificial neural network based forecasting model: A study of iPhone evolution," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    5. Percia David, Dimitri & Maréchal, Loïc & Lacube, William & Gillard, Sébastien & Tsesmelis, Michael & Maillart, Thomas & Mermoud, Alain, 2023. "Measuring security development in information technologies: A scientometric framework using arXiv e-prints," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    6. Xianghua Chu & Su Xiu Xu & Fulin Cai & Jiansheng Chen & Quande Qin, 2019. "An efficient auction mechanism for regional logistics synchronization," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2715-2731, October.

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