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Improving the functional performances for product family by mining online reviews

Author

Listed:
  • Chao He

    (China University of Mining and Technology)

  • Zhongkai Li

    (China University of Mining and Technology)

  • Dengzhuo Liu

    (China University of Mining and Technology)

  • Guangyu Zou

    (China University of Mining and Technology)

  • Shuai Wang

    (China University of Mining and Technology)

Abstract

Companies continuously perfect product performances directing at consumers’ feedback, seeking to enhance customer satisfaction and product competitiveness. To make up for the insufficiency of previous research on product family performance improvement, a method applies multiple data-mining techniques to dig out online reviews is put forward to quantify the improvement priority of each performance in the product family, so as to guide product family improvement. Web Crawler is employed to collect customer reviews of various product variants, and then natural language processing technology is utilized to identify the words expressing functional performances and customer sentiments in the reviews, where the term frequency of each performance is defined as its importance factor. The mapping model between performance specifications and module instances in the product family is established to obtain the commonality factor of each performance. Lexicon-based machine learning is exploited to analyze customers’ sentimental values for each performance specification, which is regarded as satisfaction factor. According to the importance and satisfaction of each performance, Kano coefficient is assigned to each performance by utilizing the Kano model. Finally, combined the three factors and Kano coefficient, the improvement priority of each performance specification is estimated to suggest the enterprise to plan the resource allocation for product family improvement. The feasibility of the proposed method is demonstrated by performance improvement for sweeping robot product family and comparison with traditional questionnaire method.

Suggested Citation

  • Chao He & Zhongkai Li & Dengzhuo Liu & Guangyu Zou & Shuai Wang, 2023. "Improving the functional performances for product family by mining online reviews," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2809-2824, August.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:6:d:10.1007_s10845-022-01961-w
    DOI: 10.1007/s10845-022-01961-w
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    References listed on IDEAS

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    1. 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.
    2. Yadav, Om Prakash & Goel, Parveen S., 2008. "Customer satisfaction driven quality improvement target planning for product development in automotive industry," International Journal of Production Economics, Elsevier, vol. 113(2), pages 997-1011, June.
    3. Peter E. Harland & Zakir Uddin & Sven Laudien, 2020. "Product platforms as a lever of competitive advantage on a company-wide level: a resource management perspective," Review of Managerial Science, Springer, vol. 14(1), pages 137-158, February.
    4. Jian Jin & Ying Liu & Ping Ji & Hongguang Liu, 2016. "Understanding big consumer opinion data for market-driven product design," International Journal of Production Research, Taylor & Francis Journals, vol. 54(10), pages 3019-3041, May.
    5. Lei Zhang & Xuening Chu & Deyi Xue, 2019. "Identification of the to-be-improved product features based on online reviews for product redesign," International Journal of Production Research, Taylor & Francis Journals, vol. 57(8), pages 2464-2479, April.
    6. A. M. M. Sharif Ullah & Jun'ichi Tamaki, 2011. "Analysis of Kano‐model‐based customer needs for product development," Systems Engineering, John Wiley & Sons, vol. 14(2), pages 154-172, June.
    7. Chen, Chun-Chih & Chuang, Ming-Chuen, 2008. "Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design," International Journal of Production Economics, Elsevier, vol. 114(2), pages 667-681, August.
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