IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7566-d1139717.html
   My bibliography  Save this article

Acquisition Method of User Requirements for Complex Products Based on Data Mining

Author

Listed:
  • Juan Hao

    (School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
    Shaanxi Modern Equipment Green Manufacturing Collaborative Innovation Center, Xi’an University of Technology, Xi’an 710048, China)

  • Xinqin Gao

    (School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
    Shaanxi Modern Equipment Green Manufacturing Collaborative Innovation Center, Xi’an University of Technology, Xi’an 710048, China)

  • Yong Liu

    (School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
    Shaanxi Modern Equipment Green Manufacturing Collaborative Innovation Center, Xi’an University of Technology, Xi’an 710048, China)

  • Zhoupeng Han

    (School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
    Shaanxi Modern Equipment Green Manufacturing Collaborative Innovation Center, Xi’an University of Technology, Xi’an 710048, China)

Abstract

The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility.

Suggested Citation

  • Juan Hao & Xinqin Gao & Yong Liu & Zhoupeng Han, 2023. "Acquisition Method of User Requirements for Complex Products Based on Data Mining," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7566-:d:1139717
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7566/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/9/7566/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jongchan Kim & Joonhyuck Lee & Gabjo Kim & Sangsung Park & Dongsik Jang, 2016. "A Hybrid Method of Analyzing Patents for Sustainable Technology Management in Humanoid Robot Industry," Sustainability, MDPI, vol. 8(5), pages 1-14, May.
    2. Weiya Chen & Xiaoqi Shi & Xiaoping Fang & Yongzhuo Yu & Shiying Tong, 2023. "Research Context and Prospect of Green Railways in China Based on Bibliometric Analysis," Sustainability, MDPI, vol. 15(7), pages 1-12, March.
    3. F. Tao & Y. Cheng & L. Zhang & A. Y. C. Nee, 2017. "Advanced manufacturing systems: socialization characteristics and trends," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1079-1094, June.
    4. Yuguo Jiang & Min Li & Asante Dennis & Xin Liao & Enock Mintah Ampaw, 2022. "The Hotspots and Trends in the Literature on Cleaner Production: A Visualized Analysis Based on Citespace," Sustainability, MDPI, vol. 14(15), pages 1-18, July.
    5. Hao Li & Shanghua Mi & Qifeng Li & Xiaoyu Wen & Dongping Qiao & Guofu Luo, 2020. "A scheduling optimization method for maintenance, repair and operations service resources of complex products," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1673-1691, October.
    6. Yuanzhu Zhan & Kim Hua Tan & Baofeng Huo, 2019. "Bridging customer knowledge to innovative product development: a data mining approach," International Journal of Production Research, Taylor & Francis Journals, vol. 57(20), pages 6335-6350, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Keeheon Lee, 2021. "A Systematic Review on Social Sustainability of Artificial Intelligence in Product Design," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    2. Uwizeyemungu, Sylvestre & Poba-Nzaou, Placide & St-Pierre, Josée, 2022. "Back-end information technology resources and manufacturing SMEs’ export commitment: An empirical investigation," International Business Review, Elsevier, vol. 31(5).
    3. Vendrell-Herrero, Ferran & Bustinza, Oscar F. & Opazo-Basaez, Marco, 2021. "Information technologies and product-service innovation: The moderating role of service R&D team structure," Journal of Business Research, Elsevier, vol. 128(C), pages 673-687.
    4. Wei Wang & Dechao Ma & Fengzhi Wu & Mengxin Sun & Shuangqing Xu & Qiuyue Hua & Ziyuan Sun, 2023. "Exploring the Knowledge Structure and Hotspot Evolution of Greenwashing: A Visual Analysis Based on Bibliometrics," Sustainability, MDPI, vol. 15(3), pages 1-35, January.
    5. Gedas Baranauskas & Agota Giedrė Raišienė & Renata Korsakienė, 2020. "Mapping the Scientific Research on Mass Customization Domain: A Critical Review and Bibliometric Analysis," JRFM, MDPI, vol. 13(9), pages 1-20, September.
    6. Xuhui Xia & Wei Liu & Zelin Zhang & Lei Wang & Jianhua Cao & Xiang Liu, 2019. "A Balancing Method of Mixed-model Disassembly Line in Random Working Environment," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    7. Bustinza, Oscar F. & Opazo-Basaez, Marco & Tarba, Shlomo, 2022. "Exploring the interplay between Smart Manufacturing and KIBS firms in configuring product-service innovation performance," Technovation, Elsevier, vol. 118(C).
    8. Daozhi Zhao & Yang Xue & Cejun Cao & Hongshuai Han, 2019. "Channel Selection and Pricing Decisions Considering Three Charging Modes of Production Capacity Sharing Platform: A Sustainable Operations Perspective," Sustainability, MDPI, vol. 11(21), pages 1-28, October.
    9. Vishwakarma, Laxmi Pandit & Singh, Rajesh Kr & Mishra, Ruchi & Demirkol, Denizhan & Daim, Tugrul, 2024. "The adoption of social robots in service operations: A comprehensive review," Technology in Society, Elsevier, vol. 76(C).
    10. Quan Xiao & Shanshan Wan & Fucai Lu & Shun Li, 2019. "Risk Assessment for Engagement in Sharing Economy of Manufacturing Enterprises: A Matter–Element Extension Based Approach," Sustainability, MDPI, vol. 11(17), pages 1-29, September.
    11. Mu-Jung Huang & Kuo-Chih Cheng & Ching-Ju Huang & Kun-Meng Lin & Huo-Ming Wang & Cheng-Kuo Chuang & Ming-Cheng Wu, 2021. "Establishing a Dynamic Capital Structure Model for Company Sustainability Performance Using Data Mining Techniques," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    12. Federica Costa & Alberto Portioli-Staudacher, 2021. "Labor flexibility integration in workload control in Industry 4.0 era," Operations Management Research, Springer, vol. 14(3), pages 420-433, December.
    13. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    14. Kwabena Abrokwah-Larbi, 2024. "The nexus between customer value analytics and SME performance in emerging market: a resource-based view perspective," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 14(1), pages 1-20, December.
    15. Shi‐Xiao Wang & Wen‐Min Lu & Shiu‐Wan Hung, 2020. "Improving innovation efficiency of emerging economies: The role of manufacturing," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(4), pages 503-519, June.
    16. Yimeng Jin & Fei Hu & Jin Qi, 2022. "Multidimensional Characteristics and Construction of Classification Model of Prosumers," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    17. Savin, Ivan & Ott, Ingrid & Konop, Chris, 2022. "Tracing the evolution of service robotics: Insights from a topic modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    18. Denitsa ZHECHEVA & Nayden NENKOV, 2022. "Business demands for processing unstructured textual data – text mining techniques for companies to implement," Access Journal, Access Press Publishing House, vol. 3(2), pages 107-120, April.
    19. Beatriz Ferreira & Carla Curado & Mírian Oliveira, 2022. "The Contribution of Knowledge Management to Human Resource Development: a Systematic and Integrative Literature Review," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(3), pages 2319-2347, September.
    20. Bonomi, Sabrina & Sarti, Daria & Torre, Teresina, 2020. "Creating a collaborative network for welfare services in public sector. A knowledge-based perspective," Journal of Business Research, Elsevier, vol. 112(C), pages 440-449.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7566-:d:1139717. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.