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An Integrated Method to Acquire Technological Evolution Potential to Stimulate Innovative Product Design

Author

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  • Peng Shao

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
    National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin 300401, China)

  • Runhua Tan

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
    National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin 300401, China)

  • Qingjin Peng

    (Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

  • Wendan Yang

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
    National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin 300401, China)

  • Fang Liu

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
    National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin 300401, China)

Abstract

Fast and effective forecasting of the new generation of products is key to enhancing the competitiveness of a company in the market. Although the technological evolution laws in the theory of the solution of inventive problems (TRIZ) have been used to predict the potential states of products for innovation, there is a lack of effective methods to select the best technological evolution law consistently with product replacement and update, and acquiring potentially new technologies and solutions, which relies heavily on designers’ experience and makes it impossible for designers to efficiently use the technological evolution laws to stimulate product innovation. Aimed to bridge this gap, this paper proposes an integrated method consisting of three main steps, combining the technological evolution laws with back propagation neural network (BPNN), international patent classification (IPC) knowledge and company’s technological distance. The best technical evolution law is first searched by a BPNN. The functional verbs and effects in the IPC are then extracted and searched for potential technologies in the Spyder-integrated development environment. Finally, the company’s technological distance is used to select analogous sources of potential solutions in the patent database. The final innovative design is determined based on the ideality. The proposed method is applied in the development of a steel pipe-cutting machine to verify its feasibility. The proposed method reduces the dependence on designers’ experience and provides a way to access cross-domain technologies, providing a systematic approach for the technological evolution laws to motivate innovative product design.

Suggested Citation

  • Peng Shao & Runhua Tan & Qingjin Peng & Wendan Yang & Fang Liu, 2023. "An Integrated Method to Acquire Technological Evolution Potential to Stimulate Innovative Product Design," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:619-:d:1047291
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    1. Vicente-Gomila, J.M. & Artacho-Ramírez, M.A. & Ting, Ma & Porter, A.L., 2021. "Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Fey,Victor & Rivin,Eugene, 2005. "Innovation on Demand," Cambridge Books, Cambridge University Press, number 9780521826204, September.
    3. Nooteboom, Bart & Van Haverbeke, Wim & Duysters, Geert & Gilsing, Victor & van den Oord, Ad, 2007. "Optimal cognitive distance and absorptive capacity," Research Policy, Elsevier, vol. 36(7), pages 1016-1034, September.
    4. Huang, Hung-Chun & Su, Hsin-Ning, 2019. "The innovative fulcrums of technological interdisciplinarity: An analysis of technology fields in patents," Technovation, Elsevier, vol. 84, pages 59-70.
    5. Lee, Ching-Hung & Li, Li & Li, Fan & Chen, Chun-Hsien, 2022. "Requirement-driven evolution and strategy-enabled service design for new customized quick-response product order fulfillment process," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    6. Ellen Enkel & Annika Groemminger & Sebastian Heil, 2018. "Managing technological distance in internal and external collaborations: absorptive capacity routines and social integration for innovation," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1257-1290, October.
    7. Shaobo Li & Jie Hu & Yuxin Cui & Jianjun Hu, 2018. "DeepPatent: patent classification with convolutional neural networks and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 721-744, November.
    8. Xubao Liu & Yuhang Pan & Ying Yan & Yonghao Wang & Ping Zhou, 2022. "Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters," Mathematics, MDPI, vol. 10(15), pages 1-18, August.
    9. Kok, Holmer & Faems, Dries & de Faria, Pedro, 2020. "Ties that matter: The impact of alliance partner knowledge recombination novelty on knowledge utilization in R&D alliances," Research Policy, Elsevier, vol. 49(7).
    10. Jing Guo & Qingjin Peng & Liyan Zhang & Runhua Tan & Jianyu Zhang, 2021. "Estimation of product success potential using product value," International Journal of Production Research, Taylor & Francis Journals, vol. 59(18), pages 5609-5625, September.
    11. Jiawei Shi & Yan Zhou, 2022. "Group Decision Making for Product Innovation Based on PZB Model in Fuzzy Environment: A Case from New-Energy Storage Innovation Design," Mathematics, MDPI, vol. 10(19), pages 1-26, October.
    12. Robin Cowan & Nicolas Jonard & Jean-Benoit Zimmermann, 2007. "Bilateral Collaboration and the Emergence of Innovation Networks," Management Science, INFORMS, vol. 53(7), pages 1051-1067, July.
    13. Subramanian, Annapoornima M. & Bo, Wang & Kah-Hin, Chai, 2018. "The role of knowledge base homogeneity in learning from strategic alliances," Research Policy, Elsevier, vol. 47(1), pages 158-168.
    14. de Guimarães, Julio Cesar Ferro & Severo, Eliana Andréa & Jabbour, Charbel Jose Chiappetta & de Sousa Jabbour, Ana Beatriz Lopes & Rosa, Ariane Ferreira Porto, 2021. "The journey towards sustainable product development: why are some manufacturing companies better than others at product innovation?," Technovation, Elsevier, vol. 103(C).
    15. Peter J. Lane & Michael Lubatkin, 1998. "Relative absorptive capacity and interorganizational learning," Post-Print hal-02311860, HAL.
    16. Gilberto Borrego & Samuel González-López & Ramón R. Palacio, 2022. "Tags’ Recommender to Classify Architectural Knowledge Applying Language Models," Mathematics, MDPI, vol. 10(3), pages 1-26, January.
    17. Tinggui Chen & Lijuan Peng & Jianjun Yang & Guodong Cong, 2021. "Analysis of User Needs on Downloading Behavior of English Vocabulary APPs Based on Data Mining for Online Comments," Mathematics, MDPI, vol. 9(12), pages 1-26, June.
    18. Sampaio, Priscila Gonçalves Vasconcelos & González, Mario Orestes Aguirre & de Vasconcelos, Rafael Monteiro & dos Santos, Marllen Aylla Teixeira & de Toledo, José Carlos & Pereira, Jonathan Paulo Pinh, 2018. "Photovoltaic technologies: Mapping from patent analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 215-224.
    19. Zhu, Shanshan & Hagedoorn, John & Zhang, Shuhui & Liu, Fengchao, 2021. "Effects of technological distance on innovation performance under heterogeneous technological orientations," Technovation, Elsevier, vol. 106(C).
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    1. Dengke Li & Shiwen Chen & Yingmou Zhu & Ang Qiu & Zhiyuan Liao & Xiaodong Liu & Longjiang Shen & Guiyu Jian, 2023. "Application of Algorithm for Inventive Problem Solving (ARIZ) for the Heat Dissipation of Energy Storage Supply System for High-Power Locomotive," Sustainability, MDPI, vol. 15(9), pages 1-23, April.

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