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Exploring the Research Trend of Smart Factory with Topic Modeling

Author

Listed:
  • Hyun-Lim Yang

    (Department of Information and Communication Engineering, DGIST, Daegu 42988, Korea)

  • Tai-Woo Chang

    (Department of Industrial and Management Engineering/Intelligence and Manufacturing Research Center, Kyonggi University, Suwon, Gyeonggi 16227, Korea)

  • Yerim Choi

    (Department of Industrial and Management Engineering/Intelligence and Manufacturing Research Center, Kyonggi University, Suwon, Gyeonggi 16227, Korea)

Abstract

Growing competition among manufacturing businesses and the advent of the Fourth Industrial Revolution has meant that many countries are conducting various research projects to understand how to introduce and populate smart factories. Smart factories are expected to provide a way of solving the manufacturing industries’ complex problems, to take a role in breakthroughs in factories and to carry on a sustainable business. Smart factories are currently in the introduction stage, so we should follow up on the majorities and check their tendencies. However, smart-factory research is an interdisciplinary field that should be studied by researchers with diverse backgrounds in various domains. Thus, studying the past and present overall research trends of smart factory studies is required for their successful introduction and sustainable research. In this study, we explored the research trends of smart factories in both international and specifically Korean research, as an example of a nation case, to determine the major research directions. We determined trends using latent semantic analysis, which is a known topic-modeling technique, and analyzed the trends with regression-based methods. As a result, we could read the clear trends by analyzing existing studies related to smart factories. In addition, it is possible to compare research trends in Korea and international research trends for the commonly appeared topics, such as ‘ICT’ (Information and Communications Technology) and ‘R&D (Research and Development)/Technology Innovation’. We expect that the quantitative analysis results and suggestions presented in this study can be used to formulate strategies for the future diffusion of smart factories.

Suggested Citation

  • Hyun-Lim Yang & Tai-Woo Chang & Yerim Choi, 2018. "Exploring the Research Trend of Smart Factory with Topic Modeling," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2779-:d:162190
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    References listed on IDEAS

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    1. Gyusun Hwang & Jeongcheol Lee & Jinwoo Park & Tai-Woo Chang, 2017. "Developing performance measurement system for Internet of Things and smart factory environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2590-2602, May.
    2. Jeong, Do-Heon & Song, Min, 2014. "Time gap analysis by the topic model-based temporal technique," Journal of Informetrics, Elsevier, vol. 8(3), pages 776-790.
    3. Bongioanni, Irene & Daraio, Cinzia & Ruocco, Giancarlo, 2014. "A quantitative measure to compare the disciplinary profiles of research systems and their evolution over time," Journal of Informetrics, Elsevier, vol. 8(3), pages 710-727.
    4. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Jihong Chen & Kai Zhang & Yuan Zhou & Yufei Liu & Lingfeng Li & Zheng Chen & Li Yin, 2019. "Exploring the Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning," Sustainability, MDPI, vol. 11(12), pages 1-38, June.
    2. Thirupathi Samala & Vijaya Kumar Manupati & Bethalam Brahma Sai Nikhilesh & Maria Leonilde Rocha Varela & Goran Putnik, 2021. "Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status," Sustainability, MDPI, vol. 13(9), pages 1-20, May.
    3. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
    4. Xun Zhu & Timothy J. Pasch & Mohamed Aymane Ahajjam & Aaron Bergstrom, 2022. "Environmental Monitoring for Arctic Resiliency and Sustainability: An Integrated Approach with Topic Modeling and Network Analysis," Sustainability, MDPI, vol. 14(24), pages 1-20, December.

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