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Design and Verification of Process Discovery Based on NLP Approach and Visualization for Manufacturing Industry

Author

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  • Junhyung Moon

    (Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea)

  • Gyuyoung Park

    (Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea)

  • Minyeol Yang

    (Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea)

  • Jongpil Jeong

    (Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea)

Abstract

When a consultant of a company that provides a smart factory solution consults with a customer, it is difficult to define the outline of the manufacturing process and create all activities within the process by case. It requires a large amount of resources from the company to perform a task. In this study, we propose a process discovery automation system that helps consultants define manufacturing processes. In addition, for process discovery, a fully attention-based transformer model, which has recently shown a strong performance, was applied. To be useful to consultants, we solved the black box characteristics of the deep learning model applied to process discovery and proposed a visualization method that can be used in the monitoring system when explaining the discovery process. In this study, we used the event log of the metal fabrication process to perform the modeling, visualization, and evaluation.

Suggested Citation

  • Junhyung Moon & Gyuyoung Park & Minyeol Yang & Jongpil Jeong, 2022. "Design and Verification of Process Discovery Based on NLP Approach and Visualization for Manufacturing Industry," Sustainability, MDPI, vol. 14(3), pages 1-27, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1103-:d:727969
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    References listed on IDEAS

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    1. Jiexun Li & Harry Jiannan Wang & Xue Bai, 2015. "An intelligent approach to data extraction and task identification for process mining," Information Systems Frontiers, Springer, vol. 17(6), pages 1195-1208, December.
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