Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
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A Study on Process Management Method of Offshore Plant Piping Material using Process Mining Technique

프로세스 마이닝 기법을 이용한 해양플랜트 배관재 제작 공정 관리 방법에 관한 연구

  • Park, JungGoo (Ship & Offshore Research Institute, Samsung Heavy Industries) ;
  • Kim, MinGyu (Ship & Offshore Research Institute, Samsung Heavy Industries) ;
  • Woo, JongHun (Department of Naval Architecture & Ocean Engineering, Seoul National University)
  • 박중구 (삼성중공업(주) 조선해양연구소) ;
  • 김민규 (삼성중공업(주) 조선해양연구소) ;
  • 우종훈 (서울대학교 조선해양공학과)
  • Received : 2018.10.12
  • Accepted : 2018.11.28
  • Published : 2019.04.20
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Abstract

This study describes a method for analyzing log data generated in a process using process mining techniques. A system for collecting and analyzing a large amount of log data generated in the process of manufacturing an offshore plant piping material was constructed. The analyzed data was visualized through various methods. Through the analysis of the process model, it was evaluated whether the process performance was correctly input. Through the pattern analysis of the log data, it is possible to check beforehand whether the problem process occurred. In addition, we analyzed the process performance data of partner companies and identified the load of their processes. These data can be used as reference data for pipe production allocation. Real-time decision-making is required to cope with the various variances that arise in offshore plant production. To do this, we have built a system that can analyze the log data of real - time system and make decisions.

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References

  1. Kim, E.S., Kim, S. & Song, M.S., 2013. Discovery of outpatient care process of tertiary university hospital using process mining. Healthcare Informatics Research, 19(1) pp.42-49. https://doi.org/10.4258/hir.2013.19.1.42
  2. Lambert, D.M., Cooper, M.C. & Pagh, J.D., 1998. Supply chain management: Implementation issues and research opportunities. The International Journal of Logistics Management, 9(2), pp.1-29. https://doi.org/10.1108/09574099810805807
  3. Lee, D.H., Park, J.H. & Bae, H.R., 2013. Comparison between planned and actual data of block assembly process using process mining in shipyard. Journal of Society for e-Business Studies, 18(4), pp.145-167. https://doi.org/10.7838/jsebs.2013.18.4.145
  4. Lee, S.Y., Ryu, K.Y. & Song, M.S., 2012. Process improvement for PDM/PLM systems by using process mining. Korean Journal of Computational Design and Engineering, 17, pp.294-302. https://doi.org/10.7315/CADCAM.2012.294
  5. Lee, Y.H., Lee, H.J., Song, M.S., Lee, S.J. & Park, S.R., 2016. Process analysis in supply chain management with process mining : A case study. Journal of Korea Bigdata Society, 1(2), pp.65-78.
  6. Song, M., Gunther C. W. & van der Aalst, W. M. P., 2009. Trace clustering in process mining. BPM 2008 Workshops, Lecture Notes in Business Information Processing, 17, pp.109-120.