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Manufacturing process improvement of offshore plant: Process mining technique and case study

  • Shin, Sung-chul (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Kim, Seon Yeob (Department of Marine engineering, Gyeongsang National University) ;
  • Noh, Chun-Myoung (Department of Ocean System engineering, Gyeongsang National University) ;
  • Lee, Soon-sup (Department of Ocean System engineering, Gyeongsang National University) ;
  • Lee, Jae-chul (Department of Ocean System engineering, Gyeongsang National University)
  • Received : 2019.01.09
  • Accepted : 2019.05.03
  • Published : 2019.09.25

Abstract

The shipbuilding industry is characterized by order production, and various processes are performed simultaneously in the construction of ships. Therefore, effective management of the production process and productivity improvement form important key factors in the industry. For decades, researchers and process managers have attempted to improve processes by using business process analysis (BPA). However, conventional BPA is time-consuming, expensive, and mainly based on subjective results generated by employees, which may not always correspond to the actual conditions. This paper proposes a method to improve the production process of offshore plant modules by analysing the process mining data obtained from the shipbuilding industry. Process mining uses information accumulated from the system-provided event logs to generate a process model and determine the values hidden within the process. The discovered process is visualized as a process model. Subsequently, alternatives are proposed by brainstorming problems (such as bottlenecks or idle time) in the process. The results of this study can aid in productivity improvement (idle time or bottleneck reduction in the production process) in conjunction with a six-sigma technique or ERP system. In future, it is necessary to study the standardization of the module production processes and development of the process monitoring system.

Keywords

Acknowledgement

Grant : The Competency Development Program for Industry Specialist

Supported by : National Research Foundation of Korea (NRF), Korea Institute for Advancement of Technology(KIAT)

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