Journal of the Korea Safety Management & Science (대한안전경영과학회지)

Korea Safety Management & Science (대한안전경영과학회)
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A Study on Total Production Time Prediction Using Machine Learning Techniques

머신러닝 기법을 이용한 총생산시간 예측 연구

  • Eun-Jae Nam (Department of Industrial Engineering, Korea National University of Transportation) ;
  • Kwang-Soo Kim (Department of Industrial Engineering, Korea National University of Transportation)
  • 남은재 (한국교통대학교 산업경영공학과) ;
  • 김광수 (한국교통대학교 산업경영공학과)
  • Received : 2023.05.15
  • Accepted : 2023.06.26
  • Published : 2023.06.30
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Abstract

The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.

Keywords

References

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