대한안전경영과학회지 (Journal of the Korea Safety Management & Science)

  • 제25권2호
  • /
  • Pages.159-165
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  • 2023
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  • 1229-6783(pISSN)
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  • 2288-1484(eISSN)
대한안전경영과학회 (Korea Safety Management & Science)
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머신러닝 기법을 이용한 총생산시간 예측 연구

A Study on Total Production Time Prediction Using Machine Learning Techniques

  • 남은재 (한국교통대학교 산업경영공학과) ;
  • 김광수 (한국교통대학교 산업경영공학과)
  • Eun-Jae Nam (Department of Industrial Engineering, Korea National University of Transportation) ;
  • Kwang-Soo Kim (Department of Industrial Engineering, Korea National University of Transportation)
  • 투고 : 2023.05.15
  • 심사 : 2023.06.26
  • 발행 : 2023.06.30
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초록

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.

키워드

참고문헌

  1. C. U. Bae, M. H. Mun, J. S. Kim(2016), A study on productivity improvement methods through NCS-Based worker competency diagnosis. Korean Society of Management Science, pp. 1089-1094.
  2. D. M. Park(2021), "A study on how to apply machine learning to estimate total production time in individual production methods." Doctoral dissertation, Donga University Graduate School.
  3. D. M. Park, H. R. Choi, B. K. Park(2021), "Machine learning-based total production time prediction method for custom-made companies." Intelligence Information Research, 27(1):177-190.
  4. G. H. Lee(2022), "Correlation analysis between factors to improve smart factory production." Doctoral dissertation, Korea Polytechnic University.
  5. H. G. Kim(2019), "An empirical study on the intention of continuous use and conversion of smart factories." Doctoral dissertation, Pusan National University Graduate School of International Studies.
  6. H. Whang(2005). Work management theory. Yeongji Cultural History.
  7. H. Y. Kim(1996). "Standard time calculation method by real-time data collection."
  8. J. H. Yoo(2019), "Study on prediction of attendance using machine learning." Journal of IKEEE, 23(4):1243-1249. doi: 10.7471/IKEEE.2019.23.4.1243
  9. J. S. Kim, W. S. Cho(2015), "Analysis of 4M data in small and medium-sized manufacturing processes for the introduction of big data." Korean Journal of Data Information Science, 26(5):1117-1128. https://doi.org/10.7465/jkdi.2015.26.5.1117
  10. J. S. Lim, M. C. Lim, M. C. Um(2004). Operations management. Hyeongseol.
  11. Y. J. Noh, M. R. Kim(2020), "An empirical study on the factors influencing technical skill development of production workers." Vocational Competency Development Research, 23(3):139-168.
  12. B. Bahu, L. Bironneau, V. Hovelaque(2019), "Comprehension du DDMRP et de son adoption : Premiers elements empiriques." Logistique & Management, 27(1):20-32. https://doi.org/10.1080/12507970.2018.1547130
  13. K. Schwab(2017), The Fourth Industrial Revolution: Currency. Crown Business, New York, p. 4.
  14. Z. Kjell, M. Harold(2001), Maynard's industrial engineering handbook (5th ed.). McGraw-Hill Education.
  15. P. G. Mikell(2006), Work systems and the methods, measurement, and management of work. Pearson.
  16. IBM(2022, November 7). Dicision tree. Retrieved from https://www.ibm.com/downloads/cas/GQP5QPXZ
  17. Kaggle XGBM(2022, October 27). XGBM. Retrieved from https://www.kaggle.com
  18. LightGBM(2022, November 2). LGBM algorithm. Retrieved from https://lightgbm.readthedocs.io/en/v3.3.2/
  19. Sklearn(2022, November 2). randomforestregres sor. Retrieved from https://scikit-learn.org/stable/modules/classes.html#