Earticle

초록

영어

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.

목차

Abstract
1. 서론
2. 이론적 배경
2.1 생산시간 및 생산 일정
2.2 총 생산시간의 개념
2.3 생산성 및 숙련도 선행연구
2.4 인공지능을 활용한 생산시간 연구
3. 연구방법
3.1 연구문제
4. 데이터 분석
4.1 MES시스템 데이터 추출
4.2 특징검토
5. 연구결과
5.1 모델 검증
5.2 연구의 의의
5.3 향후 연구 방향
6. References

키워드

저자

• 남은재 [ Eun-Jae Nam | 한국교통대학교 산업경영공학과 ]
• 김광수 [ Kwang-Soo Kim | 한국교통대학교 산업경영공학과 ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한안전경영과학회지 [Journal of Korea Safety Management & Science]

• 간기

  계간

• pISSN

  1229-6783

• eISSN

  2288-1484

• 수록기간

  1999~2025

• 십진분류

  KDC 530 DDC 620