한국생산제조학회 학술지 영문 홈페이지
[ Papers ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 30, No. 4, pp.259-268
ISSN: 2508-5107 (Online)
Print publication date 15 Aug 2021
Received 12 Jul 2021 Revised 30 Jul 2021 Accepted 02 Aug 2021
DOI: https://doi.org/10.7735/ksmte.2021.30.4.259

FOMs 패키지 내 FOM 시스템을 활용한 제조현장 빅데이터 기반의 생산성 분석 방법에 관한 연구

김재혁a ; 김수영b, *
Productivity Analysis Method based on Manufacturing Big-data using the FOM System in the FOMs Package
Jae Hyuk Kima ; Su Young Kimb, *
aResearch Institute affiliated with Digital Factory Co., Ltd.
bDepartment of Smart Factory for Materials-Parts-Equipment, Graduate School, Hoseo University

Correspondence to: *Tel.: +82-70-8600-5336 E-mail address: df2030@hoseo.edu (Su Young Kim).

Abstract

The key to transforming the manufacturing industry is the appropriate application of information technology and the utilization of data. Therefore, a method for efficient factory operation management, as well as IT system introduction, is important. From this, it is possible to create data-driven value and strengthen the competitiveness of enterprises. In this paper, we propose a FOMs (smart-Factory Operation Managements) Package method based on 4M data of manufacturing sites to improve productivity and competitiveness. In addition, we performed a case study by applying part of the FOMs Package to the manufacturer. We performed a detailed and multi-dimensional analysis of the MES/POP data that the manufacturer managed, and were able to derive not only the products that needed improvement but also related facilities, workers, downtimes, and defects. As a result, we found that the proposed method was effective in analyzing 4M manufacturing data and making decisions for factory operation management.

Keywords:

FOMs package, FOM system, Productivity analysis, Manufacturing big data, Convergence technology, Smart manufacturing

Acknowledgments

이 연구는 중소벤처기업부 ‘중소기업연구인력지원사업’의 재원으로 한국산학엽협회(AURI)의 지원을 받아 수행된 연구임(2021년 기업연계형연구개발인력양성사업, 과제번호 : S3039971).

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Jae Hyuk Kim

Senior researcher in research institute affiliated with Digital Factory Co., Ltd.

His research interest is smart manufacturing and industrial AI.

E-mail: kiate93@naver.com

Su Young Kim

Professor in the Department of smart factory for materials-parts-equipment, Hoseo University.

His research interest is smart factory and applications of FOMs (smart-Factory Operation Managements) system.

E-mail: df2030@hoseo.edu