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Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 33 , No. 2

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Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 30, No. 4, pp. 259-268
Abbreviation: J. Korean Soc. Manuf. Technol. Eng.
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
김재혁^a ; 김수영^b^{, *}


Productivity Analysis Method based on Manufacturing Big-data using the FOM System in the FOMs Package
Jae Hyuk Kim^a ; Su Young Kim^b^{, *}
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).



Funding Information ▼ Ministry of SMEs and Startups Korea Association of University, Research Institute and Industry S3039971

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