[ Special Issue : Digital Transformation - Smart Factory Operation Managements ]

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 33, No. 3, pp.155-163

ISSN: 2508-5107 (Online)

Print publication date 15 Jun 2024

Received 13 May 2024 Revised 08 Jun 2024 Accepted 11 Jun 2024

DOI: https://doi.org/10.7735/ksmte.2024.33.3.155

고무압출 제조기업의 레거시 데이터를 활용한 FOM Solution 기반 생산관리 방법에 관한 연구

김재하^a ; 손경섭^a ; 오상석^c ; 임효재^b ; 김수영^a^{, *}

Solution-based Production-Management Method Using Legacy Data from Rubber-Extrusion Manufacturing Company

Jae Ha Kim^a ; Kyung Sup Son^a ; Sang Suk Oh^c ; Hyo Jae Lim^b ; Su Young Kim^a^{, *}

aDepartment of AI Smart Factory Convergence Engineering, Hoseo University
bGeothermal Energy Education Center, Hoseo University
cManufacturung Innovation School, Inha Univerity

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

Abstract

In this study, the legacy data of the MES system of rubber extruder company A and the FOM solution are integrated and analyzed. Owing to the characteristics of the manufacturer, 4M changes that occur frequently in existing legacy data can only be managed to a certain extent. Therefore, in this study, the MES system and FOM solution are integrated to obtain the visibility of 4M data at the manufacturing side, and a systematic and multidimensional analysis based on code is performed. If the unit price values for more diverse products are defined, then more detailed loss costs can be calculated for all productivity inhibitors. By overcoming the limitations of MES data management and making decisions to effectively improve data-based productivity using the analysis results of this study, company A is expected to benefit factory operations management.

Keywords:

4M(man, machine, material, method), Legacy system(ERP, MES, POP etc), FOM(smart-factory operation management), Productivity analysis, Manufacturing big data, Loss cost

References

Kim, J. S., 2017, Big Data Analysis for Smart Factory Implementation in Small and Medium Manufacturing Process, Doctorate Thesis, Chungbuk University, Republic of Korea.
Lee, J. H., Nam, H. K., Yoo, W. S., 2016, Real-time Monitoring System of the Legacy Systems Data -Focused on Manufacturing Shop Floor-, Journal of Korea Safety Management & Science, 18:1 219-226. [https://doi.org/10.12812/ksms.2016.18.1.219]
Kim, S. Y., 2018, A Case Study of the Introduction of Smart Factory Operation Management(FOM) in the Fourth Industrial Revolution Era, J. Korean Assoc. Comput. Account., 16:1 43-62.
Jang, J. H., Kim, S. R., Kim, J. H., Bae, B. S., Kim, S. Y., 2022, Improved Reliability of Manufacturing Process Data Using FOMs(smart-Factory Operation Management) Solution, J. Korean Soc. Manuf. Technol. Eng., 31:3 216-223. [https://doi.org/10.7735/ksmte.2022.31.3.216]
Son, K. S., Jang, J. H., Kim, J. H., Kim, S. Y., 2024, A Case Study on the Establishment of SMEs FOM MES Interworking System for Multidimensional Analysis of 4M Data in Manufacturing Sites, J. Korean Soc. Manuf. Technol. Eng., 33:1 58-68. [https://doi.org/10.7735/ksmte.2024.33.1.58]
Kim, S. C., Kim, J. H., Nam, K. S., Kim, S. Y., 2024, A Case Study on Manufacturing Innovation Using the FOM System in the Continuous Process of Film Manufacturing, J. Korean Soc. Manuf. Technol. Eng., 33:1 69-76. [https://doi.org/10.7735/ksmte.2024.33.1.69]
Kim, S. Y., Kim, J. H., 2022, Process of Big Data Analysis and Change Management and its method by Smart Factory FOMs Package, KR Patent : 1023519910000.
Kim, S. Y., Kim, J. H., Kim, J. H., 2022, Process for Inter-connection, Multi-dimensional Analysis, and Decision-making of 4M Big Data and its Method by Smart Manufacturing Innovation FOM System, KR Patent : 1024320050000.
Kim, J. H., Kim, S. Y., 2021, Productivity Analysis Method based on Manufacturing Big-data using the FOM System in the FOMs Package, J. Korean Soc. Manuf. Technol. Eng., 30:4 259-268. [https://doi.org/10.7735/ksmte.2021.30.4.259]

Jae Ha Kim

Graduate Student in Department of AI Smart Factory Convergence Engineering, Hoseo University. His research interest is FOM (smart-Factory Operation Management) with AI.

E-mail: dfs717@naver.com

Kyung Sup Son

Graduate Student in Department of AI Smart Factory Convergence Engineering, Hoseo University. His research interest is FOM (smart-Factory Operation Management) with AI.

E-mail: ksson@net-plus.kr

Sang Suk Oh

Visiting Professor in Smart Material Component Engineering of Manufacturing Innovation School, Inha University. His research interest is Smart Factory Operation Management and Manufacturing Innovation with AI.

E-mail: sangsoh@inha.ac.kr

Hyo Jae Lim

Professor in Geothermal Energy Education Center, Hoseo University. His research interest is Net-zero Carbon Energy Systems.

E-mail: hjlim@hoseo.edu

Su Young Kim

Professor in Department of AI Smart Factory Convergence Engineering, Hoseo University. His research interest is Applications of FOMs (smart-Factory Operation Managements).

E-mail: df2030@hoseo.edu