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Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 32 , No. 3

[ Special Issue : SW-based smart-Factory Operation Managements(FOMs) Technology ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 32, No. 3, pp. 177-181
Abbreviation: J. Korean Soc. Manuf. Technol. Eng.
ISSN: 2508-5107 (Online)
Print publication date 15 Jun 2023
Received 08 Apr 2023 Revised 10 May 2023 Accepted 23 May 2023
DOI: https://doi.org/10.7735/ksmte.2023.32.3.177

FOM을 활용한 문형 오면가공기계의 가공 리드타임 단축 연구
이남은a ; 장선준b ; 김수영a, *

Study on Reduction of Machining Lead Time of Double Surface Machining Machine using FOM
Nam Eun Leea ; Seon Jun Jangb ; Su Young Kima, *
aDepartment of AI Smart Factory Convergence Engineering, Hoseo University
bDivision of Mechanical and Automotive Engineering, Hoseo University
Correspondence to : *Tel.: +82-2-3473-5336 E-mail address: df2030@hoseo.edu (Su Young Kim).

Funding Information ▼

Abstract

The digital transformation of small and medium enterprises is necessary for the improvement of quality and productivity through the collection and analysis of manufacturing data and to improve delivery compliance for competitiveness. For example, it implements a smart factory operation management solution by analyzing data to reduce long lead times in machining processes and efficiently operate machines in manufacturing large vacuum chambers. The improvement effect is improved after the loss reduction for the long processing lead time of the processing machine. Therefore, many processing companies that manufacture small quantities of small items must achieve the process optimization of custom-made products and continuously promote activities to improve productivity, cost reduction, the total lead time, and profitability through fast manufacturing-level smart factory data flexibility.


Keywords: FOM(smart-factory operation management), 4M data analysis, Machining lead time, Machining innovation

Acknowledgments

이 논문은 중소벤처기업부 ‘중소기업연구인력지원사업’의 재원으로 한국산학엽협회(AURI)의 지원을 받아 수행된 연구임. (2023년 기업연계형연구개발인력양성사업, 과제번호: RS-2023-00259258)


References
1. Salesforce, 2022, viewed 30 May 2023, How Small Businesses Can Succeed With Digital Transformation, <https://www.salesforce.com/kr/hub/business/small-business-dx/> .
2. 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.
3. Kim, S. Y., 2015, Study of Digital Factory FOM Solution on Software-based : Applied Case to Heat-Treatment Company, Korean Institute of Industrial Engineers, Spring Joint Conf., 2855-2863.
4. Kim, J. S., Cho, W. S., 2015, Data Analysis of 4M Data in Small and Medium Enterprises, Journal of the Korean Data and Information Science Society, 26:5 1117-1128.
5. ProSharing Consulting, 2019, viewed 30 May 2023, Points for Reducing Lead Times ~Thoughts Needed for Proper Production Planning~, <https://circu.co.jp/pro-sharing/mag/article/3057/>.
6. Kim, S. Y., Song, M. K., 2014, Application of MI-NPS Digital Factory Methodology for Production Ability Improvement and Optimal Layout Design : Applied Case to Vehicle Shaft Manufacturing Line, Productivity Review, 28:1 47-73.
7. Oh, S. S., Yang, H. S., Bae, B. S., Kim, S. Y., 2021, Application of FOM Methodology for 4M Optimization Based on the Data of Manufacturing Process of Mechanical Parts, J. Korean Soc. Manuf. Technol. Eng., 30:6 456-464.
8. Lee, J. Y., 2015, A Study on Lead-time Reduction Based on Advanced Planing and Scheduling(APS) System, Master thesis, Hanbat University, Republic of Korea.
9. Cho, D. H., Lee, J. S., Lee, K. W., 2018, A Case Study of Setup Time Reduction in an Injection Process Using SMED Technique, Journal of the Korean Society of Facility Management, 23:3 5-17.
10. Kim, J. D., Song, Y. W., Cho, W. S., 2016, The Usage Needs and Adoption Intention of Manufacturing Big Data Technology in Small and Medium-sized Manufacturing Companies, Korean Corporation Management Review, 23:5 47-68.

Nam Eun Lee

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: leenameun00@naver.com

Seon Jun Jang

Associate Professor in Division of Mechanical and Automotive Engineering, Hoseo University. His research interest is Vibrational Energy Harvesters and Wave Energy Converters.

E-mail: mweagle@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