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

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 33, No. 3, pp.147-154

ISSN: 2508-5107 (Online)

Print publication date 15 Jun 2024

Received 13 May 2024 Revised 06 Jun 2024 Accepted 11 Jun 2024

DOI: https://doi.org/10.7735/ksmte.2024.33.3.147

FOM을 활용한 자동차용 휀 제조공정의 생산성 향상에 대한 연구

박용록^a ; 장오성^a ; 장재훈^a ; 임효재^b ; 김수영^a^{, *}

A Study on Productivity Improvement in Automotive Fan Manufacturing Process Using FOM

Park, Yong Rog^a ; Jang, O Seong^a ; Jang, Jae Hoon^a ; Lim, Hyo Jae^b ; Kim, Su Young^a^{, *}

aDepartment of AI Smart Factory Convergence Engineering, Hoseo University
bGeothermal Energy Education Center, Hoseo University

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

Abstract

This study analyzes the data using the FOM of the case companies, and the production achievement rate is 80%. The results of the 4M analysis were identified for the non-operation and process failure. In addition, through the measurement of the working time of the manufacturing process and the analysis of the manufacturing capacity, the work dispersion caused by irregular work in the process was identified as an influencing factor, and countermeasures were established accordingly. It was confirmed that the productivity rate was improved by 8.1%, the non-operation rate was improved by 0.04%, the process defect was improved by 0.02% and the loss cost was reduced by 56.94 million won. The FOM solution will be of great helpful in improving the manufacturing competitiveness of small and medium-sized businesses to analyze data, establish countermeasures and predict and apply the effectiveness of improvement plans through simulation.

Keywords:

FOM(factory operation management), Manufacturing big data, Injecting assembly process, Productivity improvement, Prediction

References

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Yong Rog Park

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

E-mail: 0310pyr@naver.com

O Seong Jang

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: osss2280@naver.com

Jae Hoon Jang

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

E-mail: fomsre@naver.com

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