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Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 33 , No. 4

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[ Article ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 33, No. 4, pp. 184-191
Abbreviation: J. Korean Soc. Manuf. Technol. Eng.
ISSN: 2508-5107 (Online)
Print publication date 15 Aug 2024
Received 31 Jul 2024 Revised 02 Aug 2024 Accepted 05 Aug 2024
DOI: https://doi.org/10.7735/ksmte.2024.33.4.184
FOM을 활용한 자동차 부품 공정의 생산성 및 생산계획 정확도 향상에 관한 연구
남기선^a ; 오상석^b ; 김수영^a^{, *}


Improvement of Production-Plan Accuracy and Productivity by Applying FOM to Automobile Parts Process
Ki Sun Nam^a ; Sang Suk Oh^b ; Su Young Kim^a^{, *}
aDepartment of AI Smart Factory Convergence Engineering, Hoseo University
bSmart Material Component Engineering of Manufacturing Innovation School, Inha University
Correspondence to : ^*Tel.: +82-42-540-9960 E-mail address: df2030@hoseo.edu (Su Young Kim).

Abstract

This study aims to improve productivity by analyzing manufacturing-process data and deriving problems using a FOM system. Additionally, an accurate demandforecast production plan is established to increase manufacturing competitiveness. As a case study, MES data are analyzed using an FOM assistant for Company I, which is an automobile parts manufacturer, and the correlation among man, machine, material, and method is identified. The production plan fluctuated significantly and the productivity declined owing to the inactivity time (equipment waiting, mold setting, etc.). The method of converting to external preparation by dividing internal and external preparations, standardizing the mold setup, and using data is presented.

In addition, the importance and evaluation method of establishing a high-accuracy production plan were presented, and the introduction of APS was proposed to increase the accuracy of the production plan by predicting, revising, and supplementing demand by period, customer, and product through accumulated data analysis.


Keywords: FOM(smart-factory operation management), 4M data analysis, APS(advanced production scheduling), Productivity

References


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Ki Sun Nam

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: ksnam20@hanmail.net

Sang Suk Oh

Invited 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

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