Penerapan FMEA untuk Analisis Penyebab Reject pada Produk Under Bracket HK2SO di PT XYZ
DOI:
https://doi.org/10.33479/sakti.v5i02.190Keywords:
FMEA, forging defects, quality control, risk priority number (RPN), under bracket HK2S)Abstract
Competition in the manufacturing industry requires companies to produce high-quality products with a low reject rate. PT XYZ faces a high level of rejects in the Under Bracket HK2SO component, resulting in increased production costs and decreased efficiency. This study aims to identify the main causes of product defects and determine improvement priorities using the Failure Mode and Effect Analysis (FMEA) method. Data were collected over a 14-week period through process observation, operator interviews, and quality report analysis. The results show that three dominant defect types—dent, underfill, and trimming—contribute to the majority of rejects, with dent having the highest Risk Priority Number (RPN) of 320. These failure modes are influenced by improper handling procedures, suboptimal equipment conditions, and unstable product positioning on the jig. Based on the FMEA analysis, corrective actions were proposed, including installing soft-pads on trays, adding locator pins to jigs, controlling billet temperature, calibrating hammer pressure, and providing operator training. Implementation of these improvements is projected to reduce RPN values by 30–50% and decrease overall reject rates by 30–45% within 1–3 months. These findings contribute to strengthening quality control in forging processes and may serve as a reference for similar industries. Future studies are recommended to integrate FMEA with statistical methods such as Statistical Process Control (SPC) or risk-weighting techniques like AHP to enhance evaluation accuracy and the sustainability of quality improvements.References
Ali, M. A., Rahman, A. H., & Yusuf, N. M. (2020). Application of FMEA in automotive component manufacturing to minimize defect rate. International Journal of Quality & Reliability Management, 37(5), 935–948.
Fachrizal, R., & Jufriyanto, M. (2025). Pengendalian kualitas dan analisis produk margarine Blue Team dengan menggunakan peta kontrol dan metode Failure Mode and Effect Analysis (FMEA) guna meminimalkan produk reject: Studi kasus pabrik pengolahan minyak kelapa sawit. Jurnal Teknologi dan Manajemen Industri Terapan, 4(3), 951–961. https://doi.org/10.55826/jtmit.v4i3.1070
Hardianto, R. D. (2023). Analisis penyebab reject produk paving block dengan pendekatan metode FMEA dan FTA. Jurnal Cakrawala Ilmiah, 2(12), 4635–4648. https://doi.org/10.53625/jcijurnalcakrawalailmiah.v2i12.6394
Hartono, A. B. (2022). Identifikasi penyebab reject raw material dan usulan perbaikan proses material handling dengan metode FMEA dan AHP (Studi kasus di PT XYZ) (Doctoral dissertation, Universitas Islam Sultan Agung).
Hidayat, A. A., Kholil, M., Haekal, J., Ayuni, N. A., & Widodo, T. (2021). Lean manufacturing integration in reducing the number of defects in the finish grinding disk brake with DMAIC and FMEA methods in the automotive sub-industry company. International Journal of Scientific Advances, 2(5). https://doi.org/10.51542/ijscia.v2i5.7
Hossen, M. M., Islam, M. N., & Rahman, M. H. (2017). Application of Pareto analysis and cause-and-effect diagram for minimizing defects in sewing section of a garment factory. Journal of Mechanical Engineering, 47(1), 42–47. https://doi.org/10.1080/00405000.2017.1308786
Krisnaningsih, E., Gautama, P., & Syams, M. F. K. (2021). Usulan perbaikan kualitas dengan menggunakan metode FTA dan FMEA. Jurnal Intent: Jurnal Industri dan Teknologi Terpadu, 4(1), 41–54.
Rahman, M. N. A., & Bakar, N. A. (2018). The use of FMEA in quality improvement process: A review. Journal of Engineering Science and Technology, 13(6), 1781–1792.
Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. Quality Press.
Sutrisno, A., Gunawan, I., Vanany, I., Asjad, M., & Caesarendra, W. (2020). An improved modified FMEA model for prioritization of lean waste risk. International Journal of Lean Six Sigma, 11(2), 233–253. https://doi.org/10.1108/IJLSS-11-2017-0125






