Analisis Efektivitas Mesin CNC Profiling Jobs Linx 30 dengan Pendekatan Total Productive Maintenance Menggunakan Metode OEE, Six Big Losses dan FMEA di PT Dirgantara Indonesia

Abstract

PT Dirgantara Indonesia menghadapi keterlambatan produksi akibat gangguan operasional mesin di Departemen Detail Part Manufacturing (DPM). Penelitian ini bertujuan mengevaluasi efektivitas mesin CNC Profiling Jobs Linx 30 pada unit Machining Shop menggunakan pendekatan Total Productive Maintenance (TPM). Metode yang digunakan meliputi perhitungan Overall Equipment Effectiveness (OEE), analisis Six Big Losses dengan diagram Pareto, identifikasi akar masalah menggunakan diagram fishbone, serta penilaian risiko melalui Failure Mode and Effect Analysis (FMEA). Data operasional yang dianalisis berasal dari periode Januari hingga Desember 2025. Hasil penelitian menunjukkan bahwa nilai OEE mesin sebesar 63,93%, masih berada di bawah standar Japan Institute of Plant Maintenance (JIPM) sebesar 85%. Rendahnya nilai tersebut terutama disebabkan oleh availability rate yang hanya mencapai 67,73% akibat tingginya unplanned downtime. Analisis Six Big Lossesmenunjukkan bahwa breakdown losses sebesar 65,90% dan setup & adjustment losses sebesar 21,74% menjadi penyumbang utama kerugian waktu produksi dengan total kontribusi mencapai 87,63%. Selain itu, hasil FMEA mengidentifikasi bahwa siklus preventive maintenance yang belum optimal akibat keterbatasan staf maintenance memiliki nilai Risk Priority Number (RPN) tertinggi, yaitu 320. Berdasarkan temuan tersebut, diusulkan perbaikan berupa penambahan staf maintenance, penyediaan buffer stock suku cadang, peningkatan koordinasi validasi NCOD, perbaikan modul Automatic Tool Change, serta penerapan inspeksi incoming material guna meningkatkan efektivitas mesin.

PT Dirgantara Indonesia is facing production delays due to machine operational disruptions in the Detail Part Manufacturing (DPM) Department. This study aims to evaluate the effectiveness of the Linx 30 CNC profiling machine in the Machining Shop using the Total Productive Maintenance (TPM) approach. The methods used include calculating Overall Equipment Effectiveness (OEE), analyzing the Six Big Losses using a Pareto chart, identifying root causes using a fishbone diagram, and assessing risks through Failure Mode and Effect Analysis (FMEA). The operational data analyzed covers the period from January to December 2025. The research results show that the machine’s OEE value is 63.93%, which remains below the Japan Institute of Plant Maintenance (JIPM) standard of 85%. This low value is primarily due to an availability rate of only 67.73% resulting from high unplanned downtime. The Six Big Losses analysis indicates that breakdown losses (65.90%) and setup & adjustment losses (21.74%) are the primary contributors to production time losses, with a combined contribution of 87.63%. Additionally, the FMEA results identified that the suboptimal preventive maintenance cycle, due to limited maintenance staff, has the highest Risk Priority Number (RPN), which is 320. Based on these findings, the following improvements are proposed: increasing the number of maintenance staff, establishing a buffer stock of spare parts, enhancing coordination for NCOD validation, improving the Automatic Tool Change module, and implementing incoming material inspections to improve machine efficiency.