ANALISIS PENERAPAN PREDICTIVE MAINTENANCE UNTUK MENINGKATKAN EFEKTIVITAS PEMELIHARAAN MESIN PADA PT TOYOTA ASTRA MOTOR SPLD DENGAN METODE OVERALL EQUIPMENT EFFECTIVENESS (OEE)

LESTARI, INTAN (2025) ANALISIS PENERAPAN PREDICTIVE MAINTENANCE UNTUK MENINGKATKAN EFEKTIVITAS PEMELIHARAAN MESIN PADA PT TOYOTA ASTRA MOTOR SPLD DENGAN METODE OVERALL EQUIPMENT EFFECTIVENESS (OEE). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The implementation of Predictive Maintenance is becoming increasingly important in enhancing the efficiency and effectiveness of production systems. This research aims to analyze the application of Predictive Maintenance on Automated Mobile Robots (AMR) at PT Toyota Astra Motor SPLD. Through this study, production system data are analyzed to evaluate the impact of PdM on Total Productive Maintenance (TPM). The methodology used includes data collection on operational time, frequency of failures, and machine downtime. Additionally, statistical calculations regarding the improvement of Overall Equipment Effectiveness (OEE) are conducted to provide a comprehensive picture of machine performance before and after the implementation of PdM. The analysis results indicate that the application of PdM significantly reduces downtime and increases OEE from 69.99% to 94.56%, thereby contributing to continuous improvement in the production process. This research is expected to provide valuable insights for the industry in implementing data-driven maintenance strategies to enhance productivity and competitiveness in the digital era. Keywords:Down time, Predictive Maintenance, OEE, Total Productive Maintenance. Penerapan Predictive Maintenance menjadi semakin penting dalam meningkatkan efisiensi dan efektivitas sistem produksi. Penelitian ini bertujuan untuk menganalisis penerapan Predictive Maintenance pada Automated Mobile Robot (AMR) di PT Toyota Astra Motor SPLD. Melalui studi ini, data dari sistem produksi dianalisis untuk mengevaluasi dampak PdM terhadap Total Productive Maintenance (TPM). Metodologi yang digunakan mencakup pengumpulan data mengenai waktu operasi, frekuensi kerusakan, dan downtime mesin. Selain itu, dilakukan perhitungan statistik mengenai peningkatan nilai Overall Equipment Effectiveness (OEE) untuk mendapatkan gambaran yang komprehensif tentang kinerja mesin sebelum dan sesudah penerapan PdM. Hasil analisis menunjukkan bahwa penerapan PdM secara signifikan mengurangi downtime dan meningkatkan OEE dari 69,99% menjadi 94,56%, sehingga berkontribusi pada perbaikan berkelanjutan dalam proses produksi. Penelitian ini diharapkan dapat memberikan wawasan yang berharga bagi industri dalam mengimplementasikan strategi pemeliharaan berbasis data untuk meningkatkan produktivitas dan daya saing di era digital. Kata kunci: Down time, Predictive Maintenance, OEE, Total Productive Maintenance

Item Type:	Thesis (S1)
Call Number CD:	FT/IND. 25 008
NIM/NIDN Creators:	41619110072
Uncontrolled Keywords:	Down time, Predictive Maintenance, OEE, Total Productive Maintenance
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 022 Administration of The Physical Plant/Administrasi dan Fisik Perpustakaan > 022.9 Equipment, Furniture, Furnishings/Peralatan, Perabotan 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 025 Operations, Archives, Information Centers/Operasional Perpustakaan, Arsip dan Pusat Informasi, Pelayanan dan Pengelolaan Perpustakaan > 025.3 Bibliographic Analysis and Control/Bibliografi Analisis dan Kontrol Perpustakaan > 025.31 Library Catalog/Katalog Perpustakaan > 025.317 Conversion and Maintenance/Konversi dan Pemeliharaan 600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 653 Shorthand/Penulisan Cepat, Penulisan dengan Tangan > 653.3 Machine Systems/Sistem Mesin
Divisions:	Fakultas Teknik > Teknik Industri
Depositing User:	khalimah
Date Deposited:	10 Feb 2025 03:37
Last Modified:	10 Feb 2025 08:38
URI:	http://repository.mercubuana.ac.id/id/eprint/94042

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