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All Journal Ekliptika: Jurnal Inovasi Teknologi Berkelanjutan AJCD
Enik Sulistyowati
Universitas Nahdlatul Ulama Pasuruan, Indonesia
Religion Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Education Environmental Science Industrial & Manufacturing Engineering

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Effectiveness of Preventive and Predictive Maintenance in Improving Machine Performance: A Systematic Review Enik Sulistyowati; Anurag Hazarika; Samikshya Madhukullya
AMCA Journal of Community Development Vol. 4 No. 2 (2024): AMCA Journal of Community Development
Publisher : AMCA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51773/ajcd.v4i2.278

Abstract

Effective machine maintenance is key to ensuring productivity and operational efficiency in the industrial sector, where although preventive maintenance has become the standard, technological developments are now introducing more advanced predictive maintenance strategies, but a comprehensive comparison of the effectiveness of these two approaches in improving machine performance is still limited. This study aims to conduct a systematic review and meta-analysis of the efficacy of preventive and predictive maintenance methods, especially in the manufacturing industry, through a literature search in Scopus, Web of Science, and IEEE Xplore databases with related keywords, where empirical studies comparing both methods and their impacts on machine performance, including availability, reliability, and maintenance costs, are included. Meta-analysis was used to measure the difference in effectiveness between the methods, with study quality assessed using the AMSTAR checklist. The results showed that of the 25 studies that met the inclusion criteria, predictive maintenance was shown to be more effective in improving availability than preventive maintenance (effect size = 0.75, 95% CI: 0.60-0.90, p < 0.01), where factors such as cost, technology level, and machine type influenced the selection of the optimal maintenance method. This systematic review indicates that predictive maintenance is more effective in improving machine performance than preventive methods. However, the choice of the optimal method must be tailored to the specific industry context, and these findings are essential for decision-making in industrial maintenance management.
Pengukuran Produktivitas dan Usulan Perbaikan dengan Model Marvin E Mundel dan APC pada Industri Kayu PT ABC Nur Hamidah; Iis Riyana; Enik Sulistyowati
Ekliptika Vol. 5 No. 1 (2024): Ekliptika
Publisher : Universitas Nahdlatul Ulama Pasuruan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55757/ekliptika.v5i1.452

Abstract

PT ABC merupakan perusahaan pengolahan kayu turning yang memerlukan pengukuran pemanfaatan sumber daya sebagai dasar perbaikan kinerja. Penelitian ini mengevaluasi produktivitas menggunakan model Marvin E. Mundel dan American Productivity Center (APC), dengan tahun 2022 sebagai periode dasar dan 2023 sebagai periode pengukuran. Analisis mencakup material, tenaga kerja, energi, dan modal, serta evaluasi kualitatif melalui wawancara dan diagram sebab akibat. Indeks produktivitas total Mundel yang dilaporkan sebesar 96,18 menunjukkan penurunan 3,82% terhadap dasar 100. Indeks parsial Mundel menunjukkan kenaikan tenaga kerja 11,57% dan penurunan energi 12,39%. Pada APC, indeks tenaga kerja yang dilaporkan sebesar 111,47 menunjukkan kenaikan 11,47%. Namun, indeks total APC sebesar 271,9 belum dapat dikonfirmasi karena tabel lain memuat 96,45 dan rincian agregasi harga konstan tidak tersedia. Pemeriksaan rasio energi 520,9/543,3 menghasilkan indeks 95,88, berbeda dari 92,57 yang dilaporkan. Rekap harga berlaku menghasilkan indeks profitabilitas total sekitar 98,78 setelah penjumlahan ulang komponen input. Hasil mendukung prioritas pemeriksaan penggunaan energi, mutu material, dan pengaturan kerja. Usulan perbaikan belum diuji melalui pengukuran setelah penerapan, sehingga keunggulan salah satu model dan besarnya peningkatan produktivitas belum dapat ditetapkan.
Co-Authors Anurag Hazarika Iis Riyana Nur Hamidah Samikshya Madhukullya