Anisa Oktariani
Universitas Padjadjaran

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Analisis Komparatif Availability Losses Berbasis OEE pada Dua Line Mesin Blister untuk Menentukan Prioritas Perbaikan di Industri Farmasi Anisa Oktariani; Holis Abdul Holik
OBAT: Jurnal Riset Ilmu Farmasi dan Kesehatan Vol. 4 No. 4 (2026): Juli : OBAT: Jurnal Riset Ilmu Farmasi dan Kesehatan
Publisher : Asosiasi Riset Ilmu Kesehatan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/obat.v4i4.2318

Abstract

The pharmaceutical industry requires effective production equipment to ensure product quality consistency and timely distribution. This study aimed to analyze the Availability component within the Overall Equipment Effectiveness (OEE) framework through downtime evaluation of two blister machine lines at PT XYZ during the January–December 2025 period. A quantitative descriptive approach was employed using historical production data, with analysis focused on Planned Downtime (PDT) and Unplanned Downtime (UPDT). Dominant losses were identified using Pareto analysis and further investigated through root cause analysis. The results showed that the annual average OEE of Blister A was 78.4%, while Blister B achieved 71.51%, indicating a performance gap of 6.9%. PDT in both lines was primarily dominated by Lunches & Breaks, Cleaning, Inspection, and Lubrication (CIL), and Change Batch. Meanwhile, UPDT in Blister A was mainly caused by Unplanned Stops (1,132 minutes) and Reprocess, whereas Blister B was dominated by Unplanned Stops (5,117 minutes) and Breakdown (2,015 minutes). The findings indicate that Availability losses were influenced not only by mechanical factors but also by managerial aspects, digital system integration, and suboptimal process parameter standardization. Improving OEE requires optimizing operator rotation, implementing Single-Minute Exchange of Die (SMED), strengthening condition-based maintenance, and applying Statistical Process Control (SPC) to maintain sustainable process stability. These findings provide a data-driven basis for optimizing production capacity, prioritizing maintenance strategies, and supporting continuous improvement in pharmaceutical packaging operations.