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All Journal Indonesian Journal of Innovation Studies Golden Ratio of Mapping Idea and Literature Format
Z.S, Joumil Aidil Saifuddin
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Productivity Improvement through Line Balancing Measurement in the Loom Section Using the Ranked Positional Weight (RPW) Method: Peningkatan Produktivitas Melalui Pengukuran Line Balancing Pada Section Loom Menggunakan Metode Ranked Positional Weight (RPW) Fauzi, Abiyan Falih; Z.S, Joumil Aidil Saifuddin
Indonesian Journal of Innovation Studies Vol. 26 No. 4 (2025): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v26i4.1581

Abstract

General Background: The textile industry’s productivity hinges significantly on the efficiency of its production lines. Specific Background: At PT XYZ, the section loom area has persistently failed to meet monthly production targets, achieving less than 80% output. Knowledge Gap: Existing work distribution methods lack optimization, resulting in unbalanced workloads and excessive idle time. Aim: This study aims to enhance productivity through line balancing using the Ranked Positional Weight (RPW) method.Results: Initial conditions with three workstations yielded a line efficiency of 58.92%, a balance delay of 41.08%, and 422.48 minutes of idle time. Reconfiguring the system into four workstations using the RPW method increased line efficiency to 72.12%, reduced balance delay to 27.88%, and decreased idle time to 310.34 minutes. Novelty: The application of RPW in this specific section loom context provides a quantifiable strategy for balancing workloads and improving flow. Implications: These findings confirm the RPW method as an effective tool for optimizing production lines, offering a replicable solution for similar industrial settings to boost overall productivity. Highlights: Highlights the use of RPW to solve production inefficiency. Demonstrates measurable gains in efficiency and idle time reduction. Offers a replicable model for workload balancing in manufacturing. Keywords: Line Balancing, Ranked Positional Weight, Productivity Improvement, Textile Industry, Workload Optimization
Analysis of Preventive Maintenance Line Dosing using Modularity Design and Mean Time Between Failure (MTBF) Firmansyah, Aries; Z.S, Joumil Aidil Saifuddin
Golden Ratio of Mapping Idea and Literature Format Vol. 6 No. 2 (2026): February - April
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grmilf.v6i2.2103

Abstract

Maintaining operational continuity requires effective maintenance of production equipment. The dosing line at PT XYZ experienced a total downtime of 7,597 minutes between October 2024 and September 2025, indicating the need for a structured and data-driven maintenance strategy. This study aims to classify maintenance modules, identify failure root causes using a modularity design approach, determine optimal inspection intervals and preventive maintenance frequencies, and evaluate actual availability against standard availability based on mean time between failure (MTBF) and operational time. The dosing line was divided into six main modules, followed by root cause analysis and calculation of MTBF, inherent availability, and operational availability to determine optimal inspection intervals. The results indicate that failures are mainly caused by material blockages, pneumatic issues, component aging, and programmable logic control (PLC) disturbances. The dosing weight module requires the highest preventive maintenance frequency (71 times per year), with optimal inspection intervals ranging from 16 to 53 days across modules. All modules exceeded standard availability thresholds, with inherent availability above 99% and operational availability above 90%. These findings confirm that integrating modularity design and MTBF analysis effectively supports systematic and data-driven preventive maintenance planning.
Co-Authors Fauzi, Abiyan Falih Firmansyah, Aries