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All Journal Jurnal Optimasi Sistem Industri Qomaruna
Khairai, Kamarulzaman Mahmad
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Religion Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Line Balancing Study Using Value Stream Mapping Tool on Lean Manufacturing: A Case Study in an Electronic Industry Khairai, Kamarulzaman Mahmad; Khalil, Siti Nor Aisyah
Jurnal Studi Multidisiplin Qomaruna Vol 1 No 2 (2024): 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Universitas Qomaruddin, Gresik, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62048/qjms.v1i2.39

Abstract

 XY Electronics, a leading international company in electronic components manufacturing, is confronting significant production constraints that adversely affect output, lead times, and operational expenses. This study examines the manufacturing line for product A using Value Stream Mapping to analyze process times and identify bottlenecks where takt times are exceeded. It focuses on areas surpassing production cycle times and aims to enhance line utilization through better line balancing and waste reduction. The results reveal that the header assembly, along with coplanarity and pre-testing 3, are major bottlenecks, which significantly impact productivity. By optimizing task allocation, refining workforce distribution, and employing cross-training, the production line efficiency improved significantly. In addition, strategic workforce reallocation and station optimization were crucial in addressing resource underutilization and enhancing overall operational efficiency
Product Quality Output Measurement for Preventive Maintenance on Computer Numerical Control (CNC) Machines at an Electronic Manufacturing Industry Apandi, Aizat Haikal; Sharrif, Adam; Khairai, Kamarulzaman Mahmad
Jurnal Studi Multidisiplin Qomaruna Vol 2 No 1 (2024): 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Universitas Qomaruddin, Gresik, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62048/qjms.v2i1.56

Abstract

Computer Numerical Control (CNC) machines remove material from a blank or workpiece using digital controls to produce custom-designed parts. Maintaining their accuracy and precision under challenging conditions after long-term usage is crucial. This study aims to evaluate CNC product quality using Overall Equipment Effectiveness (OEE) and enhance long-term performance through data-driven approaches. The method of this study focuses on analyzing scrap rate data, employing a u-chart to monitor stability, and applying machine learning regression models—K-Nearest Neighbour (KNN) and Random Forest (RF)—to forecast scrap rates. These forecasts help identify when preventive maintenance is necessary, preserving machine precision over time. This study also applied visualization of results with Microsoft Power BI to enhance data interpretation, aiding quick responses to potential problems. Results indicate that RF outperforms KNN in predicting scrap rates. Stacking these models further improves accuracy, offering a more reliable decision-making tool for anticipating quality issues. By detecting anomalies early, manufacturers can implement timely maintenance, minimizing downtime and prolonging CNC machine lifespan. In conclusion, integrating scrap rate analysis, statistical process control, and advanced machine learning techniques can maintain product quality and reduce inaccuracies. Companies should include more proactive maintenance planning by employing better forecasting.
Co-Authors Apandi, Aizat Haikal Khalil, Siti Nor Aisyah Sharrif, Adam Sutarto, Auditya Purwandini Wahab, Muhammad Nubli Abdul