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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.
The Influence of Stress on Industrial Operator’s Physiology and Work Performance Khairai, Kamarulzaman Mahmad; Sutarto, Auditya Purwandini; Wahab, Muhammad Nubli Abdul
Jurnal Optimasi Sistem Industri Vol. 19 No. 2 (2020): Published in October 2020
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (616.904 KB) | DOI: 10.25077/josi.v19.n2.p44-66.2020

Abstract

Elevated stress has been widely associated with physical and physiological threats as well as reduced work performance. However, there is still a lack of studies that investigate whether stress influences concurrently physiological and objective work performance. The purpose of this study is to examine whether workers’ level of stress or negative emotional symptoms correlates with their physiological coherence and work performance. Eighteen female operators who reported high severity levels of stress, assessed using the Depression, Anxiety, and Stress Scale (DASS-42) were categorized as the risk group. The comparison group was formed by randomly selecting 18 of 99 female workers who had significantly lower DASS scores. Both participants attended one session of physiological measurement. Their work performance was observed by calculating their cycle time completing a product during five workdays. A significant difference in HRV between the two groups was also found in physiological and work performance measures. The results showed that workers in the risk group obtained significantly lower coherence levels and longer work cycle time than the control participants, indicating that negative emotional symptoms were parallel with physiological coherence and work performance. However, a weak correlation was found between work performance and negative emotional symptoms as well as physiological coherence. Despite the study limitations, our findings support to evidence the more complete picture of how stress affects female worker’s physiology and work performance, suggesting a need to implement effective workplace stress intervention. Further study is needed to be conducted among different group characteristics such as male and occupational settings.
Co-Authors Apandi, Aizat Haikal Khalil, Siti Nor Aisyah Sharrif, Adam Sutarto, Auditya Purwandini Wahab, Muhammad Nubli Abdul