Analysis of Bolt Former Machine Effectiveness Using Overall Equipment Effectiveness (OEE) and Six Big Lossess


Analisis Efektivitas Mesin Bolt Former Menggunakan Overall Equipment Effectiveness (OEE) dan Six Big Losses


  • (1) * Haekal Ardy            Universitas 17 Agustus 1945 Surabaya  
            Indonesia

  • (2)  Wiwin Widiasih            Universitas 17 Agustus 1945 Surabaya  
            Indonesia

    (*) Corresponding Author

Abstract

XYZ a manufacturer of nuts and bolts, frequently faced bolt former breakdowns that resulted in huge losses and disrupted the company's operational efficiency and reputation. To solve this problem, PT XYZ needs to implement an effective maintenance strategy and closely monitor machine conditions to reduce the risk of breakdowns and ensure smooth production. This can be analyzed using the OEE method that exists in the company to detect early symptoms of damage, and continuous monitoring of machine conditions. Based on the calculation (OEE) before maintenance scheduling is made on the bolt former machine is currently 69.34%, which is below the standard (JIPM) of 85%. Therefore, the machine still needs to increase productivity through the OEE and Six Big Losses methods and then proceed with analyzing the problem using a fishbone diagram. The results of the analysis found that the biggest factor of time loss was reduced speed losses of 23.15%. The value of setup and adjustment losses was 7.82%, idling and minor stoppages losses were 7.44%, equipment failure losses were 3.12%, defect losses were 0.99%, and scrap losses were 0.11%. After the implementation of OEE, it has an impact on the level of productivity with the calculation results increasing to 77.78%. The availability value reached 89.90%, the performance value was 87.75%, and the quality value was 99.57%.

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Published
2024-06-30
 
How to Cite
Ardy, H., & Widiasih, W. (2024). Analysis of Bolt Former Machine Effectiveness Using Overall Equipment Effectiveness (OEE) and Six Big Lossess. PROZIMA (Productivity, Optimization and Manufacturing System Engineering), 8(1), 25-34. https://doi.org/10.21070/prozima.v8i1.1688
Section
Articles