Automatic Filling Machine for Metracide 1 Liter Product Variant at Global Medipro Investama LLC
Main Article Content
Abstract
Abstract – Global Medipro Investama LLC previously used a CNC liquid filling machine to fill the Metracide 1 liter variant product into bottles. This machine very often produces output that was not accordance with the company's specifications. Based on data from 3 production batches, the average yield rate score obtained was 51.96%. The low-yield rate score indicates that the production process is ineffective and inefficient because of the unstable filling process. This research aims to design and manufacture an automatic filling machine with four nozzles and use PLC as its controller so that the production process becomes more ef-fective and efficient by increasing the rated yield and quantity of output and speeding up the cycle time by eliminating the manual weighing process using loadcell and weighing indicator. Based on data from 3 batches of production using the new machine, the average yield rate score obtained was 98.51%, which increased by 46.3%, significantly more than the old machine of 51.96%. The machine also managed to speed up the production cycle time at the filling station. To produce 495 bottles only takes 33 minutes, making the production process 75.49% faster than the old machine of 134 minutes. The increase in output yield and quantity, and the reduction in cycle time show that the production process has become more efficient and effective.
[Manuscript received: 8 Apr 2024 | Accepted: 4 Jul 2024 | Published: : 30 Sep 2024]
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
R. Salih, S. Kamal, E. Adil and M. Ameen, “Designing a PLC-based bottle filling machine with conveyor system and reduced filling time using ladder logic,” Academic Journal of Nawroz University, vol. 13, no. 1, pp. 535–544, 2024.
DOI: https://doi.org/10.25007/ajnu.v13n1a1766
R. Faiber, J. Betancourt, P. Salgado, J. Julián and M. Mosquera, “Automatic gallon filling system controlled by programmable logic controller-PLC,” ARPN Journal of Engineering and Applied Sciences, vol. 18, no. 5, pp. 502-507, 2023.
DOI: https://doi.org/10.59018/032371
V. Sundarnath, “Design of IoT based smart ration dispensing system using loadcell feedback to prevent ration fraudulence,” International Journal of Creative Research Thoughts, vol. 9, pp. 563–569, 2021.
DOI: https://doi.org/10.1729/Journal.27234
A. Yermia Tobe, D. Widhiyanuriyawan and L. Yuliati, “The integration of overall equipment effectiveness (OEE) method and lean manufacturing concept to improve production performance (case study: fertilizer producer),” Journal of Engineering and Management in Industrial System, vol. 5, no. 2, pp. 102–108, 2017.
DOI: https://doi.org/10.21776/ub.jemis.2017.005.02.7
R. Sumargo and A. Makmur, “Automatic OEE data collection and alert system for food industry,” Sinkron, vol. 8, pp. 2158–2167, 2023.
DOI: https://doi.org/10.33395/sinkron.v8i4.12953
M.R. Ullah, S. Molla, I. Siddique, A. Siddique and M.M. Abedin, “Optimizing performance: a deep dive into overall equipment effectiveness (OEE) for operational excellence,” Journal of Industrial Mechanics, vol. 8, pp. 26–40, 2023.
DOI: https://doi.org/10.46610/JoIM.2023.v08i03.004
H. Prabowo, R. Hutmi and I. Dewata, “Optimizing digging equipment productivity using overall equipment effectiveness (OEE) method in coal overburden mining activities,” INVOTEK: Jurnal Inovasi Vokasional dan Teknologi, vol. 23, pp. 99–108, 2023.
DOI: https://doi.org/10.24036/invotek.v23i2.1097
P.C. Rahayu and K.A. Wicaksono, “Real time OEE monitoring for intelligent manufacture technology,” 15th International Conference on Mechanical and Intelligent Manufacturing Technologies, pp. 80–83, 2024.
DOI: https://doi.org/10.1109/ICMIMT61937.2024.10585713
A. Arifin, I. Tama and Y. Sumantri, “Analysis the effectiveness of CNC turning machines type XTRA 420 using the overall equipment method effectiveness (OEE),” Journal of Engineering and Management in Industrial System, vol. 11, no. 1, pp. 46–53, 2023.
DOI: https://doi.org/10.21776/ub.jemis.2023.011.01.5
S.A. Lesmana, “Implementation and analysis of overall equipment effectiveness (OEE) methodology in tube welding machine productivity at PT Denso Indonesia,” International Journal of Engineering Research and Advanced Technology, vol. 8, no. 8, pp. 20–31, 2022.
DOI: https://doi.org/10.31695/ijerat.2022.8.8.4
E. Febianti, K.D. Safitri, K. Kulsum, B. Kurniawan, P.F. Ferdinant and H. Setiawan, “Measurement of effectiveness of food processing machine through overall equipment effectiveness (OEE),” Journal Industrial Servicess, vol. 8, no. 1, pp. 46-52, 2022.
DOI: https://doi.org/10.36055/jiss.v8i1.14076
F. Achmadi, B. Harsanto and A. Yunani, “Cycle time analysis of weapon assembly process in PT Pindad (Persero),” Operational Excellence, vol. 13, pp. 159–168, 2021.
DOI: https://doi.org/10.22441/oe.2021.v13.i2.015
Z. Feng, M. Wang, J. He and W. Xiao, “Real-time equipment state monitoring and cycle time calculation method based on DTW-KNN,” International Conference on Intelligent Computing and Signal Processing 2023, pp. 1350–1353, 2023.
DOI: https://doi.org/10.1109/ICSP58490.2023.10248673
G.W. Horn and W. Podgorski, “The cycle time of front end IC manufacturing and amhs variability,” China Semiconductor Technology International Conference 2019, pp. 1–3, 2019.
DOI: https://doi.org/10.1109/CSTIC.2019.8755804
B. Thangavel, C. Venugopal, S. Immanuel, J.E. Raja and W. C. Chua, “Design and development of automated solar grass trimmer with charge control circuit,” International Journal on Robotics, Automation, and Sciences, vol. 6, no. 1, pp. 36–45, 2024.
DOI: https://doi.org/10.33093/ijoras.2024.6.1.6
T. Bhuvaneswari, V. Chitra, and G.C. Cheng, “Voice controlled home automation system design,” International Journal on Robotics, Automation, and Sciences, vol. 5, no. 2, pp. 94–100, 2023.
DOI: https://doi.org/10.33093/ijoras.2023.5.2.12
Y.S. Bong and G.C. Lee, “A Contactless Visitor Access Monitoring System,” International Journal on Robotics, Automation, and Sciences, vol. 3, pp. 33–41, 2021.
DOI: https://doi.org/10.33093/ijoras.2021.3.6
S.A.B. Siddik, W.N.A.A.W Husin and T. Muniandy, “The enhanced speech recognition in automated home lighting system using adaptive time-frequency domain noise removal algorithm filter,” International Journal on Robotics, Automation, and Sciences, vol. 6, no. 1, pp. 20–28, 2024.