Proposed ProModel Implementation to Increase Production Quantity at PT ABC

Diki Muchtar; Ramlan Yuniar Velani; Suci Tresna Dewi Handayani

doi:10.51132/teknologika.v15i2.543

Diki Muchtar Sekolah Tinggi Teknologi Wastukancana
Ramlan Yuniar Velani Universitas Teknologi Bandung
Suci Tresna Dewi Handayani Universitas Teknologi Bandung

DOI: https://doi.org/10.51132/teknologika.v15i2.543

Keywords: Manufacturing Efficiency; Process Improvement; ProModel; Production Quantity; Simulation

Abstract

This study focuses on initiatives to enhance production quantity at PT ABC. The study applied a simulation-based approach using ProModel software to test various improvement scenarios without interfering with actual production activities. The data collected includes processing time at each workstation and machine capacity, which were used to build an accurate simulation model. The simulation results showed a significant increase in productivity from 35 to 78 units per hour after implementing the second improvement alternative involving additional manpower. This study offers practical and data-driven solutions to enhance production efficiency, reduce cycle time, and eliminate bottlenecks. The findings serve as a valuable reference for companies aiming to implement production line optimization strategies and simulation modeling to support continuous improvement in manufacturing performance.

References

A. Vaghefi and V. Sarhangian, “Contribution of simulation to the optimization of inspection plans for multi-stage manufacturing systems,” Computers & Industrial Engineering, vol. 57, no. 4, pp. 1226–1234, Nov. 2009, doi: https://doi.org/10.1016/j.cie.2009.06.001.

G. Werker, A. Sauré, J. French, and S. Shechter, “The use of discrete-event simulation modelling to improve radiation therapy planning processes,” Radiotherapy and Oncology, vol. 92, no. 1, pp. 76–82, Jul. 2009, doi: https://doi.org/10.1016/j.radonc.2009.03.012.

D. D. Purwito, Ramlan Yuniar Velani, Diki Muchtar, and S. T. D. Handayani, “Line Balancing with Simulation Approach (ProModel) on SMC Big Volume Lane in HA Export Department at PT. XYZ,” Jurnal Teknologika, vol. 15, no. 1, pp. 666–674, May 2025, doi: https://doi.org/10.51132/teknologika.v15i1.456.

T. Eftonova, M. Kiran, and M. Stannett, “Long-term Macroeconomic Dynamics of Competition in the Russian Economy using Agent- based Modelling,” International Journal of System Dynamics Applications, vol. 6, no. 1, pp. 1–20, Jan. 2017, doi: https://doi.org/10.4018/ijsda.2017010101.

S. Bhushan, “System Dynamics Base-Model of Humanitarian Supply Chain (HSCM) in Disaster Prone Eco-Communities of India,” International Journal of System Dynamics Applications, vol. 6, no. 3, pp. 20–37, Jul. 2017, doi: https://doi.org/10.4018/ijsda.2017070102.

D. Mourtzis, N. Papakostas, D. Mavrikios, S. Makris, and K. Alexopoulos, “The role of simulation in digital manufacturing: applications and outlook,” International Journal of Computer Integrated Manufacturing, vol. 28, no. 1, pp. 3–24, Sep. 2013, doi: https://doi.org/10.1080/0951192x.2013.800234.

Koulouris A., Misailidis N., Petrides D. (2021). Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food and Bioproducts Processing, 126, 317-333. https://doi.org/10.1016/j.fbp.2021.01.016

H. T. Shubbar, F. Tahir, and T. Al-Ansari, “Bridging Qatar’s food demand and self-sufficiency: A system dynamics simulation of the energy–water–food nexus,” Sustainable Production and Consumption, Feb. 2024, doi: https://doi.org/10.1016/j.spc.2024.02.017.

Y. Li, Y. He, R. Liao, Xin Xiao Zheng, and W. Dai, “Integrated predictive maintenance approach for multistate manufacturing system considering geometric and non-geometric defects of products,” vol. 228, pp. 108793–108793, Aug. 2022, doi: https://doi.org/10.1016/j.ress.2022.108793.

G. Kaur and R. G. Kander, “Supply Chain Simulation of Manufacturing Shirts Using System Dynamics for Sustainability,” Sustainability, vol. 15, no. 21, pp. 15353–15353, Oct. 2023, doi: https://doi.org/10.3390/su152115353.

K. Tsiamas and S. Rahimifard, “A simulation-based decision support system to improve the resilience of the food supply chain,” International Journal of Computer Integrated Manufacturing, vol. 34, no. 9, pp. 996–1010, Aug. 2021, doi: https://doi.org/10.1080/0951192x.2021.1946859.

S. Akter Jahan, M. Al Hasan, and H. El-Mounayri, “A framework for graph-base neural network using numerical simulation of metal powder bed fusion for correlating process parameters and defect generation,” Manufacturing Letters, vol. 33, pp. 765–775, Sep. 2022, doi: https://doi.org/10.1016/j.mfglet.2022.07.095.

Diki Muchtar, “Bottleneck Analysis in Garment Production Process Using Simulation Approach (ProModel),” Jurnal Teknologika, vol. 15, no. 1, pp. 643–651, 2025, doi: https://doi.org/10.51132/teknologika.v15i1.457.

C. L. Alves et al., “Integrated process simulation of porcelain stoneware manufacturing using flowsheet simulation,” Cirp Journal of Manufacturing Science and Technology, vol. 33, pp. 473–487, May 2021, doi: https://doi.org/10.1016/j.cirpj.2021.04.011.

A. Butrat and S. Supsomboon, “A Plant Simulation approach for optimal resource utilization: A case study in the tire manufacturing industry,” Advances in Production Engineering & Management, vol. 17, no. 2, pp. 243–255, Aug. 2022, doi: https://doi.org/10.14743/apem2022.2.434.

D. Muchtar, F. Herdiansyah, and I. Gumelar, “Simulasi Proses Produksi Kerupuk Kulit Dorokdok PD ABC Sukaregang–Garut,” Jurnal Teknologika, vol. 14, no. 1, pp. 80–90, May 2024, doi: https://doi.org/10.51132/teknologika.v14i1.364.