Line Balancing with Simulation Approach (ProModel) on SMC Big Volume Lane in HA Export Department at PT. XYZ

Dewanto Djoko  Purwito; Ramlan Yuniar Velani; Diki Muchtar; Suci Tresna Dewi  Handayani

doi:10.51132/teknologika.v15i1.456

Dewanto Djoko Purwito STT.Wastukancana
Ramlan Yuniar Velani Universitas Teknologi Bandung
Diki Muchtar Sekolah Tinggi Teknologi Wastukancana
Suci Tresna Dewi Handayani Universitas Teknologi Bandung

DOI: https://doi.org/10.51132/teknologika.v15i1.456

Abstract

This research aims to identify and overcome bottlenecks in production process at PT. XYZ Indonesia, especially in HA Export Department on SMC Big Volume Lane. Bottlenecks were detected in Visual Acc and Housing processes, which resulted in decreased efficiency and productivity. The method used in this study is line balancing with the Pro Model simulation approach, which allows analysis of improvement scenarios without disrupting ongoing operations. Data collected includes process time and machine capacity. Initial simulation results showed significant idle time 15,4%, with accumulation at Raw Material workstation (12.30%), Housing workstation (1.20%), and Visual Acc (1.90%). After the improvements were made, including increasing the Raw Material capacity to 20 pcs, the Housing process to 3 pcs, and Visual Acc to 3 pcs, the bottleneck was successfully eliminated from 15.4% became 0% and the production flow became more stable. This research provides practical solutions to improve efficiency and reduce cycle time, and can be a reference for companies in implementing line balancing and simulation methods to improve productivity in the manufacturing industry.

References

Reference

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.

A. Jayant, P. Gupta, and S. K. Garg, “Reverse logistics network design for spent batteries: a simulation study,” International Journal of Logistics Systems and Management, vol. 18, no. 3, p. 343, 2014, doi: https://doi.org/10.1504/ijlsm.2014.062820.

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.

V. Ngunzi, F. Njoka, and R. Kinyua, “Modeling, simulation and performance evaluation of a PVT system for the Kenyan manufacturing sector,” Heliyon, vol. 9, no. 8, p. e18823, Aug. 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e18823.

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.

M. Pekarcikova, P. Trebuna, M. Kliment, and M. Dic, “Solution of Bottlenecks in the Logistics Flow by Applying the Kanban Module in the Tecnomatix Plant Simulation Software,” Sustainability, vol. 13, no. 14, p. 7989, Jul. 2021, doi: https://doi.org/10.3390/su13147989.

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.