Bottleneck Analysis in Garment Production Process Using Simulation Approach (ProModel)
Abstract
Garment industry is a labor-intensive manufacturing sector with a complex production flow, highly susceptible to bottlenecks. Unresolved bottlenecks can lead to decreased efficiency, increased waiting times, and reduced productivity. This research aims to analyze bottlenecks in the garment production process using a simulation approach with ProModel software. The research method was carried out using a quantitative-descriptive approach, starting with production system selection, process flow mapping, and simulation model development and testing. The results of the original model simulation show a production output of 1.719 units, with a bottleneck of 3,86% and a cost of USD 38,01. This situation requires improvement, which was achieved through scenario 1, where production capacity at each location was increased, considering a relatively similar cost of USD 41,40. This change reduced the bottleneck to 0,60%, and total production increased to 1.811 units. In scenario 2, with a similar cost, the author recommended further adjustments to the production capacities at the required processes. After conducting the simulation, total production increased to 1.908 units, with a cost of USD 41,42. The simulation using ProModel proved effective in systematically identifying and evaluating bottleneck solutions. This research provides practical recommendations for garment industry management in making data-driven decisions to improve production efficiency.
keyword: Bottleneck; Garment Industry; Simulation; ProModel; Production Efficien
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.
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.
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.
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.
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.
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.
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.
O. Bongomin, J. I. Mwasiagi, E. O. Nganyi, and I. Nibikora, “Simulation metamodeling approach to complex design of garment assembly lines,” PLOS ONE, vol. 15, no. 9, p. e0239410, Sep. 2020, doi: https://doi.org/10.1371/journal.pone.0239410.
