Simulasi Monte Carlo untuk Analisis Kinerja Sistem Antrian pada Operasional Coffee Shop Skala Kecil
DOI:
https://doi.org/10.31004/jutin.v9i1.55849Keywords:
Monte Carlo Simulation, Queueing System, Coffee Shop, Risk Analysis, Operational PerformanceAbstract
This study aims to analyze the performance of a queueing system in a small-scale coffee shop operation using the Monte Carlo simulation method based on historical data from 2022–2026. Coffee shop operations exhibit stochastic characteristics influenced by fluctuations in customer arrivals and service time variability. Data on daily visitors, revenue, cost, and profit were processed using Microsoft Excel to construct empirical probability distributions. The simulation was executed through thousands of iterations to ensure statistical stability. The results indicate that the model effectively captures operational uncertainty, with convergent average daily profit and measurable downside risk assessed through percentile analysis and Value at Risk (VaR). The findings provide an analytical foundation for managerial decision-making regarding service capacity and cost control strategies. Monte Carlo simulation proves to be an effective tool for performance evaluation and risk management in small-scale service businesses.References
Adel, M. E., & Mack, C. A. (2025). Via to line and line-end contact failures: modeling and the impact of stochastics. Journal of Micro/Nanopatterning, Materials and Metrology, 24(1). https://doi.org/10.1117/1.JMM.24.1.014001
Amato, S., & Arnold, A. (2025). A Data-Informed Mathematical Model of Microglial Cell Dynamics During Ischemic Stroke in the Middle Cerebral Artery. Bulletin of Mathematical Biology, 87(2). https://doi.org/10.1007/s11538-025-01412-6
Aziminezhad, M., Shadabfar, M., Bediwy, A., & Mohamedelhassan, E. (2026). AI-based meta model for predicting the performance of low-carbon concrete, considering the effects of multiple waste materials. Cleaner Materials, 19. https://doi.org/10.1016/j.clema.2025.100364
Bi, S., Beer, M., Cogan, S., & Mottershead, J. (2023). Stochastic Model Updating with Uncertainty Quantification: An Overview and Tutorial. Mechanical Systems and Signal Processing, 204. https://doi.org/10.1016/j.ymssp.2023.110784
Brilon, W., Wu, N., & Koenig, R. (2023). Delays and Queue Lengths at Traffic Signals With Two Greens in One Cycle. Transportation Research Record, 2677(2), 828–838. https://doi.org/10.1177/03611981221108981
Cao, X., He, W., Shi, Y., An, T., Wang, X., Liu, F., Zhao, Y., Zhou, P., Chen, C., & He, J. (2023). EMMTE: An Excel VBA tool for source apportionment of nitrate based on the stable isotope mixing model. Science of the Total Environment, 868. https://doi.org/10.1016/j.scitotenv.2023.161728
de Carvalho, A., Martins, A., Mota, A. F., & Loja, M. A. R. (2024). Variability on Functionally Graded Plates’ Deflection Due to Uncertainty on Carbon Nanotubes’ Properties. Mathematical and Computational Applications, 29(2). https://doi.org/10.3390/mca29020022
Dębski, R., Gąsior, W., Gierlotka, W., & Dȩbski, A. (2025). Uncertainty-Aware Design of High-Entropy Alloys via Ensemble Thermodynamic Modeling and Search Space Pruning. Applied Sciences (Switzerland), 15(16). https://doi.org/10.3390/app15168991
Heni, H., Arona Diop, S., Renaud, J., & Coelho, L. C. (2023). Measuring fuel consumption in vehicle routing: new estimation models using supervised learning. International Journal of Production Research, 61(1), 114–130. https://doi.org/10.1080/00207543.2021.1948133
Juini, M., Arfa, H., Bouchoucha, F., Abed, I., & Ichchou, M. (2025). Bending Vibration in Uncertain Function Gradient Material Structure Through Stochastic Wave Finite Element Method. Journal of Vibration Engineering and Technologies, 13(7). https://doi.org/10.1007/s42417-025-02101-3
Le, X.-H., Hien, L. T. T., Ho, H. V, & Lee, G. (2024). Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets. Journal of Hydroinformatics, 26(7), 1572–1588. https://doi.org/10.2166/hydro.2024.297
Li, G., Xue, Y., Qu, C., Qiu, D., Liu, Q., & Ma, X. (2023). Stability classification probability model of loess deposits based on MCS-Cloud. Environmental Science and Pollution Research, 30(11), 31218–31230. https://doi.org/10.1007/s11356-022-24346-1
Liu, J., Cheng, Z., Yi, P., Li, W., & Wang, L. (2024). Predictive evaluation of city sustainability based on benchmarking method - A case study of 34 cities in northeastern China. Sustainable Cities and Society, 112. https://doi.org/10.1016/j.scs.2024.105627
Ma, Y., Qu, D., & Pyrozhenko, M. (2026). Bio-RegNet: A Meta-Homeostatic Bayesian Neural Network Framework Integrating Treg-Inspired Immunoregulation and Autophagic Optimization for Adaptive Community Detection and Stable Intelligence. Biomimetics, 11(1). https://doi.org/10.3390/biomimetics11010048
Momeni, A., Piratla, K. R., Anderson, A., Chalil Madathil, K., & Li, D. (2023). Stochastic model-based leakage prediction in water mains considering pipe condition uncertainties. Tunnelling and Underground Space Technology, 137. https://doi.org/10.1016/j.tust.2023.105130
Nechval, N. A., Berzins, G., & Nechval, K. (2023). New Applications of Computational Intelligence for Constructing Predictive or Optimal Statistical Decisions under Parametric Uncertainty. Automatic Control and Computer Sciences, 57(5), 473–489. https://doi.org/10.3103/S0146411623050085
Wannenburg, J. W., Ngcobo, G. N., & Heyns, P. S. (2026). Development of a predictive, risk-based model to assess the effects of maintenance decisions on vertical mine shaft structures. Tunnelling and Underground Space Technology, 171. https://doi.org/10.1016/j.tust.2026.107481
Zheng, Z., & Shuang, Q. (2026). Dynamic scenario analysis and prediction of embodied carbon emissions in China’s building sector: A hybrid interpretable machine learning model. Environmental Impact Assessment Review, 116. https://doi.org/10.1016/j.eiar.2025.108119
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Erza Arkan Zharif, Putri Bintang Lubis, Putri Najiha, Akbar Abdillah, Tasya Dewi Mutiara
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
