Simulasi Prediksi Penjualan Harian Menggunakan Metode Monte Carlo pada Usaha Roti Skala UMKM
DOI:
https://doi.org/10.31004/jutin.v9i1.55835Keywords:
Monte Carlo Simulation, Sales Prediction, Micro and Small Enterprises, Production Planning, Risk AnalysisAbstract
This study aims to apply the Monte Carlo simulation method to predict daily sales in a small-scale bakery enterprise to support risk-based production planning. The data used consisted of historical daily sales records analyzed to obtain statistical parameters, including mean and standard deviation. The results indicate that daily demand follows a normal distribution with an average of 151.73 units. A Monte Carlo simulation with 10,000 iterations was conducted to estimate the distribution of daily profit and associated risk levels. The findings show an average daily profit of IDR 199,029 with a 95% Value at Risk (VaR) of IDR 99,952. Furthermore, a positive correlation of 0.629 was identified between demand and profit. These results demonstrate that the Monte Carlo method is effective in modeling demand uncertainty and supporting more optimal and efficient production decision-making in micro and small enterprises.References
Bouziane, S. E., Arab, S., Mohamed Khadir, M. T., & Sabri, S. (2025). Uncertainty-aware energy forecasting and environmental impact simulation using Monte Carlo and deep learning. Journal of Simulation. https://doi.org/10.1080/17477778.2025.2574719
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
Epicoco, N., & Massaro, A. (2025). A dynamic approach to predict systems requirements for continuous improvement. Computers and Industrial Engineering, 208. https://doi.org/10.1016/j.cie.2025.111415
Fenneteau, B., Deck, O., Mehdizadeh, R., & Laigle, F. (2025). Improved Squeezing Prediction of Deep Tunnels Within Highly Fractured Rock Mass: Update of the Hoek & Marinos Curve Under Uncertainties. Rock Mechanics and Rock Engineering, 58(8), 8557–8574. https://doi.org/10.1007/s00603-025-04574-w
Fierce, L., Yao, Y., Easter, R., Ma, P.-L., Sun, J., Wan, H., & Zhang, K. (2024). Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions. Journal of Aerosol Science, 181. https://doi.org/10.1016/j.jaerosci.2024.106388
Ghezal, A., & Alzeley, O. (2024). Probabilistic properties and estimation methods for periodic threshold autoregressive stochastic volatility. AIMS Mathematics, 9(5), 11805–11832. https://doi.org/10.3934/math.2024578
Huang, M., Wu, J., Wang, Y., Zhong, J., Guan, K., & Yu, B. (2026). A probabilistic creep damage model for studying the dispersion of small punch creep test and uniaxial tensile creep test. International Journal of Pressure Vessels and Piping, 222. https://doi.org/10.1016/j.ijpvp.2026.105764
Khan, M. S., Ali, A., Suhail, M., & Kibria, B. M. G. (2025). On some two parameter estimators for the linear regression models with correlated predictors: simulation and application. Communications in Statistics: Simulation and Computation, 54(10), 3933–3947. https://doi.org/10.1080/03610918.2024.2369809
Li, J., Wang, Y., Fu, Y., & Chen, Y. (2025). Ecological risk transmission network and its response characteristic in heavy metal-contaminated soil ecosystem under different scenarios. Environmental Pollution, 385. https://doi.org/10.1016/j.envpol.2025.127083
Li, K.-Q., Yin, Z.-Y., & Liu, Y. (2026). AGFNN: A smart platform for uncertainty-aware prediction of freezing time in artificial ground freezing. Tunnelling and Underground Space Technology, 170. https://doi.org/10.1016/j.tust.2025.107331
Liu, Y., Jiang, Y., Nasar, N., Bajón Fernández, Y., Longhurst, P., Guo, W., Lei, M., & Bortone, I. (2026). Improving ADM1 predictions via Bayesian analysis for continuous anaerobic digestion. Journal of Environmental Management, 398. https://doi.org/10.1016/j.jenvman.2025.128537
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
Rajagopal, S., Hmar, R. V, Mookherjee, D., Ghatak, A., Shanbhag, A. P., Katagihallimath, N., Venkatraman, J., Ks, R., & Datta, S. (2022). Validated in Silico Population Model of Escherichia coli. ACS Synthetic Biology, 11(8), 2672–2684. https://doi.org/10.1021/acssynbio.2c00097
Schilling, F., Beesigamukama, D., & Tanga, C. M. (2026). Reliability of approaches for measuring soil organic carbon and implications for results-based payments for smallholder carbon farming. Journal of Environmental Management, 398. https://doi.org/10.1016/j.jenvman.2026.128562
Špetlík, M., Březina, J., & Laloy, E. (2024). Deep learning surrogate for predicting hydraulic conductivity tensors from stochastic discrete fracture-matrix models. Computational Geosciences, 28(6), 1425–1440. https://doi.org/10.1007/s10596-024-10324-8
Taneja, S. B., Jaiswal, S., Bisht, S., Jindal, V., & Bedi, P. (2025). MOP-N: A Hybrid AI Model for Automated Deployment of Guns in Dynamic Wargaming Scenario. Defence Science Journal, 75(2), 179–187. https://doi.org/10.14429/dsj.20246
Wu, H., Yang, Y., & Li, W. (2025). Dynamic prediction of the land-use carbon balance in urban agglomerations under the two-way effects of climate and socioeconomic interactions. Sustainable Cities and Society, 130. https://doi.org/10.1016/j.scs.2025.106555
Zhang, N., Xu, S., Li, X., Yang, M., Liu, X., & Chang, Y. (2026). A data-efficient fragility assessment methodology for deepwater drilling risers under combined wave and current loads via multi-objective genetic optimization. Ocean Engineering, 343. https://doi.org/10.1016/j.oceaneng.2025.123356
Zhang, X., Lei, X., Liu, Y., Li, X., & Yang, Z. (2025). A Method for Quantifying Frequency Regulation Capacity Requirements Based on Credible Prediction of Fluctuation Extremum in the Dispatch Period. Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 45(21), 8431–8444. https://doi.org/10.13334/j.0258-8013.pcsee.240898
Zheng, X., CRESSIE, N., Clarke, D. A., McGeoch, M. A., & Zammit-Mangion, A. (2025). Spatial-statistical downscaling with uncertainty quantification in biodiversity modelling. Methods in Ecology and Evolution, 16(4), 837–853. https://doi.org/10.1111/2041-210X.14505
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Rika Romatona, Sabikah Nur Naylah, Baitul Maharani Lubis, Tika Gajah, Yuhani Yuhani, Bidara Jelita Maha, Erza Arkan zharif

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

