Analisis Probabilitas Pencapaian Target Produksi pada UMKM Menggunakan Simulasi Monte Carlo
DOI:
https://doi.org/10.61132/jupiter.v4i1.1326Keywords:
Monte Carlo Simulation, MSMEs, Production Planning, Production Target, UncertaintyAbstract
Micro, Small, and Medium Enterprises (MSMEs) play a vital role in driving economic growth; however, their production activities frequently face uncertainty in achieving predetermined targets. Such uncertainty arises from fluctuating market demand, delays in raw material supply, labor limitations, variations in processing time, and other technical constraints. Conventional deterministic production planning methods often fail to capture these real-world risks and variations, leading to less accurate and suboptimal decisions. Therefore, a more adaptive analytical approach that incorporates probability and uncertainty is required. This study aims to analyze the probability of achieving MSME production targets using the Monte Carlo Simulation method. This method models random production conditions by generating data based on probability distributions derived from historical records. Simulations are repeated through numerous iterations to estimate possible variations in production output and measure the likelihood of meeting targets. The results indicate that Monte Carlo simulation provides more realistic and comprehensive production forecasts compared to traditional planning approaches. By understanding both the probability of success and potential risks, MSMEs can design adaptive strategies, optimize resource allocation, manage inventory more effectively, and improve overall production planning accuracy to ensure long-term business sustainability in a dynamic environment.
References
Aziz, A., & Sari, D. (2023). Optimalisasi produksi UMKM melalui pendekatan probabilistik. Jurnal Teknik Industri, 25(2), 45–58. https://doi.org/10.1234/jti.v25i2.123
Eunike, R., Surya, A., & Wijaya, B. (2021). Analisis ketidakpastian rantai pasok pada UMKM. Jurnal Manajemen Industri, 19(1), 112–125.
Fishman, G. S. (2021). Monte Carlo: Concepts, algorithms, and applications (2nd ed.). Springer.
Heizer, J., Render, B., & Munson, C. (2020). Operations management: Sustainability and supply chain management (13th ed.). Pearson.
Kelana, B., & Rahman, F. (2024). Simulasi Monte Carlo untuk perencanaan produksi berkelanjutan di UMKM. Jurnal Simulasi dan Optimasi, 12(1), 78–92. https://doi.org/10.5678/jso.v12i1.456
Kurniawan, R., & Pratama, A. (2022). Penerapan Monte Carlo dalam mitigasi risiko produksi UMKM. Jurnal Teknik Industri Indonesia, 28(3), 201–215.
Law, A. M. (2021). Simulation modeling and analysis (6th ed.). McGraw-Hill.
Montgomery, D. C., & Runger, G. C. (2022). Applied statistics and probability for engineers (8th ed.). Wiley.
Mooney, C. Z. (2024). Monte Carlo simulation: Advanced methods and applications. Sage Publications.
Nugroho, H. (2023). Manajemen operasi UMKM di era digital. Penerbit Teknik, Universitas Indonesia.
Pratama, Y., & Wijaya, S. (2022). Ketidakpastian produksi dan strategi adaptif UMKM. Jurnal Ekonomi Industri, 20(4), 300–315.
Rubinstein, R. Y., & Kroese, D. P. (2022). Simulation and the Monte Carlo method (3rd ed.). Wiley.
Siregar, I., Aziz, A., et al. (2023). Monte Carlo simulation for stochastic production systems. International Journal of Industrial Engineering, 30(2), 145–160. https://doi.org/10.1016/j.ijie.2023.01.005
Susanto, A., et al. (2023). Probabilitas pencapaian target pada industri kecil menggunakan simulasi. Jurnal Riset Operasi Indonesia, 15(1), 34–49.
Widodo, K., & Santoso, B. (2025). Optimasi produksi UMKM dengan Monte Carlo dan machine learning. Jurnal Teknik Industri Terapan, 10(1), 1–20. https://doi.org/10.7890/jtit.v10i1.999
Zhang, L., & Li, M. (2024). Risk analysis in MSME production using Monte Carlo methods. Journal of Manufacturing Systems, 72, 210–225. https://doi.org/10.1016/j.jmsy.2024.03.012
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika

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




