Monte Carlo Simulation as a Predictive Tool in Addressing Demand Volatility
DOI:
10.29303/jppipa.v11i11.13413Published:
2025-11-25Downloads
Abstract
This research aims to estimate the probability distribution of 330 mL product demand at PT XYZ using Monte Carlo simulation to support risk-based production and inventory decisions. Monthly demand data for one year was used to form an empirical probability distribution, followed by random number generation using the Mixed Congruential Generator method and the execution of 100,000 simulation iterations. The simulation results show that demand has a wide range of uncertainty, with the 90th and 95th percentiles falling within the range of 7,000–7,800 units per month. Extreme demand risks were also identified, with the probability of demand exceeding 7,460 units reaching 22.4%. Based on these results, quantile-based safety stock calculations indicate an additional need of approximately 1,800 units to achieve a 90% service level. Validation using Kolmogorov–Smirnov and Chi-Square tests shows that the simulation distribution is consistent with the historical distribution, thus validating the model. Overall, this research produced an accurate and reliable probabilistic framework to support capacity planning, buffer inventory determination, and demand uncertainty risk mitigation at PT XYZ.
Keywords:
Demand forecasting Inventory Management Monte Carlo Simulation Probability DistributionReferences
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