Purpose: This article aims to evaluate the effectiveness of Monte Carlo simulation as a tool for demand forecasting. Design/Methodology/Approach: The study analyzes historical data on product sales, fits a theoretical distribution, and then applies Monte Carlo simulation to forecast demand for the next 15 days. Findings: The result of the research shows that Monte Carlo simulation can outperform more straightforward methods such as averaging, particularly in the presence of uncertainty or randomness Practical Implications: The study demonstrates how Monte Carlo simulation can improve demand forecasting accuracy, which is crucial for optimizing various business operations. Originality/Value: This study's novelty lies in demonstrating the practical application of Monte Carlo simulation for demand forecasting and comparing its performance against traditional methods.

Bartosz Przysucha Et Al (2024). Monte Carlo Simulation as a Demand Forecasting Tool, European Research Studies Journal Volume XXVIΙ Special Issue A, 103-113