Monte Carlo Simulation as a Demand Forecasting Tool

Bartosz Przysucha, Piotr Bednarczuk, Wlodzimierz Martyniuk, Ewa Golec, Michal Jasienski and Damian Pliszczuk

European Research Studies Journal, 2024, vol. XXVII, issue Special A, 103-113

Abstract: 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.

Keywords: Monte Carlo simulation; demand forecasting; business operations; sales prediction; uncertainty management. (search for similar items in EconPapers)
JEL-codes: C15 C53 D81 E27 L11 M11 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xxvii:y:2024:i:speciala:p:103-113

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