Product Demand Prediction in E-Commerce Systems Using the Monte Carlo Method

Abstract

The rapid growth of e-commerce has intensified demand uncertainty, creating significant challenges in inventory management due to the risks of overstock and stockout conditions. Fluctuating consumer behavior and dynamic digital market trends require forecasting approaches capable of modeling probabilistic variability rather than relying solely on deterministic estimates. This study aims to analyze and implement the Monte Carlo simulation method to forecast product demand in an e-commerce system and to evaluate its effectiveness in supporting optimal inventory decision-making. The research adopts a quantitative approach using historical monthly sales data of laptop products collected over a ten-month period. The Monte Carlo method was applied by constructing probability distributions, calculating cumulative probabilities, defining random number intervals, and performing repeated simulations to generate demand predictions. The simulation results produced an average predicted demand of 139 units, closely aligned with the historical average of 137 units, with a Mean Absolute Deviation (MAD) of 2 units, indicating a low prediction error. These findings demonstrate that the Monte Carlo approach effectively captures demand variability and provides accurate probabilistic estimates. The study implies that integrating Monte Carlo simulation into e-commerce inventory planning can enhance risk-based decision-making, improve stock control accuracy, and reduce potential financial losses associated with demand uncertainty.