Applying the Monte Carlo Method for Modeling Order Fulfillment with Consideration of Supply Risk

Abstract

This paper presents the application of the Monte Carlo method for modelling order fulfilment, taking into account supply risks and delays. The method allows for the consideration of stochastic events and uncertainties in supply chains, which are becoming increasingly complex and vulnerable to various risks, such as production failures, transportation issues, and external factors. Using probabilistic distributions, the Monte Carlo method enables forecasting the frequency and impact of delays, supporting proactive decision-making in logistics management. The developed model assesses the likelihood of on-time order fulfilment under uncertainty, demonstrating the effectiveness of Monte Carlo simulations. The simulation results provide insights into delay patterns, risk factors, and potential strategies for minimizing them, creating opportunities for supply chain optimization.