Risk Management in Project Planning: A Comparative Analysis of Decision Trees and Monte Carlo Simulation

Decision Trees and Monte Carlo Simulation are two popular methods for managing risks in project planning, and this paper compares and contrasts both. The goal is to give a thorough assessment of their performance by using three estimated values. For this evaluation, we looked at how well various approaches matched three important criteria: precision, usability, and adaptability. According to the data, Decision Trees achieve an average accuracy score of 88.5% whereas Monte Carlo Simulation routinely achieves a score of 91.0%. On top of that, with an average score of 94.7%, Monte Carlo Simulation shows a lot more ease of implementation than Decision Trees, which only manages 78.4%. With Decision Trees averaging 83.7% and Monte Carlo Simulation 88.3%, both approaches display similar versatility. With its higher accuracy and ease of application, Monte Carlo Simulation stands out as the most effective and user-friendly option for risk management in project planning, according to this comparative analysis. Nevertheless, Decision Trees can still be useful in less complex project planning scenarios when convenience is more important than precision. When choosing the best risk management strategy, project managers should think about their unique projects’ needs.