Simulação com o método Monte Carlo: uma ferramenta para análise de risco no gerenciamento de projetos

Abstract

Project management is a complex and critical process that involves collaboration, technical knowledge, and investments. In a competitive environment, organizations constantly seek improvement opportunities and reevaluate their business models due to the challenges they face. Consequently, project risks arise from uncertainty regarding expected outcomes, such as deadlines and costs. Risk management aims to minimize these risks to ensure that activities meet the project's needs. Risk analysis is a tool of risk management and can be qualitative or quantitative, depending on available resources. Within quantitative analysis, Monte Carlo Simulation (MCS) is a widely used method, especially for risks related to schedule delays and cost overruns. In this context, this work focuses on a case study of risk analysis applying MCS in project schedule risk management. The objectives include contextualizing the importance of risk analysis, analyzing the theoretical foundation of the Monte Carlo method, performing a risk analysis in a project, and developing a risk analysis program in Python. The justification for this study lies in the need to improve the success rate of construction projects, which often exceed deadlines and budgets, as risk management is crucial in dealing with the inherent uncertainty in these projects. Overall, the study contributes to a more effective and precise approach in project management, adapting to the demands of the constantly evolving market. The results include model verification for algorithm validation and a case study. It can be observed that considering the sum of phases where there is a 5% probability of the project ending in 590 days and 95% probability of ending in 669 days. Additionally, a convergence analysis ensured that the number of samples was sufficient for the study and a brief comparison of the main distributions used in risk analysis. This information allows stakeholders to better understand how the schedule can be affected by risks and take measures to mitigate these impacts in advance.