Application of Risk Management and Monte Carlo simulation on a Construction Project (Case Study)

One of the construction manager's primary roles in fixed priced construction contracts is to ensure that the General contractor delivers the project in time (by monitoring the project schedule) and according to the required quality. However, the complexity and the constant changes in construction projects lead construction management companies like RECCHIENGINEERING to find that monitoring the ongoing process only is not enough to make sure that the project finishes in time. They need to find a way to look ahead by predicting what activities may delay the project and alarm the owner and the general contractor about them. Also, to propose actions can be taken to mitigate the delay so that they keep tracking on ongoing activities and anticipating any delay and avoid it as possible. This thesis aimed to provide RECCHIENGINEERING with a methodology to predict the delay and tackle it by providing a structured forecasting and mitigation plan. The methodology is an application of risk management on construction projects. Risk management is the process of identifying, analyzing, and assessing risks that may impact the project -schedule in this case- either positively(opportunities) or negatively(threats) and implement a response plan to eliminate or mitigate threads and augment opportunities. One of RECCHINGINEERING'S projects has been taken as a case study. Risk management has been applied by following the PMI standards, which is started by a risk initiation plan in which the main project scope and objectives have been defined. This step is followed by the risk identification process in which all possible risk has been identified using different techniques and registered in the risk register. After that, a risk breakdown structure (RBS), qualitative risk analysis has been applied, and semi-quantitative risk analysis has been. All risk data and the project's Gantt chart have been imported into RISKYPROJECT Professional to obtain sensitivity analysis using Monte Carlo simulation. In the end, a response plan has been implemented. The result of qualitative and semi-quantitative risk analysis is represented in the Cross Risk breakdown Matrix (RBM), from which activities are classified according to how much risk is associated with each activity. Also, the most significant risk sources are defined and classified. The result of sensitivity analysis is tornado charts which order activities depending on how likely each activity would fall into the critical path.