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Introduction
The area of risk management has received significant recognition in the field of project management in recent years (Kwak and Stoddard, 2004). Project managers and their superiors discovered that the process of identification, analysis, and assessment of possible project risks benefits them greatly in developing risk mitigation and contingency plans for complex project (Charette, 1996). This planning, in turn, helps the project manager better handle the difficult situations that invariably occur during projects, and therefore allows for more successful project completion.
One method used by some project managers during the risk analysis process is Monte Carlo simulation applications. This activity has been widely used for decades to simulate various mathematical and scientific situations, and it is mentioned often in project management curricula and standards, such as A Guide to the Project Management Body of Knowledge (Project Management Institute, 2004). Monte Carlo simulation has not yet, however, found a strong footing in the actual practice of project management in the "real world".
This paper reviews the applications of Monte Carlo simulation and its relevance to risk management and analysis in project management. It also outlines the uses of Monte Carlo simulation in other disciplines and in the field of project management. Finally, it discusses the pros and cons of Monte Carlo simulation applications in project management environment, some examples of proposed improvements or alternatives to Monte Carlo simulation, and concludes with a recommendation that more project managers should take advantage of this simple and useful tool in managing project risks and uncertainties.
Overview of Monte Carlo simulation
Brief history of Monte Carlo simulation
The Monte Carlo simulation encompasses "any technique of statistical sampling employed to approximate solutions to quantitative problems" (Monte Carlo Method, 2005). A model or a real-life system or situation is developed, and this model contains certain variables. These variables have different possible values, represented by a probability distribution function of the values for each variable. The Monte Carlo method simulates the full system many times (hundreds or even thousands of times), each time randomly choosing a value for each variable from its probability distribution. The outcome is a probability distribution of the overall value of the system calculated through the iterations of the model.
The invention of this method, especially the use.