Introduction
Real projects are very complex because they have multiple risks that have the potential to trigger other risks. Risks can have different outcomes. For example, in one scenario a risk will delay a task while in another scenario the same risk will cancel it. In addition, some risks may be correlated with each other. Therefore, the problem is how to model these complex relationships so that it becomes practical for project management purposes.
If uncertainties are expressed as events with outcomes, it will significantly simplify project risk management calculations. Once this data is available, it is possible to perform quantitative analyses and determine how the uncertainties in each particular task will affect the main project parameters, i.e. project duration and finish time, cost, and success rate.
The Event Chain Methodology sets out to solve this problem. It is a method of modeling uncertainties for different time-related business and technological processes including project management. Event Chain Methodology is not a simulation or risk analysis method. It is based on existing analysis methodologies including Monte Carlo simulation, Bayesian approach and others.
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Abstract
