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Project Management using Event Chain Methodology
Overview of Event Chain Methodology
Therefore, we are drawn to the conclusion that if uncertainties are expressed as events with outcomes, it will significantly simplify our project management estimations. By mitigating some biases in estimation such as availability and anchoring, we can develop numbers that are more accurate for task duration, cost, and other project parameters. Once we have this data, we can perform quantitative analysis and determine how uncertainties in each particular task will affect the main project parameters: project duration, cost, finish time, and success rate. However, real projects are very complex; they have multiple risks that have the potential to trigger other risks. Risks can have different outcomes; in one scenario a risk will delay a task, in another scenario the same risk will cancel it. In addition, some risks are correlated with each other. Therefore, the problem remains how to model these complex processes so that it becomes practical for project management.
Event Chain Methodology proposes to solve this problem. It is important to note that Event Chain Methodology is not a simulation or risk analysis method. It is based on existing analysis methodologies including Monte Carlo simulation, Bayesian Believe Network and others.
Event Chain Methodology is a method of modeling of uncertainties for different time-related business and technological processes including project management.
Event Chain Methodology is based on six major
principles:
1. An activity (task) in most real life processes is not a continuous uniform procedure. It is affected by external events, which transform an activity from one state to another. It is important to point out that these events occur during the course of an activity. The moment, when an event occurs, in most cases is probabilistic and we can define it using statistical distribution. Events (risks) can have a negative impact on the project. For example, the event “delayed arrival of component” can cause a delay in an activity. However, the opposite is also true, events can positively affect an activity, e.g. reduce costs.
2. Events can cause other events, which will create event chains. These event chains can significantly affect the course of the project. For example, requirement changes can cause a delay of a task. To accelerate the activity, a resource is allocated from another activity; which can lead to a missed deadline. Eventually, this can lead to the failure of the project. Events may instantly trigger other events or transform an activity to another state. The notion of state is very important as states can serve as a precondition for other events. For example, if a change of requirements causes a delay, it transforms the activity to a different state. In this state, the event “reallocate resource” can occur. Alternatively, it is possible, if the task is in certain state, an event cannot occur.
3. Once events and event chains are defined, we can perform quantitative analysis using Monte Carlo simulation to determine uncertainties and quantify the cumulative impact of the events. Sometimes we can supplement information about uncertainties expressed as an event with distributions related to duration for start time, cost, and other parameters, as done in classic Monte Carlo simulations. However, in these cases it is important to discriminate between the factors that are contributing to the distribution and the results of events to avoid a double count of the same factors.
4. The event chains that have the most potential to affect the projects are the “critical chains of events.” By identifying critical chains of events, we can mitigate their negative effects. We can identify these critical chains of events by analyzing the correlations between main the project parameters, such as project duration or cost, and the event chains.
5. Probabilities and impact of the events are obtained from the historical data. Monitoring the activity's progress ensures we use updated information to perform the analysis. In many projects, it is hard to determine which historical data we should use as an analog for future analysis. For example in most cases, in research and development, new projects differ from the previous projects. We can accomplish the proper selection of analogs for the historical data by applying analysis using a Bayesian Belief Networks. In addition, during the course of the project, we can recalculate the probability and time of the events based on actual data.
6. Event Chain Diagrams are visualizations that show the relationships between events and tasks and how the events affect each other. By using Event Chain Diagrams to visualize events and event chains, we can simplify the modeling and analysis of risks and uncertainties.
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