Event chain methodology is a schedule network analysis technique that is focused on identifying and managing events and relationship between them (event chains) that affect project schedules. It is the next advance beyond critical path method and critical chain project management. Event Chain Methodology is an extension of traditional Monte Carlo Simulations and project risk analysis with Risk Driver. Event chain methodology helps to mitigate the effect of motivational and cognitive biases in estimating and scheduling. It improves accuracy of risk assessment and helps to generate more realistic risk adjusted project schedules.
In the initial stages of a project, complex processes and the many risks involved make it impossible to accurately model. A model of a project is necessary for efficient project management. Event Chain Methodology, an improbable modelling and schedule network analysis technique, is a solution to this problem. This technique is used to manage events and event chains that influence project schedules. It is neither a simulation nor a risky analysis method but rather works using existing methodologies such as Monte Carlo Analysis and Bayesian Believe Network.
6 Principles of Event Chain Methodology
Event Chain Methodology is a more of a statistical method. It is based on six main principles
Moment of Risk and State of Activity – In a real life project process, a task or an activity is not always a continuous procedure. Neither is it a uniform one. A factor that influences tasks is external events, which in turn transform tasks or activities from one position to another.
During the course of a project, the time or moment when an event occurs is a very important component of the event. This time or moment is predominantly probabilistic and can be characterized using statistical distribution. More often than not, these external events have a negative impact on the project.
Event Chains – An external event can lead to another event and so forth. This creates event chains. Event chains have a significant impact of the course of a project.
For example, any changed requirements to the materials needed for the project can cause the activity to be delayed. The project manager then allocates resources from another activity. This leads to missed deadlines and eventually leads to the failure of the project.
Monte Carlo Simulations – On the clear definition of events and event chains, Monte Carlo Analysis is utilized in order to quantify the collective consequences of the events.
The probability of the risks occurring and the effects they may have are used as input data for the Monte Carlo Analysis. This analysis gives a probability curve of the project schedule.
Critical Event Chains – Critical events or critical chains of events are those with the potential to impinge on a project the most. By identifying such events at the very beginning, it is possible to lessen the negative effect they have on projects.
These types of events can be detected by examining the connections between the primary project parameters.
Performance Tracking With Event Chains – It is important for a manager to track the progress of an activity live. This ensures that updated information is used for the Monte Carlo Analysis.
Hence during the duration of the project, the probability of events can be calculated more accurately using actual data.
Event Chain Diagrams – Event Chain Diagrams depict the relationships between external events and tasks and how the two affect each other. These chains are represented by arrows that are associated with a particular activity or time interval on a Gantt chart.
Each event and event chain is represented by a different colour. Global events affect all the tasks in a project while local events affect just one task or activity in a project. Event Chain Diagrams allow for the simple modelling and analysis of risks.
Event chain methodology is an uncertainty modelling and schedule network analysis technique that is focused on identifying and managing events and event chains that affect project schedules. It helps to:
- Mitigate effects of motivational and cognitive biases in estimating and scheduling. In many cases, project managers intentionally or unintentionally create project schedules that are impossible to implement.
- Simplify the process of defining risks and uncertainties in project schedules, particularly improve the ability to provide reality checks and visualize multiple events.
- Perform more accurate quantitative analysis while taking to an account such factors as relationship between different events and actual moment of the events.