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Event Chain Diagrams

 

FEATURED PAPER

By Lev Virine, Michael Trumper, Eugenia Virine

Ontario, Canada

 



Relationships between project risks can be very complex. Risks can be assigned to different activities and resources, have different probabilities and impacts, and have correlations or act as triggers with each other. Due to this complexity, we recommend visualizing project events and event chains using event chain diagrams. Event chain diagrams use the familiar structure of a Gantt chart to visualize the relationships between project risks. State tables are also a useful tool and can be used to define the state of an activity. This paper provides a specification of Event chain diagrams and State Tables along with advice on how to use them effectively.

Event Chain Methodology

Event chain methodology is an extension of “traditional” and event-based quantitative risk analysis. Event chain methodology is an uncertainty modeling and schedule network analysis technique that is focused on identifying and managing events and event chains that affect project schedules. It is a logical formula to model and analyze a wide variety of different problems related to managing uncertainties in project schedule (Virine and Trumper 2007, Virine and Trumper 2013).

According to Event chain methodology activities in project schedule are affected by external events that transform them from one state to another (Virine 2013). The notion of state means that activity will be performed differently as a response to the event. This process of changing the state of an activity is called excitation. For example, an activity may require different resources, take a longer time, or must be performed under different conditions. As a result, this may alter the activity’s cost and duration. The original or planned state of the activity is called a ground state. Other states, associated with different events are called excited states. For example, in the middle of an activity requirements change. As a result, a planned activity must be restarted. Similarly to quantum mechanics, if a significant event affects the activities, it will dramatically affect the property of the activity; for example, cancelling the activity (Agarwal and Virine 2017).

Each state of activity in particular may subscribe to certain events. It means that an event can affect the activity only if the activity is subscribed to this event. For example, an assembly activity has started outdoors. The ground state the activity is subscribed to the external event “Bad weather”. If “Bad weather” actually occurs, the assembly should move indoors. This constitutes an excited state of the activity. This new excited state (indoor assembling) will not be subscribed to the “Bad weather”: if this event occurs it will not affect the activity.

Some events can cause other events. These series of events form event chains, which may significantly affect the course of the project by creating a ripple effect through the project. Here is an example of an event chain ripple effect:

  1. Requirement changes cause a delay of an activity.
  2. To accelerate the activity, the project manager diverts resources from another activity.
  3. Diversion of resources causes deadlines to be missed on the other activity
  4. Cumulatively, this reaction leads to the failure of the whole project.

Events can also cause execution of activities and group of activities. Risk response efforts are considered to be events, which are executed if an activity is in an excited state. Risk response events may attempt to transform activity from excited state to the ground state.

Analysis of project schedules with event and event chain are performed using Monte Carlo simulation. The result of analysis is a risk adjusted project schedule. The event and event chains can be ranked as a result of analysis. Events and event chains, which affect the project the most are called critical events or event chains.

Information about events and event chains, particularly probabilities and impacts of risks should be monitored and updated as part of project control.

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To read entire paper, click here

 



About the Authors


Lev Virine, PhD

Intaver Institute
Alberta, Canada

 


Lev D. Virine
, Ph.D. has more than 25 years of experience as a structural engineer, software developer, and project manager. He has been involved in major projects performed by Fortune 500 companies and government agencies to establish effective decision analysis and risk management processes as well as to conduct risk analyses of complex projects. Lev’s current research interests include the application of decision analysis and risk management to project management. He writes and speaks around the world on the decision analysis process, the psychology of judgment and decision-making and risk management. Lev can be contacted at [email protected]

 


Michael Trumper

Intaver Institute
Alberta, Canada

 


Michael Trumper
has over 20 years’ experience in communications, software design, and project risk and management. Michael is a partner at Intaver Institute Inc., a vendor of project risk management and analysis software. Michael has authored papers on quantitative methods in project estimation and risk analysis. He is a co-author of two books on project risk management and decision analysis. He has developed and delivered project risk analysis and management solutions to clients that include NASA, DOE, and Lockheed Martin.

 


Eugenia Virine, PMP

Alberta, Canada

 


Eugenia Virine
, PMP, is a senior manager for revenue development at Greyhound Canada. Over the past 12 years Eugenia has managed many complex projects in the areas of transportation and information technology. Her current research interests include project risk and decision analysis, project performance management, and project metrics. Eugenia holds B. Comm. degree from University of Calgary.