Open Collections

Dijkerman-Mounji, Adam

University of British Columbia

Risk management in large infrastructure projects is an integral methodology for delivering project goals. While the identification and quantification of risks are common practices contributing to risk management approaches, the prevention and mitigation of these risks often lay in the hands of the organization’s ability to define action items. These action items mitigate the risk by reducing or eliminating the consequences and/or the probability of occurrence of these uncertain events. This process however is often not clear, and mitigation strategies are often not implemented successfully. We put forward questions regarding effectiveness of risk management tools to measure the success of these mitigation strategies. If an organization can establish methods to measure the effectiveness of the mitigation actions applied to their projects, they could use this as a tool to adjust and adapt to future projects based on collected data and lessons learned. Presently, it is uncommon to collect specific data about applied mitigation strategies. When faced with a new project, an organization relies on experiences provided by Subject Matter Experts and available information documented in lessons learned. Risk management tools do not currently propose methods to collect metrics on mitigation strategies and their effectiveness applied to large infrastructure projects. This thesis investigates pragmatic methods which could be applied to existing risk management processes to implement tracking of mitigation strategies and measuring their perceived effectiveness. We established identification and categorization of processes similar to those set out in identifying risks, implementing a systematic way to categorize actions in their intended application. This provides a foundation for recording data about these applied mitigation actions, categorizing actions into mitigation strategies. A live library of actions can be populated, updating it as the success/failure of mitigation actions are realized. This library is a tool to collect mitigation strategy metrics based on the real time evolution of project information and/or data from past projects. We can then assign probability distributions to the metrics of mitigation components. In using Bayesian updating of beliefs in probability distribution form, the belief of a given mitigation strategy and corresponding action can be updated to effectively mitigate the given risk.

Text

2022-12-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83454

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/