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UBC Theses and Dissertations

Adaptive risk management in mining project development : a flexible approach to improve risk response Rice, Craig Matthew


Capital project development is the mechanism through which mining companies turn promising orebodies into profitable mines. However, broad-scale project success is elusive, with projects frequently exceeding approved cost and schedule estimates. Researchers and mining professionals have pointed to project risk management as a way to improve project success. Despite mature project risk management practices and widespread adoption, projects still repeatedly fail. This is due partly to the current risk management paradigm of prediction and advanced planning, with risks frequently materializing differently than predicted. One method of addressing uncertainty underlying project risks is by embracing flexibility and using an adaptive management approach to risk response. This dissertation demonstrates how adaptive project risk management can preserve project value and reduce time to resolve risks. This dissertation first establishes the state of project risk management practices in mining through an exploratory survey of industry practitioners. The survey fills a literature gap by documenting the methods currently used and attitudes towards them. Next, the adaptive project risk management framework is proposed, describing a structured, iterative process to pursue multiple competing response alternatives in parallel. The framework also details how experimentation, observation, and learning support the adaptive process. A continuous discounted cash flow model and stochastic simulation address uncertain elements of both the risk and the risk response alternatives. The system model provides insight into the effects of different risk responses and the value gained through the adaptive approach. The adaptive framework and system model are explored through two case studies. The first case involved multiple adaptive iterations and demonstrates how the adaptive process gathers and models new information using Bayesian inference while pursuing and dynamically tracking multiple risk response alternatives. The second case study shows how experiments and pilot tests can be used to learn more about the risk and viability of risk responses. These cases demonstrate that an adaptive approach can increase project value, reduce the duration of resolving risks, and potentially limit downside risk compared to a non-adaptive response. The methods proposed in this dissertation give decision-makers the tools to manage project risks more effectively and improve risk management outcomes.

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