Assessing Optimum Delivery Strategy for Procuring a Major Water Pipeline Project

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Presented by

Pedram Danesh-Mand

About this talk

Risks, uncertainties and probabilistic decision making in an increasingly volatile engineering & construction – A case study assessing optimum delivery strategy for procuring a major water pipeline project Post COVID-19, the Engineering & Construction sector is changing fundamentally, leading to significant risk exposure faced by both public and private organizations. Beyond the recent economic uncertainties, more frequent catastrophic events are expected in future, as well as disruptions including climate change, digital revolution, limited capability and capacity, supply chain constraints, social license to operate, stakeholder expectations and political risks. One of the best ways to embrace uncertainty in our approach is to learn to think probabilistically, like a professional gambler. Developing a probabilistic mindset allows us to be better prepared for uncertainties, complexities, and different scenarios for making better informed decisions. With a global investment of approximately US$60 trillion, including a potential pipeline of $800b projects in Australia over the three decades to 2050 net zero emission targets, across power, mining and metals, hydrogen, carbon capture and oil & gas projects, Pedram believes there are good opportunities for the industry to improve the way it selects, develops, and delivers major projects by better utilizing the risk engineering techniques such as probabilistic decision tree analysis. By sharing a recent case study of using a probabilistic decision tree analysis (by using Palisade Decision Tools software) for comparing and selecting the most optimum delivery strategy for procuring a major water pipeline project, Pedram will explain the fundamentals of decision-making process. According to Pedram, organizations that arm themselves with probabilistic thinking and capabilities will become more resilient and put themselves in a much better position to safeguard for uncertainties ahead.

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