(RISK-1662) Using Stochastic Optimization to Improve Risk Mitigation
Primary Author: Mr Eric Druker Booz Allen Hamilton
Co-author(s): Mr Graham Gilmer Booz Allen Hamilton; Dr David T. Hulett Hulett & Associates, LLC
Audience Focus: Advanced
Application Type: Research
Time/Location: THU 3:15-4:15/Room 1
Abstract: Today’s risk analysts have several tools to help them identify and mitigate future sources of cost and schedule risk. Traditionally, the risk cube method has been used to provide probability-weighted metric for each risk’s severity by multiplying its likelihood and consequence factors together. Unfortunately, this methodology ignores secondary and tertiary impacts of risks, in particular when they could drive cost by creating a new critical path within the project plan. Integrated cost & schedule risk analysis provides greater insights by integrating the risks into the schedule. Using traditional sensitivity metrics such as Pearson’s correlation, analysts are able to identify risks contributing to cost and schedule growth. While stronger than the risk cube methodology, analysts using this method are unable to measure a risk’s contribution to a particular confidence level of either cost or schedule and cannot uncover when the impact of removing a set of risks may be greater than sum of the impacts of removing them individually. This paper will show how stochastic optimization – the optimization of simulation models – can be used to better identify risks, and combination of risks, that when mitigated will best reduce project cost and schedule risk.
(RISK-1763) Planning and Estimating Risky Projects: Oil and Gas Exploration
Primary Author: Mr Colin H. Cropley Risk Integration Management Pty Ltd
Co-author(s): Mr Matthew D. Dodds Risk Integration Management Pty Ltd; Mr Grant Christie Talisman Australasia Pty Ltd
Audience Focus: Intermediate
Application Type: Application
Time/Location: WED 10:15-11:15/Room 2
Abstract: Deterministic methods of planning and estimating projects tend to be inherently optimistic, for reasons set out in this paper.
After setting out the challenges of realistic planning of oil & gas exploration in PNG, the paper explores the reasons for inherent optimism in project planning. Use of probabilistic planning and estimating based on an Integrated Cost & Schedule Risk Analysis (IRA) version of the Monte Carlo Method (MCM) is shown to enable the user to take account of these reasons to produce more realistic forecasts of time and cost outcomes. IRA is contrasted with conventional separate cost and schedule risk analyses to highlight the benefits of the integrated approach.
The application of the IRA approach to produce realistic planning of exploration for oil and gas in Papua New Guinea (PNG) is then described.
While PNG represents extremes of time and cost uncertainty that demonstrate the value of probabilistic planning and estimating, the principles and approach are just as applicable to all kinds of projects where time and cost uncertainty are less extreme but still significant.