@RISK Modeling Tips
CH2M HILL Subsidiary Halcrow uses @RISK to Assess Infrastructure Flood Risks
Most recently, United Kingdom-based Halcrow has incorporated @RISK into its flood planning efforts. Severe flooding due to heavy rainfall in the UK continues to be a real threat. Halcrow looks at mitigating the consequences by determining the risk faced by critical assets such as water treatment works and pumping stations, and weighing these against the cost of appropriate preventive measures. The process is subject to a great deal of uncertainty, which Halcrow quantifies using @RISK. This ensures that informed decisions are made on the best action to be taken.
“Serious flooding having a major impact on water services is a very real possibility in some areas in the UK – and this has been demonstrated in recent years,” explains Alec Yeowell, asset management engineer at Halcrow. “We set out to understand what it was realistic to mitigate against and achieve in terms of the costs and benefits of improving the current levels of resilience. The nature of the task means that each stage of the calculation is subject to uncertainty. @RISK, which is flexible and robust, allows us to measure each eventuality and present it visually so that the benefits of each path are clear to everyone and an informed decision can be taken.”
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» Regional Planning Using @RISK, PrecisionTree, and Evolver -
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Journal of Financial Planning Explains
The Journal of Financial Planning recently published an article entitled “Achieving a Higher Safe Withdrawal Rate with the Target Percentage Adjustment” by David M. Zolt, a NAPFA-registered Financial Advisor and president of Westlake Advisors in Westlake, Ohio. According to Zolt: “The most important question posed by clients to their financial planner is: ‘How much can I withdraw from my retirement portfolio each year and still be sure, or at least very confident, that I will not outlive my money?’” For years, financial planners have used a widely-accepted “4 Percent Rule” to answer this question. According to the rule, a “well-allocated” portfolio subjected to an initial 4 percent withdrawal and adjusted for annual inflation would survive at least 30 years under nearly all scenarios.
In the article, Zolt argues for the use of a Target Percentage withdrawal rate that varies depending on the performance of the portfolio each year. The Target Percentage rate is made with a 95 percent confidence that the portfolio will remain solvent for at least 30 years. Estimates for the portfolio’s future performance based on varying withdrawal rates were made using @RISK. The test portfolio assumed 35 percent large-cap stocks, 15 percent small-cap stocks, and 50 percent intermediate-term bonds. @RISK Monte Carlo simulations of 50,000 iterations each were used with normal distributions on returns.
See similar financial case studies at the 2013 Palisade Risk Conference Las Vegas:
» Private Client and Institutional Investment Management
Over 40% of all infrastructure projects, and over 80% of major infrastructure projects, exceed their budget or schedule. In today’s climate of financial uncertainty, funding constraints, volatile commodity prices, and compliance changes, conventional planning methods are no longer sufficient.
HDR incorporates @RISK for a wide range of risk management decisions. Applications include: business case evaluations of alternatives; refinement of program budget and schedule; risk-based management of contingency; forecasting of escalation and risk-reducing contracting strategies; and evaluation of procurement and alternative delivery strategies. The types of risks HDR must analyze are similarly diverse. Project managers face budget, event, and scope risks. Material prices may rise, extreme weather may occur, or community pressures force a change in location.
For example, HDR can use @RISK to make the best possible bids on new projects. Using @RISK’s Monte Carlo simulation, they can estimate the likely project costs, estimate the chances of meeting various milestones, and identify and quantify potential mitigation strategies. Armed with this information, HDR can better balance potential profits against likely competitor bids.
Furthermore, HDR incorporates @RISK’s RISKOptimizer feature to develop a schedule of start times for projects that minimizes the overall impacts of disruptions. These disruptions could be delays in delivery of materials, labor shortfalls, budget problems, or other problems. RISKOptimizer can experiment with many different combinations of project start times while simultaneously accounting for the uncertainty of disruption with each scenario.
@RISK can be used effectively with PrecisionTree to represent uncertainty in decision tree models. There may be outcomes, for example, that in reality are continuous in nature and better represented by a distribution function rather than by an arbitrary number of discrete nodes. The same concept applies to chance events.
The purpose of this simple example is to illustrate the @RISK options in PrecisionTree. In general, you build the tree in the usual way, but then you enter @RISK distributions on selected branches.
However, the real question is what happens when you run the @RISK simulation. This depends on the PrecisionTree Model settings, specifically the settings under the @RISK tab. There are two categories of @RISK settings: one that performs calculations each iteration of the @RISK simulation, and one that forces decisions during the @RISK simulation. The example explores what happens using a variety of different settings for different situations.
Introduction to Risk Analysis using
Exploring Oil and Gas Applications of @RISK and the DecisionTools Suite: Examples from Petrobras and Others
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