ARC Ltd uses @RISK to Inform Catastrophe Insurance Risk Pool in Africa

In This Issue
What's New
» @RISK Models Financial
   Uncertainty of the
   Fehmarn Belt Fixed Link

» 2016 Risk Conference
   New Orleans

@RISK Modeling Tips
» Interpreting Change
   of Output Statistics
   in Tornado Graphs


In This Issue
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2016 Palisade Risk Conferences
An intensive networking and software training event presented by Palisade trainers and consultants, with real-world case studies presented by industry experts.
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The DecisionTools Suite

@RISK Models Financial Uncertainty of Fehmarn Belt Fixed Link
The Fehmarn Belt Fixed Link is a planned immersed tunnel that will cross the Fehmarn Strait to connect Denmark and Germany. This large infrastructure project is mainly user funded, with an official projected payback period of less than 40 years. However, based on the latest research from RD&I Consulting based in Denmark, it is highly unlikely that this projected payback deadline will be met.

Hans Schjær-Jacobsen, Director of RD&I Consulting and former Professor and Director at the Technical University of Denmark, used Palisade's @RISK software to create financial models for the Fixed Link to determine the likelihood of project success – or failure – in terms of the payback period.

Uncertainty in the model was calculated by probabilistic uncertainty representation and Monte Carlo simulation, as well as interval analysis. Schjær-Jacobsen leveraged @RISK software's ability to calculate a genuine uncertainty profile by Monte Carlo simulation in contrast to just partial sensitivity analyses, using uniform and also triangular distributions as they were easily derived from double estimates (i.e. best- and worst-case), and triple estimates (i.e. best-, base- and worst-case), respectively. The simulation generated the probability distribution of the payback period for the Fehmarn Belt Fixed Link over 60 years, providing a project uncertainty profile presented in terms of a traffic light metaphor: green corresponds to a payback period of less than 40 years, yellow corresponds to a payback period of 40-50 years, and red corresponds to a payback period of more than 50 years.

Uncertainty Profile

For the Danish government, the only acceptable outcome of the model for the projected payback period is in the green light zone: less than 40 years. However, based on the model created by Schjær-Jacobsen, the Fehmarn Belt Fixed Link is a high-risk business case and the likelihood of financial project failure in terms of the payback period taking longer than 40 years is equal to 92.5% and substantially larger than acknowledged by the project proponents and presented to the public. This is due to several realistic uncertainties based on readily available facts, including traffic volume and income, construction costs, services and EU subsidies.

"As nobody had done this kind of analysis on the project before, the results were a surprise," explained Schjær-Jacobsen. "However, now that these results have been presented to Fehmarn A/S and the Commission of Transport, we will see how they impact future development of the Fehmarn Belt Fixed Link."

» Read the detailed case study
» Learn more about @RISK

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Industry experts present a selection of real-world case studies about innovative approaches to managing risk and uncertainty in a wide range of business applications.

2016 New Orleans Palisade Risk Confernece

Here’s just a sample:

  • Ontario Power Generation – Megaproject management
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  • Duke Energy – Utilities load/demand forecasting
  • APQ/Novel Pharma – Strategic forecasting
  • Federal Home Loan Bank of Indianapolis – Portfolio risk management
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Product Spotlight
Interpreting Change in Output Statistics in Tornado Graphs
Let's talk first about the default behavior for Change in Output Mean, which is the default statistic. We'll suppose that you have 2500 iterations in your simulation. The baseline is the overall simulated mean of that output.

The double-sided tornado has one bar for each selected input, and each bar has numbers at its edges. Each bar is prepared by considering one input and ignoring everything else but the output. The key here is that the inputs are first sorted in ascending order and binned in that order, then an output mean is computed for just the iterations in each bin. Click on the link below to discover more details on how the numbers on each bar are computed for Change in Output Mean.

Different shading beginning with @RISK 7.5 shows you which end of each bar represents high input values and which represents low input values. Thus you can easily tell which inputs have positive impact on this output (high inputs at the right) and which have negative impact (high inputs at the left).

» Learn More


On-Demand Webinars: Watch at your convenience -- any time, any place, and free.

Introduction to Risk Analysis using @RISK
Presenter: Michelle Jackson, Palisade
04 October - 10am EDT / 4pm CEST / 3pm BST » Register Now
21 October - 11am AEDT Sydney / 8am SGT » Register Now

Risk Register Focus: Introduction to Risk Analysis using @RISK
Presenter: Doug Oldfield, Palisade
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Introduction to Risk & Decision Analysis using @RISK & The DecisionTools Suite
Presenter: Will Van der Merwe, Palisade
10 November - 10am GMT / 11am CET / 9pm AEDT » Register Now

Presenter: Sascha Stange, Palisade
08 December - 10am GMT / 11am CET / 9pm AEDT » Register Now

Introduction to BigPicture
Presenter: Robin Holloway, Palisade
26 October - 10am BST / 11am CEST / 1pm GST » Register Now
16 November - 10am EST / 3pm GMT / 4pm CET » Register Now

» See complete Webinar schedule

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