In This Issue
What's New
» Murray & Roberts Uses
   @RISK for a Cash Flow
   Based Approach to ERM

Case Study
» NeuralTools Assists Airline
   to Determine the Most
   Profitable Price Points

@RISK Modeling Tips
» Projecting Interest Rates

Upcoming Webcasts

In This Issue
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Risk analysis software using Monte Carlo simulation for Excel and Project

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The DecisionTools Suite

Murray & Roberts Uses @RISK for a Cash Flow Based Approach to ERM

Murray & Roberts is a leading engineering and construction services company based in South Africa, with projects including the Cape Town Stadium, the Burj Al Arab hotel in Dubai, the World Trade Centre in Bahrain, and the Diavik Diamond Mine in the Arctic Circle.

Engineering and construction projects face various high risk scenarios, including a combination of negative markets, protracted strike action with labor unions unable to reach settlements amicably, loss of life due to poor safety standards, and delays and cost-overruns on major projects. If risks are not understood and managed, stakeholders could face significant losses.

Most organisations in the industry undertake some form of enterprise risk management (ERM) based on best practices such as the ISO31000 framework. However, many still take a qualitative approach, possibly due to a lack of the required level of technical knowledge, the belief that intuition provides adequate insight, and a tendency to avoid complex mathematical issues.

Murray & Roberts is evaluating a quantitative approach to ERM that links risk exposures to company value by way of cash flows.

Murray & Roberts uses enterprise discounted cash flow (DCF) and discounted economic profit (EVA) as the valuation technique for company value. This measurement approach was selected as it relies exclusively on the flow of cash in and out of the organisation -- the method preferred by investment houses and analysts -- rather than on accounting-based contributions.

Risk problems are simulated using @RISK, which allows the simulations to be adjusted to account for interdependencies and correlations between risks.

Sean de la Rosa, project and enterprise risk manager of the Power & Water Platform at Murray & Roberts, says: “@RISK offers the powerful simulation capability that is required to model complex risk problems. Despite this, the simulations are relatively easy to perform and require minimal in-depth mathematical expertise.”

» See a detailed look at the simulation in the case study

Case Study
NeuralTools Assists Airline to Determine the Most Profitable Price Points
A risk analysis expert was tasked with developing a model for predicting demand and price elasticity for a domestic airline catering to the tourism industry in Costa Rica. This predicting model uses neural networks technology powered by Palisade’s NeuralTools application. NeuralTools is a sophisticated data mining application that uses neural networks in Microsoft Excel. New predictions are based on the patterns of the known data. It imitates brain functions in order to “learn” the structure of your data. Once the trained network understands the data, it can take new inputs and make intelligent predictions.

The model was able to determine the mean fare to maximize revenue under the specific conditions of the flight’s route and date. NeuralTools was also helpful in sensitizing passenger predictions under certain marketing strategies around dates and routes. NeuralTools helped the airline by utilizing data it already had to determine the most profitable price point and adjust it based on the numerous factors that impact air travel.

» Read the complete case study

Product Spotlight
Projecting Interest Rates
This simple model illustrates two ways variable interest rates on a loan might be simulated. In the first model, the yearly interest rates are generated independently of one another. Each is normally distributed with mean 10% and standard deviation 1%. In the second model, a random walk model, the first interest rate is normally distributed with mean 10% and standard deviation 1%, but each succeeding interest rate is normally distributed with mean equal to the actual previous rate and standard deviation 1%. (Note that unlike many typical loan payment schedules, where all payments are equal, the schedules illustrated here pay a constant principal each year, but have unequal yearly payments.)

This example will demonstrate how the way interest rates are generated makes a big difference in the distribution of the total payment. Specifically, interest rates can vary much more in the random walk model, and this drives the larger variation in the total payment.

» Download the example model
» More about @RISK


What's New in DecisionTools Suite version 7
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Introduction to BigPicture
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Cost Estimation and Risk Registers using @RISK
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Introduction to Project Risk Management using @RISK
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Introduction to Risk and Decision Analysis using
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Presented by: Palisade Corporation
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» See complete Webcast schedule


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