Abstracts

  Intro to Palisade and the World of Probabilistic Analysis

We will open the conference with a brief overview of Palisade, and then address the question you’ve undoubtedly been faced with: “What is risk analysis, and why do I need it?” After all, risk management appears to have failed in many examples, and even now companies are trying to figure out how to grapple with risk and unlock the power of probabilistic analysis. How do we avoid missteps, and how do we make the most of the tools we have for managing the future?

  Building a Project Cost Estimate with @RISK

In this session attendees will learn how to put together a simple cost model using @RISK, learning about a number of important features along the way.

Take outs will include:

• Understanding how to get the same results each time a simulation is run.
• How to use Excel cell references in a model, and why it is best practice to do so.
• Generating a histogram/ S-Curve and customising it for reports.
• Interpreting tornado graphs to understand key drivers in models.
• Using scatter plots as a sanity check on a model.
• Using confidence levels (percentiles) to quantify the likelihood of an outcome.
• Automatically generating meaningful information using the @RISK statistics functions.
• Calculating how much contingency needs to be added to a base estimate.

  Making Risk Registers with @RISK

Quantitative risk registers are something which can be added to any model, but are fundamentally about modelling likelihood (or frequency) and severity, and then combining them. This session will give attendees the opportunity to build one themselves from scratch.

Take outs will include:

• Understanding why the average is a risky way to model risk
• How to choose a distribution to model event risk
• Why building a conditional risk register is the only theoretically sound way of modeling event risk
• How to combine likelihood and severity using RiskMakeInput and RiskCompound

  Choosing a Distribution That Fits

This session will look at the most popular distributions in @RISK. A variety of techniques for choosing the right distribution for your needs will be discussed and compared, including pragmatic, theoretical and data-driven methods. We will discuss the use of different distributions; discrete, continuous and a combination of both, to address a wide range of risk modelling situations.

  Financial Modelling with @RISK

Many organizational decisions involve making calculated risk / return judgements, evaluating and managing risk must therefore be an integral part of business activities. This session is an introduction to the fundamentals of @RISK using a probabilistic discounted cash flow -NPV model as the basis. This includes setting out the model logic, inserting input distributions and output functions to the spreadsheet, running a simulation, and then generating various graphs and reports. We will discuss the interpretation of these Monte Carlo simulation results, and describe the more advanced features such as assessing different scenarios with special functions.

  Sensitivity Analysis and Optimisation: TopRank, PrecisionTree, and RISKOptimizer/Evolver

This session will look at some of the companion tools that are available alongside @RISK within the DecisionTools Suite. First of all we'll take a look at TopRank, a very quick, yet powerful, tool which allows you to find key drivers in any model. It's also a very popular tool for auditing models to make sure they're behaving as expected. This will be followed by a look at PrecisionTree, a tool which allows you to quickly build up decision trees to guide strategy choices in the face of different options. Finally, the session will give an overview of RISKOptimizer, the software which utilises the power of @RISK to optimise under uncertain conditions - stochastic optimisation.