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Why Risk Managers Need a Higher Profile

Why Risk Managers Need a Higher Profile

Palisade Europe Managing Director Craig Ferri explains why now is the perfect opportunity for risk managers to play a more important role in companies than ever before. Click here to read the article.
Running multiple risk analysis simulations in @RISK to see how changes in model variables affect simulation results

Running multiple risk analysis simulations in @RISK to see how changes in model variables affect simulation results

Example Model: SENSIM.XLS Sensitivity analysis in @RISK (risk analysis software using Monte Carlo simulation) lets you see the impact of uncertain risk analysis model parameters on your results. But what if some of the uncertain model parameters are under your control? In this case the value a variable will take is not random, but can be ...
The Rise of the NOMFET

The Rise of the NOMFET

By now we’ve become accustomed to the marvels of neural network technology and, in fact, inured to the advances it brought in statistical analysis with its computational simulations of nerve cells.  Its many everyday applications–especially in online retailing–seem kind of ho-hum, and we’d be put out if for some reason they weren’t in use. ...
Batch Fitting in @RISK Risk Analysis Software

Batch Fitting in @RISK Risk Analysis Software

@RISK allows you to use historical data to fit data to a probability distribution. The process is very simple: first select the range where the data is located, and then select the Distribution Fitting button. @RISK will guide through the fitting process where you can select a variety of statistical tests such as Chi-Square, Anderson-Darling, ...
The DNA of Cement

The DNA of Cement

A team of MIT scientists calling themselves Liquid Stone made a breakthrough (as it were) discovery about cement.  The Romans used cement to build their remarkable aqueducts, and the stuff is still in use.  In fact it’s one the most widely used building materials on the planet.  It has a chemical name, calcium-silica-hydrate.  But until ...
Analysis Placebos: The Difference Between Perceived and Real Benefits of Risk Analysis and Decision Models

Analysis Placebos: The Difference Between Perceived and Real Benefits of Risk Analysis and Decision Models

The authors examine decision analysis methods that merely make people feel better about their decisions with those that produce measurable improvements over time. They find that Monte Carlo simulation is one of the most effective methods for decision and risk analysis. Click here to read the article.
Modeling the Compound Effect of Concurrent Occurrences of Risk Events with @RISK

Modeling the Compound Effect of Concurrent Occurrences of Risk Events with @RISK

When modeling risk events, it is common that several events could affect the same cost element of a project. During the simulation, two or more risk events can occur at the same time. The question becomes how to calculate the total impact. This type of modeling technique is very common and often needed in project risk analysis, contingency ...
Using Named Ranges in Excel: Some Comments

Using Named Ranges in Excel: Some Comments

An earlier blog on Best Practice Principles in Excel Modelling generated quite some interest, as well as demand for more details on some of the points made, especially those concerning the use of named ranges risk asssessment models in Microsoft Excel. In the earlier posting, I had simply stated that (in my opinion): “Named ranges should be ...
Best Practices in Risk Modelling

Best Practices in Risk Modelling

The recent blog positing on best practices in Excel modelling could be thought of as providing a reasonable and robust set of principles for building static Excel models. When building simulation models for risk analysis in Excel (for instance, with @RISK Monte Carlo software), some other points are worthy of consideration: A risk model ...
Some Best Practice Principles in Excel Modelling

Some Best Practice Principles in Excel Modelling

This blog briefly posts some fairly standard (but not fully accepted, and more often simply not implemented!) “best practice principles” in Excel modelling. A later blog discusses a related topic as to whether risk modelling (when building Monte Carlo simulation models using @RISK in Microsoft Excel) requires the same (or a modified) set of ...
Correlation and Directionality

Correlation and Directionality

This is the second in a series of postings about correlation modelling.  In the first posting we discussed the idea of correlation as representing a proxy model of dependency between random variables. In this posting, we discuss the idea the often overlooked concept that relationships of correlation do not necessarily imply any ...
Getting the Full Picture – Combining Monte Carlo Simulation with Decision Tree Analysis Part II

Getting the Full Picture – Combining Monte Carlo Simulation with Decision Tree Analysis Part II

In Part I combining simulation and decision tree techniques was introduced. But what does that actually give you? What meaningful results are created to justify the work? Obviously there are good things to come, or I wouldn’t be bringing it up! A regular spreadsheet model can produce a distribution of outcomes like this: But as we ...

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