Resource Library

Jun. 8, 2010
Running Multiple Risk Analysis Simulations in @RISK with Sensitivity Simulation

Example Model of Sensitivity Simulation: 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 […]

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Feb. 5, 2010
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. Wasn’t […]

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Sep. 17, 2009
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 recent, […]

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Sep. 1, 2009
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.

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Sep. 1, 2009
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 […]

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Jun. 9, 2009
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 assessment models in Microsoft Excel. In the earlier posting, I had simply stated that (in my opinion): “Named ranges should […]

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Apr. 1, 2009
Best Practices in Risk Modelling

The 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 may need to be built […]

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Mar. 18, 2009
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 principles. The […]

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Jan. 15, 2009
Getting the Full Picture – Combining Monte Carlo Simulation with Decision Tree Analysis Part II

In Part I, combining simulation and decision tree analysis 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 […]

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Dec. 29, 2008
Monte Carlo Simulation Provides Advantages in Six Sigma

First of all, what is Monte Carlo simulation? Monte Carlo simulation is a computerized mathematical technique that allows people to account for variability in their process to enhance quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, […]

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Oct. 24, 2008
Tornado Graphs: Basic Interpretation

When using @RISK (risk analysis software for conducting Monte Carlo simulations in Microsoft Excel), one of the output graphs is a tornado graph. Such graphs have their most direct interpretation for linear models with independent input distributions, such as in most typical cost budgeting models. In these cases, the regression coefficients provide a measure of […]

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Jun. 30, 2008
Palisade - Quantifying Risk

@RISK is used to assess the relative benefits of different drilling strategies for Lundin, a small exploration company, for field development. Click here to read the article.

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