Resources
@RISK is available with companion product PrecisionTree in the DecisionToolsSuite. PrecisionTree creates decision trees in Excel to allow you to map and understand the complex decision problems. @RISK functions are recognized by PrecisionTree, and the two may be launched from a common Excel toolbar. @RISK allows you to: quantify the uncertainty that exists in the values and probabilities […]
This article explains how 'Black Swan' events are changing perceptions of risk analysis. Click here to read the article.
@RISK’s use of Monte Carlo simulation allows for powerful features, like RiskSimtable. The RiskSimtable feature can be used to run multiple simulations to test the sensitivity of the risk analysis model, for example to changes in the parameters of a distribution. This model is of a business with a base case expected revenue of 100 and […]
This article 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.
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 […]
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 […]
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, […]
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.
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 […]
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 […]
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 […]
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 […]
