New to version 7.5, all graphs and reports in @RISK now feature a more streamlined interface, to accomplish more in fewer clicks. One of the many additions made to help save time: you can now toggle between your default probability, cumulative and tornado graphs without having to click from each respective drop-down menu.
The new style of these easy-access buttons will allow you to more easily toggle between your favorite graphs.
In addition, graphs and reports support Excel’s built-in themes and colors. Now you can apply your company’s standard Excel themes to your @RISK reports, improving communication, consistency and efficiency!
The same report with stylistic changes that happen with a click of the mouse.
»Learn about all the new functions in @RISK 7.5 and The DecisionTools Suite
@RISK has the ability to use multiple CPUs, but have you ever wanted more control to choose how many CPUs are used in @RISK to run simulations? Now you can decide exactly how many CPUs to utilize, giving you more control of @RISK simulations and your computer!
also now has the capability of using multiple CPUs, dramatically increasing the speed of calculating optimizations. Speed tests showed optimizations run 4x faster than before – or even faster – saving you tremendous amounts of time. So now there’s no excuse to take coffee breaks while waiting for your optimizations!
Functions are at the heart of risk analysis involving Monte Carlo simulation. In version 7.5 we have added a total of 22 new functions – 16 distribution functions and 6 new statistical functions. @RISK’s new distribution functions will appeal to a variety of industries and applications:
- RiskDagum – This distribution is mostly associated with modeling income distribution and is useful in many actuarial statistics.
- RiskFréchet – Used to quantify extreme events, Fréchet distributions is helpful in modeling rare, unexpected events such as radioactive emissions, seismic analysis, and peak single-day rainfall and flooding.
- RiskCauchy – This distribution is useful in scientific and engineering applications to model resonance behavior, measurement repeatability and light dispersion.
- RiskBurr12 – Burr distributions are used to model household income, insurance risk and reliability data.
- RiskFatigueLife – If you are looking to model material reliability, FatigueLife distribitions are effective in modeling reliability and are used to estimate failure of materials over time.
These new functions are important for accurate, insightful estimation of uncertainty and provide useful statistics on simulation results data. Ranging from insurance risk to reliability engineering to modeling of household income, @RISK 7.5 has your risk analysis needs covered!
»Learn about all the new functions in @RISK 7.5
»Learn more about @RISK
It’s often difficult to determine which factors require the most attention in business decisions which can lead to focusing on the wrong things while ignoring what’s most important. Tornado graphs are an effective way to determine which aspects have the most impact on a business decision, so you can focus on what matters rather than negligible factors. Palisade’s @RISK, which has been used for years by many companies who utilize tornado graphs for sensitivity analyses, was just given multiple improvements to its tornado graph functions that offer new analytical features and simpler use to increase the ease of understanding and communicating your results.
One of the new features of @RISK 7.5 is the ability to overlay multiple tornado graphs into one visual. Creating a separate chart for each simulation and then comparing the results can be both difficult to compare and time-consuming. With @RISK 7.5 you can simply overlay all of the simulations making comparison easier to see and communicate to others. Rather than focusing on communicating the comparison, you can jump right to making more informed decisions!
Another new exciting feature in @RISK 7.5 is the Contribution to Variance tornado graph function. This feature determines the amount of output variance is attributed to each factor so you can see which inputs are creating the most impact on the output. You can choose whether you want to see the magnitude and direction of the variance or just the magnitude to more easily compare the contribution variance.
Finally, we have added a shading option to the Change in Output Mean tornado graphs to quickly see whether the input associated with each bar is high or low when the output statistic increases or decreases. In the example below you can see that when inputs such as Product Lifetime and Initial Unit Price are high, there is a positive impact on the net present value (NPV) of the project; when an input such as Initial Cost is high it will have a negative impact on the NPV.