New in @RISK 6.1 (Apr 29, 2014)
- New, faster calculation engine for the simulation of Microsoft Project schedules. Many Project simulations now run 5-15 times faster than before!
- Simplified Ribbon and toolbar:
- The @RISK toolbar ribbon has been better organized to allow quicker access to common tasks, and to make it easier to find different analyses.
- New ribbon and toolbar:
- Mini “pop-up” toolbar:
- As is offered in Excel, @RISK now provides a small toolbar for quick access to graphs and functions that pops up where you are working in your model, saving you time dragging the mouse. To activate the mini toolbar, simply click and hold the left mouse button.
- New ribbon and toolbar:
- The @RISK mini toolbar provides quick access to common tasks.
- Detailed statistics and data with graphs:
- @RISK now provides detailed statistics and simulation data in the same window as simulation results graphs. This simplifies your analysis by eliminating the need to open multiple windows. You also have the option to hide this information and view only summary statistics as before.
- New graph options:
- @RISK enables you to see detailed simulation statistics and data in the same window as your graph.
- Double-sided Tornado with Change in Output Values:
- This new “double-sided” tornado graph shows an input’s positive and negative impact on actual output values – information very valuable to managers, and much easier to understand than statistical coefficients. It uses “input scenarios” to calculate the impact of each input on a specific output statistic, such as the mean, a percentile, or others.
- New graph options:
- @RISK’s double-sided tornado is much easier to understand, making it ideal for managerial reporting.
- Scatter plots have also been integrated into this “double-sided” tornado to understand and highlight different input scenarios.
- By dragging a bar from a tornado graph, you can see a scatter plot of the impact of a given input on your output, and understand different scenarios.
- Spider graphs:
- New spider graphs display the change in a given output’s mean (or whatever statistic you specify) across a range of values for all the various inputs. It’s a very intuitive new view on sensitivity analysis – great for reports and presentations.
- New graph options:
- Spider graphs in @RISK show intuitively how an output changes as a given input changes.
- Integration with Microsoft Project for project risk modeling:
- @RISK is now a truly cross-platform tool, enabling risk modeling of your Microsoft Project schedules using the same @RISK you use for modeling in Excel! You can now do your project risk modeling in Excel rather than Microsoft Project, providing a new world of flexibility. A new interface layer reproduces your schedule in Excel, enabling you to use all Excel formulas and @RISK functions. When you make changes to your model in either Project or Excel, those changes are reflected in the other with @RISK’s Sync feature. (Note that all @RISK modeling takes place in Excel, so @RISK functions do not appear in Microsoft Project.) Then simulate your Project schedules in Project itself, using Project's scheduling and calculation engine.
- The benefits of using Excel for your Project risk modeling are many. You can easily build risk registers in Excel for your Project model using new “RiskProject” functions. You can integrate your cost and schedule analyses. You can standardize on a single tool - @RISK – to meet the needs of your project managers, cost estimators, finance analysts – everyone who deals with risk in your company. Plus, a single interface means a shorter learning curve for everybody.
- @RISK integrated with Microsoft Project:
- You can perform risk modeling on your Microsoft Project schedules directly in Excel. Here, we are defining a distribution to reflect the uncertain duration of a task.
- @RISK can show you the likelihood of your project being completed on time or by a specific date.
- Better distribution fitting with BestFit®:
- @RISK now offers integrated bootstrapping to estimate confidence intervals for fitted parameters. This automated process greatly saves you time and gives you more confidence in your fits.
- New goodness-of-fit measures as well. You can also hold certain parameters fixed during fits, and fit data sizes up to 10 million values (increased from 100,000).
- @RISK now offers batch fitting for fitting batches of data sets. There’s even a correlation matrix feature built in to batch fitting.
- the live fit function, RiskFitDistribution, along with supporting functions, returns statistics on fit results in real time as new fits are run.
- Better distribution fitting:
- Fitting reports show which distributions were chosen and why, and include bootstrapping estimates of fitted parameters.
- New tests, bootstrapping, and batch fitting are among the enhancements to @RISK’s distribution fitting.
- Time-series modeling:
- @RISK offers a new set of functions for simulating time series processes, or values that change over time. Any future projection of time series values has inherent uncertainty, and @RISK now lets you account for that uncertainty by looking at the whole range of possible time series projections in your model. This is particularly useful in financial modeling and portfolio simulation.
- There are functions available for 17 different statistical time series models, including ARMA, GBM, GARCH, and others. These functions are entered as array functions in Excel.
- @RISK provides new windows for fitting historical time series data to these new functions. The results can be animated to show the behavior of your time series during simulation. All this is integrated into the existing @RISK interface.
- Time-series modeling:
- @RISK has fitted the Moving Average 1 (MA 1) stochastic time series process to this variable, which is the stock price of Apple Computer.
- New distribution functions:
- New distribution functions have been added to the more than 40 already in @RISK: DoubleTriang, Levy, Laplace, F, Extreme Value Min, and Bernoulli.
- Converter for Crystal Ball models:
- An automatic converter has been added to @RISK that lets you open and run risk models created in Crystal Ball. @RISK converts Crystal Ball distributions and other model elements to native @RISK functions, enabling you to use all old and current Crystal Ball models.