Wouldn’t you like to know the chances of making money – or taking a loss — on your next venture? Or the likelihood that your project will finish on time and within budget? How about the probabilities of finding oil or gas, and in what amounts?
Everyone would like answers to these types of questions. Armed with that kind of information, you could take a lot of guesswork out of big decisions and plan strategies with confidence. With @RISK, you can answer these questions and more – right in your Excel spreadsheet.
@RISK (pronounced “at risk”) performs risk analysis using Monte Carlo simulation to show you many possible outcomes in your spreadsheet model—and tells you how likely they are to occur. It mathematically and objectively computes and tracks many different possible future scenarios, then tells you the probabilities and risks associated with each different one. This means you can judge which risks to take and which ones to avoid, allowing for the best decision making under uncertainty.
@RISK also helps you plan the best risk management strategies through the integration of RISKOptimizer, which combines Monte Carlo simulation with the latest solving technology to optimize any spreadsheet with uncertain values. Using genetic algorithms or OptQuest, along with @RISK functions, RISKOptimizer can determine the best allocation of resources, the optimal asset allocation, the most efficient schedule, and much more.
New in Version 6 – Improved VBA Automation, Greater Language Support, and More
@RISK version 6 offers improvements to the XDK feature – the built-in Excel Developer’s Kit that enables you to automate and customize @RISK for Excel using Excel’s VBA programming language. The @RISK XDK now includes new functionality and greatly improved documentation to help you get started quickly.
Version 6 also includes faster simulation setup time for Project models, and has been translated into Russian. This is in addition to Spanish, Portuguese, French, German, Japanese, and Chinese. Users can specify the language at install, and even change languages later -- all from the same installer. This is a major benefit to multinational companies seeking to roll out a single risk analysis standard around the world.
Integration with Microsoft Project
With the release of @RISK 6, Microsoft Project schedules were integrated in the @RISK for Excel platform, enabling you to perform risk analysis and Monte Carlo simulation on your schedules using the more flexible @RISK for Excel interface. @RISK now imports your Project schedules into Excel so that you can use all of Excel’s formulas, and @RISK’s features, on your Project models. Excel becomes a front-end for your Microsoft Project schedule, linking directly to the underlying .MPP(X) file. Changes made in either Project or Excel are reflected in the other. When it’s time to run your Monte Carlo simulation, Microsoft Project’s scheduling engine is used for the calculations, ensuring accuracy.
Time Series Simulation
Version 6 of @RISK also introduced 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.
@RISK is used to analyze risk and uncertainty in a wide variety of industries. From the financial to the scientific, anyone who faces uncertainty in their quantitative analyses can benefit from @RISK.
FINANCE / SECURITIES
» Case Studies
Real options analysis
Discounted Cash Flow analysis
INSURANCE / REINSURANCE
» Case Studies
Loss reserves estimation
OIL / GAS / ENERGY
» Case Studies
Exploration and production
Oil reserves estimation
Capital project estimation
SIX SIGMA / QUALITY ANALYSIS
» Details and Models
» Case Studies
Manufacturing quality control
Customer service improvement
DFSS / DOE
Lean Six Sigma
» Case Studies
Six Sigma and quality analysis
New product analysis
Product life cycle analysis
PHARMACEUTICALS / MEDICAL / HEALTHCARE
» Case Studies
New product analysis
Disease infection estimation
» Case Studies
Endangered species preservation
Pollution cleanup and projections
GOVERNMENT AND DEFENSE
» Case Studies
Welfare and budgetary projections
AEROSPACE AND TRANSPORTATION
» Case Studies
Highway planning and optimization
Supply chain distribution
How @RISK Works
Running an analysis with @RISK involves three simple steps:
1. Set Up Your Model. Start by replacing uncertain values in your spreadsheet with @RISK probability distribution functions, like Normal, Uniform, or over 50 others. These @RISK functions simply represent a range of different possible values that a cell could take instead of limiting it to just one case. Choose your distribution from a graphical gallery, or define distributions using historical data for a given input. Even combine distributions with @RISK’s Compound function. Share specific distribution functions with others using the @RISK Library, or swap out @RISK functions for colleagues who don’t have @RISK.
Next, select your outputs—the "bottom line" cells whose values interest you. This could be potential profits, ROI, insurance claims payout, disease recovery rate, or anything at all.
Define Uncertainty with Ease
@RISK comes with over 50 distribution functions. These are true Excel functions, behaving the same way as Excel’s native functions and giving you total modeling flexibility. Choosing which @RISK distribution function to use is easy because @RISK comes with a graphical distribution gallery that lets you preview and compare various distributions before selecting them. You can even set up your distributions using percentiles as well as standard parameters, and overlay different distribution graphs for comparison. You can use historical or industry data and @RISK’s integrated data fitting tool BestFit® to select the best distribution function and the right parameters. You can select the type of data to be fit (e.g. continuous. discrete, or cumulative), filter the data, specify distribution types to be fit and specify Chi-Squared binning to be used. Fitted distributions are ranked based on three statistical tests, and may be compared graphically. You can even overlay graphs of multiple fitted distributions. Fit results can be linked to @RISK functions, so the functions will update automatically when input data changes.
Input distributions may be correlated with one another, individually or in a time series. Correlations are quickly defined in matrices that pop up over Excel, and a Correlated Time Series can be added in a single click. A Correlated Time Series is created from a multi-period range that contains a set of similar distributions in each time period.
All @RISK functions and correlations in your model are summarized—with thumbnail graphs—in the dashboard-style @RISK Model window, and you can watch distribution graphs pop up as you browse through cells in your spreadsheet.
Share Your Model with Others
@RISK functions can be stored in the @RISK Library, a SQL database for sharing with other @RISK users. @RISK functions may also be removed with the Function Swap feature, enabling your models be to shared with colleagues who don’t have @RISK installed. @RISK will keep track of any changes that occur in the spreadsheet while the @RISK functions were “swapped out.” You can control how @RISK should update formulas when it finds changes in the model. In addition, you can have @RISK automatically swap out functions when a workbook is saved and closed and automatically swap in if necessary when a workbook is opened.
2. Run the Simulation. Click the Simulate button and watch. @RISK recalculates your spreadsheet model thousands of times. Each time, @RISK samples random values from the @RISK functions you entered, places them in your model, and records the resulting outcome. Explain the process to others by running your simulation in Demo Mode, with graphs and reports updating live as the simulation runs.
3. Understand Your Risks. The result of a simulation is a look at a whole range of possible outcomes, including the probabilities they will occur. Graph your results with histograms, Scatter Plots, cumulative curves, Box Plots, and more. Identify critical factors with Tornado charts and sensitivity analysis. Paste results into Excel, Word, or PowerPoint, or place them in the @RISK Library for other @RISK users. You can even save results and charts right inside your Excel workbook.
Clear, Easy-to-Understand Results
@RISK provides a wide range of graphs for interpreting and presenting your results to others. Histograms and cumulative curves show the probability of different outcomes occurring. Use overlay graphs to compare multiple results, and summary graphs and Box Plots to see risk and trends over time or over ranges. Right-click menus and handy toolbars make navigation a snap. All graphs are fully customizable—including titles, axes, scaling, colors, and more—and ready for export to Excel, Word, or PowerPoint. You can watch results graphs pop up as you browse through cells in your spreadsheet.
@RISK provides you with sensitivity and scenario analyses to determine the critical factors in your models. Use sensitivity analysis to rank the distribution functions in your model according to the impact they have on your outputs. See the results clearly with an easy-to-interpret Tornado diagram, or uncover relationships with Scatter Plots. Sensitivity analysis pre-screens all inputs based on their precedence in formulas to outputs in your model, thus reducing irrelevant data. In addition, you can use @RISK’s Make Input function to select a formula whose value will be treated as an @RISK input for sensitivity analysis. In this way, multiple distributions can be combined into a single input, streamlining your sensitivity reports.
All simulation results for both outputs and inputs are summarized—with thumbnail graphs—in the dashboard-style @RISK Results Summary window. Simulation results may be saved directly in your Excel workbook, and also placed in the @RISK Library to for sharing with other @RISK users.
Excel Ease of Use
@RISK is a true add-in to Microsoft Excel, integrating completely with your spreadsheet. Browse, define, analyze—while never leaving Excel. All @RISK functions are true Excel functions, and behave exactly as native Excel functions do. @RISK windows are all linked directly to cells in your spreadsheet, so changes in one place are carried out in the other. @RISK graphs point to their cells via callout windows. Drag-and-drop ease, context-sensitive right-click menus, and the @RISK toolbar make learning and navigating @RISK a snap.
|Advanced Simulation Engine C||•||•||•|
|Support for 2 CPUs or processor cores||•||•||•|
|Over 50 Built-in Distribution Functions||•||•||•|
|Integrated Distribution Gallery||•||•||•|
|Insert Function Command||•||•||•|
|@RISK Function Swap||•||•||•|
|Compound and Six Sigma functions||•||•||•|
|Variety of Result Graphs and Charts||•||•||•|
|Live Updating During Simulation||•||•||•|
|Sensitivity & Scenario Analysis||•||•||•|
|Correlation of Inputs||•||•||•|
|Freehand Distribution Artist||•||•||•|
|Integrated Distribution Fitting||•||•|
|Integration with Microsoft Project||•||•|
|Excel Developer Kit (XDK)||•||•|
|Advanced Sensitivity Analysis||•||•|
|@RISK Goal Seek||•||•|
|Full Multi-CPU Support||•|
|Simulation of Time-series Forecasts||•|
Optimization under uncertainty
Ranges for adjustable cells and constraints
Six solving methods, including GAs and OptQuest
Convergence monitoring and genetic operators
Original, Best, Last model updating
Three Editions to Meet Your Needs
@RISK is available in three editions: Standard, Professional, and Industrial.
Designed for professional-grade problems in any industry, @RISK Professional is perfect for most commercial uses. It provides a balance of advanced analysis and point-and-click ease of use, and includes:
- Integrated distribution fitting with BestFit® : Defines distribution functions for you based on historical or industry data.
- @RISK Library: A SQL database for storing and sharing with others @RISK distribution functions, model components, and simulation results.
- Excel Developer Kit (XDK): Automate and customize @RISK for Excel through a complete library of commands and functions that let you control every aspect of @RISK in your spreadsheet. Add @RISK for Excel to any custom application.
- Stress Analysis: Lets you control the range that is sampled from a distribution function, enabling you to see how different scenarios affect your bottom line without changing your model.
- Advanced Sensitivity Analysis: Lets you see how changes in any input—distributions or regular values—affect simulation results.
- @RISK Goal Seek: Uses multiple simulations to find an input value that achieves a target simulation result you specify.
Designed for your largest, most complex models, @RISK Industrial includes everything in @RISK Professional, plus the following:
- RISKOptimizer: Combines Monte Carlo simulation with
sophisticated optimization techniques to find the best combination of factors that lead to a desired result under uncertain conditions. Use RISKOptimizer for resource allocation,
scheduling, investment, route planning, and other types of tricky problems where you need to determine the best combination of inputs to maximize a return, minimize a cost, or achieve a specific target.
RISKOptimizer uses genetic algorithms and Optquest solving methods so you’ll be sure to have the right engine for any type of problem.
» More about RISKOptimizer
- Simulation of Time-series Forecasts: @RISK offers a 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 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 also 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.
- Full Multi-CPU support: Speed up simulations with parallel processing by using all multi-core processors and available CPUs within a single machine.
|Monte Carlo simulation||Shows possible outcomes to avoid pitfalls and identify opportunities|
|100% Excel calculations for simulation
||Highest level of accuracy, and maximum
use of multi-core processors for speed
|Seamless integration into Microsoft Excel||Never leave your spreadsheet; get up to speed quickly|
|Intuitive toolbars and right-click menus||Easy navigation—multiple ways to perform common tasks|
|Distribution palette and Insert Function command||Easy and accurate definition of uncertain factors|
|BestFit distribution fitting and Distribution Artist||Use data and expert judgment to define uncertain factors|
|Over 50 built-in distribution functions
||Represent virtually any uncertain
factor for accurate modeling
|@RISK Function Swap
||Remove (and later restore) @RISK functions for sharing models with non-@RISK users|
|Integration with Microsoft Project
||Read Microsoft Project schedules into @RISK for Excel for risk modeling of any project with maximum flexibility|
||Define, reuse, and share custom distributions and simulation results|
||Combines two distributions into one to streamline
insurance or other large models
|Percentile distribution parameters||More flexible ways to define uncertainty|
|Correlation of inputs and correlation time series
||Represent dependency between related
variables for accurate modeling
|Simulation of Time-series forecasts
||Understand the risks in values that change
|Demo Mode and live updating
||Graphs and reports update during simulation for
illustration to others
|Extensive settings control||Customize simulations to specific needs|
||“Split up” large simulations to run on multiple CPUs or cores and reduce simulation run time|
|Fully customizable presentation-quality graphs
||See the impact of risk and communicate to stakeholders|
|Reporting in Office
||All graphs and charts can be exported to Excel,
Word, and PowerPoint in native chart format for
easy distribution to others
|One-click Quick Reports
||See summary of charts and graphs
pre-formatted for one page
|Histograms, area, line, cumulative, summary,
box plot, and overlay graphs
|Variety of graphing and charting options
for easy, accurate communication
|Tornado charts and scatter plots
||Visually identify critical factors and trends, overall and for particular scenarios|
|Sensitivity and Scenario Analysis
||Identify the individual tasks that have the
most impact on results, and the particular
scenarios that lead to certain results
|Six Sigma functions||Report Six Sigma statistics for quality analysis|
|Multi-core processor and multiple CPU support||Speed up simulations|
|RISKOptimizer||Combines Monte Carlo simulation with sophisticated optimization techniques to find the best combination of factors that lead to a desired result under uncertain conditions.|
@RISK for Six Sigma
@RISK can also be used in Six Sigma and quality analysis to improve processes, reduce variability, and enhance the quality of products and services, and save money. @RISK includes a range of Six Sigma statistics, functions, and reports. With these tools, you can identify, measure, and root out the causes of variability in your production and service processes and designs.
Part of the DecisionTools Suite
@RISK is available by itself or as part of the DecisionTools Suite, Palisade’s complete risk and decision analysis toolkit. The DecisionTools Suite includes PrecisionTree for decision trees, TopRank for what-if analysis, NeuralTools and StatTools for data analysis, and more. @RISK is fully compatible with all DecisionTools programs and can be combined with them for greater insight and analysis. For example:
@RISK and TopRank
The focus of an @RISK analysis can be narrowed using TopRank. Especially with large models, this saves time and improves accuracy of your @RISK analysis. @RISK functions can also be used by TopRank to represent a wider range of values than TopRank’s standard functions.
@RISK and PrecisionTree
In addition, @RISK can be combined with PrecisionTree to represent uncertain chance events and payoffs in decision tree models. This enhances the accuracy of decision tree models by considering wider ranges of values for chance events instead of a few limited, discrete options.
@RISK then StatTools
@RISK results can be run through a StatTools analysis to assess confidence intervals. @RISK can also be applied to results from a StatTools time-series forecast to simulate possible outcomes with more precision.
Save Over 50%
When you buy the DecisionTools Suite, you save over 50% versus buying all components individually. The best analyses at a great price–with the DecisionTools Suite.
Licensing and Training
@RISK is available through a variety of licensing options, including corporate, network, and academic licenses. Training, consulting, and books can be bundled with your software to ensure you and your staff get the most out of your investment.
@RISK simulations are calculated 100% within Excel, supported by Palisade sampling and statistics proven in over twenty years of use. Palisade does not attempt to rewrite Excel in an external recalculator to gain speed. A single recalculation from an unsupported or poorly reproduced macro or function can dramatically change your results. Where will it occur, and when? Palisade harnesses the power of multiple CPUs and multi-core processors to give you the fastest calculations. Correct results-and fast-using @RISK!
COMPATIBILITY: @RISK and DecisionTools Suite software is compatible with all 32-bit and 64-bit versions of Microsoft Windows XP-8, Excel 2003-2013, and Project 2003-2013.
64-bit Compatible: 64-bit technology enables Excel and DecisionTools software to access much more computer memory than ever before. This allows for vastly larger models and greater computational power.