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RISK: Risk Analysis Software using Monte Carlo Simulation for Excel - at risk. 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. Using genetic algorithms or Opt. Quest, along with @RISK functions, RISKOptimizer can determine the best allocation of. RISK in project management» @RISK in Six Sigma@RISK videos @RISK is also available in Spanish, Portuguese, German, French, Russian, Japanese, and Chinese. Update Now @RISK 7. New and enhanced graphing options, faster performance, and sophisticated analytics make @RISK 7.
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Learn more about What’s New in @RISK 7. On- Demand Webinar: What's New in @RISK and Decision. Tools Suite 7. 5 Tornado graphs have long been used to identify the critical variables affecting a risk analysis. Version 7. 5 adds a new graph type called Contribution to Variance which shows the amount of the variance in the output attributable to each input variable.
In addition, you can now overlay tornado graphs from multiple simulations to compare the effects of model changes. Watch The Dinner Online. Lastly, we’ve added shading options on the graph bars to indicate whether inputs are high or low when the output increases or decreases. RISKOptimizer, available in @RISK Industrial, is a powerful tool that allows you to optimize linear and nonlinear problems in uncertain situations. It combines optimization with Monte Carlo simulation, and is very computationally intensive. New version 7. 5 now runs at least four times faster than previous versions!
Sixteen new distribution functions have been added to @RISK, along with six new statistical functions. The new functions are important for accurate, insightful modeling of uncertainty in finance, insurance, reliability, and other applications. If you need to run Excel Solver during an @RISK simulation, you can now do so without writing any VBA code, saving you time.
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Version 7. 5 makes this easy to set up via dialog box, and it works for Excel Goals Seek and Palisade's Evolver as well. All @RISK graphs and reports now feature an updated, more streamlined interface, as well as support for Excel’s built- in themes and colors. Now you can apply your company’s standard Excel themes to your @RISK reports! In addition, graphical displays have been improved for ultra- high resolution monitors.
You can now control the number of CPUs @RISK uses to run simulations. We’ve also added easy access to @RISK statistics functions in the Model and Summary Results windows, saving you time in modeling.@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. INDUSTRYSAMPLE APPLICATIONFINANCE / SECURITIES»Models»Case Studies. Retirement planning. Currency valuation.
Real options analysis. Watch Big Night Online more. Discounted Cash Flow analysis. Value- at- risk. Portfolio optimization INSURANCE / REINSURANCE »Models»Case Studies.
Loss reserves estimation. Premium pricing. OIL / GAS / ENERGY»Models»Case Studies. Exploration and production. Oil reserves estimation Capital project estimation Pricing Regulation compliance.
SIX SIGMA / QUALITY ANALYSIS»Details and Models»Case Studies. Manufacturing quality control. Customer service improvement. DMAICDFSS / DOELean Six Sigma.
MANUFACTURING»Case Studies. Six Sigma and quality analysis New product analysis.
Production siting Plant shutdown Product life cycle analysis. PHARMACEUTICALS / MEDICAL / HEALTHCARE»Case Studies. New product analysis. R& D estimation. Disease infection estimation ENVIRONMENT»Case Studies. Endangered species preservation. Pollution cleanup and projections.
GOVERNMENT AND DEFENSE»Case Studies. Resource allocation. War games. Welfare and budgetary projections. AEROSPACE AND TRANSPORTATION»Case Studies. Cost estimating. Highway planning and optimization Supply chain distribution. 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 or Uniform, or dozens of 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. See the @RISK distribution palette.
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. RISK comes with dozens of distribution functions for defining uncertain variables. 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. RISK’s integrated data fitting tool Best.
Fit® to select the best distribution function and the right parameters. You can select the type of data to be. Chi- Squared binning to be used. Fitted distributions are ranked based. You can even overlay graphs of multiple fitted distributions. Fit results can be linked to @RISK functions, so the functions. 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. 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. 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.