@RISK for Cost and Schedule Risk Using Risk Registers (with Example Model)

@RISK can be used in conjunction with MS Project and Excel to model the schedule and cost risks inherent in large, complex projects. This example demonstrates the use of @RISK to build a complete model of the construction of a new commercial venue. The model includes uncertainty in task times, a Risk Register for calculating […]

Free Minicourse in Renewable Energy Modeling using @RISK, with example models

How do we insure a reliable energy supply when using renewable energy sources? Solar power is inherently unreliable, fluctuating with time of day and degree of cloudiness. Wind power is a victim of air flow patterns. To prevent blackouts, renewable energy sources need to be backed up with conventional power sources. In effect, they require […]

@RISK Helps Zero-In on U.S. Senate Race Outcomes

The midterm Senate race is fast approaching—and so are the speculations on its outcome. Previously, Lawrence W. Robinson, Professor of Operations Management at Cornell University’s Johnson Graduate School of Management used @RISK to statistically predict the senate races, using data from the stats-centered news site, FiveThirtyEight.   FiveThirtyEight was founded by statistician and political analyst Nate […]

Risk Management Monitor Features Palisade’s World Cup Prediction Model

Palisade's detailed simulation of the 2014 World Cup garnered attention from the Wall Street Journal, and is now featured in the Risk Management Monitor. In the article, Palisade Vice President Randy Heffernan describes the model developed by Palisade trainer and consultant Fernando Hernández as follows: "By running 50,000 iterations in a Monte Carlo simulation and […]

How to win the World Cup Office Pool: Use DecisionTools Suite to Choose the Champions

As the World Cup in Brazil approaches this summer, worldwide anticipation has reached a fever pitch. In South America, and Brazil in particular, office pools and group bets are popping up, with soccer fans hoping to pick the winning team. At Palisade, we don’t believe in taking wild guesses—which is why Fernando Hernández, consultant and […]

Custom Solutions: Using @RISK for Oil Field Development Decisions

Oil companies need to assess new fields or prospects where very little hard data exists. Based on seismic data, analysts can estimate the probability distribution of the reserve size. With little actual data available, companies still must quantify and optimize the Net Present Value (NPV) of this asset. The number of wells to drill, the […]

Looking for inspiration for your models? Palisade just added 5 new Example Models!

Palisade provides a collection of example spreadsheet models that highlight different applications of @RISK and the DecisionTools Suite. These example files demonstrate a variety of risk analysis solutions, including credit risk analysis, financial risk analysis, energy risk analysis and more! Below are a sample of five of our newly posted example models. Price Evolution in […]

Estimating Costs using Guidelines in the U.S. Air Force’s “Cost Risk and Uncertainty Analysis Handbook”

In its 2007 Cost Risk and Uncertainty Analysis Handbook, the U.S. Air Force describes in great detail its methodology for estimating the total cost of a given system. This methodology is applied to a potential (but fictitious) missile system. Here, we have a series of 3 example models which use @RISK to build increasingly sophisticated […]

@RISK for Cost and Schedule Risk using Risk Registers (with example model)

@RISK can be used in conjunction with MS Project and Excel to model the schedule and cost risks inherent in large, complex projects. This example demonstrates the use of @RISK to build a complete model of the construction of a new commercial venue. The model includes uncertainty in task times, a Risk Register for calculating […]

Simple Oil and Gas Production Forecasting

@RISK can be very effectively used to forecast the production of oil and gas reserves about which little is known. This is a simple model forecasting production for a particular oil well. The estimated reserves within the well are uncertain and are represented with a Lognormal distribution function. The mean is 500,000 STB and the […]