Example Models

@RISK and the DecisionTools Suite are used for a wide variety of applications in business, engineering, science, and more. See more example spreadsheet models highlighting different applications of the software at http://www.palisade.com/models/

@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 contingencies, and a link to real-time cash flows in an NPV calculation model.

@RISK probability distributions have been assigned to the durations of several tasks in the schedule, some with a distribution and others using Risk Categories. The uncertain task times are assumed to be uncorrelated.

A Risk Register lists three possible risk events that could impact the project schedule and costs. By using the RiskProjectAddDelay function, these risks introduce schedule delays and associated costs. Specifically, this function allows the model to generate new tasks dynamically, depending on whether the risks occur or not. Changes are reflected at run time only, so it is necessary to run a simulation to see the impact and results of the Risk Register.

The example also contains a model of cash flows that leads to the NPV of the project. In particular, the project costs create a Timescaled Data report. This collects the total cumulative costs during a simulation. After a simulation, you can see the total cumulative costs for the project as they grow over time.

The other reports generated are the NPV and the Contingency for the Risk Register, at different confidence levels. Finally, the cash flow also includes a Revenue Adjustment calculation that takes the portion of the year in which Sales are initiated and applies a discount to the predicted annual revenue.

» Download the model (Requires @RISK 6.x Professional or higher, Microsoft Project must be installed.): XLSX fileMPP file
» Download a trial of @RISK Industrial

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

How do we insure a reliable energy supply when using renewable energy sources?

Renewable Energy Modeling

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 virtually 100% backup with fossil, nuclear, hydro sources of power. Think of the repercussions of a solar eclipse and calm winds on renewable energy output, which occurred in Europe in 2015.

In this free on-demand minicourse, Professor Roy Nersesian models the complexities of this problem – and demonstrates solutions – using @RISK for Excel.

Integrating Renewables with Electricity Storage, using @RISK

Materials include:

  • 1-hour webinar delivered by Roy Nersesian
  • Energy example models
  • 50-page whitepaper
  • Presentation slidedeck

» Go to free minicourse now

» Case study: @RISK Helps Integrate Renewable Energy Sources

@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 Silver, who, in his forecasts earlier in the year, initially summed up the probabilities of either Democrats or Republicans winning all their races.

Robinson took this analysis a step further by adding Monte Carlo simulation to the mix. While Silver warned in previous articles that to assume races are uncorrelated is “dubious,” and that Monte Carlo simulations requires variables to be uncorrelated, Robinson demonstrated that it is in fact very possible to include correlation in Monte Carlo analyses.

He started by creating a “lower bound” (zero correlation) and an “upper bound” (total correlation) in his model, and showed that Democrats’ chances of retaining control only fell somewhere between 41% and 50%.

Robinson_Probability

The FiveThirtyEight Approach

Fast-forward a few months, and FiveThirtyEight’s models have gotten considerably more complex and data-rich, and their interactive forecasts are updated almost daily. As of this writing, the model predicts that Democrats have a 42% chance of retaining the Senate next year.

538_2014Senate_Probabilities

Unlike their earlier forecasts, “they’ve also included a correlation, of a type in their model,” says Robinson. “They do not explicitly use a correlation coefficient, as I did—instead, they change the distribution of the candidate’s lead.” Robinson explains that Silver and FiveThirtyEight introduce correlation through an additional random variable representing what they’ve labelled “national error,” which they generate and add to the mean margin of victory of every candidate.

This national error “could be a sex scandal, or some underlying and largely uncaptured sentiment in the nation,” Robinson explains. “For example, in the 2012 presidential race, it might have been Hurricane Sandy, and how presidential Obama looked in response.”

In the FiveThirtyEight forecast model, if the national error (whatever it represents) turns out to be +3 for the Republicans, they shift the mean margin of victory three points towards each and every Republican. “Unfortunately, nowhere in their post do they specify the probability distribution for his new ‘national error’ random variable,” Robinson says. “Thus it is not possible to know how correlated the individual races are with one another.”

@RISK Presents an Alternative Method

Because FiveThirtyEight’s methods are not entirely clear, Robinson wanted to devise a way to arrive at these forecasts using his own statistical methods, and to use a correlation that is explicitly defined. Instead of just using FiveThirtyEights’s “Leader’s chance of winning,” which was only given to the nearest percent, Robinson started with the mean and (estimated) standard deviation of the margin of victory, and calculated the probability of winning by assuming that the margin of victory on Election Night was normally distributed. “Although Silver says he assumes that the victory margin is leptokutic [has fat tails] for finding the probability of winning, he never specifies its probability distribution,” says Robinson. “I found that the standard assumption that the margin was normally distributed better matched his reported analysis.”

Robinson then built a Monte Carlo model in Excel using @RISK, treating the outcome of each race as a Bernoulli (0/1, win/lose) random variable. He then introduced a correlation matrix that captured the correlation between every pair of races, and ran 27 different simulations (each one simulating 400,000 elections) for correlations ranging between 0% and 100%. His results closely match that of FiveThirtyEight’s, showing the probability that Democrats will retain control of the Senate as a function of the correlation among races. “Now we can say that, as long as the correlation is between 20% and 97%, the probability that the Democrats will retain control will be between 40% and 42%,” says Robinson. “The advantage of this approach,” Robinson says, “is that we specify the correlation precisely, and that we conduct robustness analysis to see how the results change with the correlation.”

Interested in playing a political prognosticator? Check out our models here and run the @RISK simulation yourself.

 

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.

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 mapping out the likely winners in a decision tree, Hernández created a model that depicts the probabilities of different teams winning at different stages, and calculates the overall odds of each team winning the championship."

The model predicts that Brazil, with its home-field advantage, has a 17% chance to take home the Cup. Without home-field advantage, Germany would likely triumph, with a 19.9% chance of winning.

Heffernan adds the unfortunate fact that the U.S. stands a mere 2% chance of victory.

Read the original article in the Risk Management Monitor here.

For more information on the model and how it was developed, check out the case study here: How to win the World Cup Office Pool: Use DecisionTools Suite to Choose the Champions.

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

How to Win the World Cup Office Pool

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 trainer for Palisade Corporation, developed a model using DecisionTools Suite software to forecast the probabilities of each one of the 32 national soccer teams winning this ultimate championship.

Taking data from the rankings of over 200 national teams from FIFA spanning the past four years (2011-2014), Hernández created a model that uses @RISK to determine the probabilities of different teams winning at different stages, and PrecisionTree for mapping this information into a tree, or bracket, format. @RISK uses Monte Carlo simulation to compute thousands of different possible outcomes for the tournament automatically. The historic strengths and weaknesses of each team are accounted for in the statistical models used to represent each matchup.

After running 50,000 iterations, the model probabilistically channels each team into an eventual tournament win, and calculates its odds.Depending on certain ranking assumptions, the odds calculations may vary significantly. However, a robust approach that considers both historic and current rankings yielded the following results:

With a home advantage incorporated, Brazil has the largest probability of becoming champions with a 17% chance, with Spain coming as a runner up with 12% probability. Aside from those two pack-leaders, the following six teams have the next greatest chances in taking the cup (in descending order): Switzerland (8%), Greece (8%), Germany (7%), Colombia (7%), Argentina (6%) and Uruguay (5%). Clearly, these probabilities do not differ significantly, which could make for an unpredictable championship if the two front-runners fail to win.

Without considering home advantage, Germany would be the most likely winner, with a 19.9% chance.

Hailing from Costa Rica, Hernández concedes that his trust in risk modeling may override national pride when it comes to placing bets on the World Cup. “I am still not sure whether I would bet on my country, Costa Rica, in the office pool. In a group with three former World Champions–Uruguay, Italy and England–it only stands a 23% chance of classifying for second round, and only one chance in 440 of becoming World Champions.”

» "How to Win the World Cup Office Pool"
» Webcast on Friday, June 6th with Fernando Hernández
 Discuss the model in Palisade's LinkedIn group
 
» "Cómo ganar la Copa Mundial en las apuestas de la oficina"
» Webcast con Fernando Hernández, viernes el 6 de junio
 Discutir el modelo en los grupos de LinkedIn de Palisade
 
» "Como ganhar o bolão da Copa do Mundo"
» Webcast com Fernando Hernández, Sexta-feira 06 de junho
 Discutir o modelo em grupos do LinkedIn da Palisade

 

 

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 size of the processing facility, and the plateau rate of the field must all be optimized. The following example is a custom application written by Palisade Custom Development using @RISK’s XDK in Excel.

Oil Field Development Screenshots

This analysis can be simplified by representing the production profile by three phases:

  1. Build up: The period when wells are drilled to gain enough production to fill the facilities.
  2. Plateau: After reaching the desired production rate (plateau), the period when production is continued at that rate as long as the reservoir pressure is constant and until a certain fraction of the reserves is produced. In the early stages of development, this fraction can only be estimated, and production above a certain rate influences plateau duration.
  3. Decline: The period when production rates, P, decline by the same proportion in each time step, leading to an exponential function: P(t) = P(0) exp(-c*t), where t is the time since the plateau phase began and c is some constant.

With only estimates for the total Stock Tank Oil Initially In Place (STOIIP = reserve size) and percent recovery amounts, the objective is to select a production rate, a facility size, and well numbers to maximize some financial measure. In this example, the measure used is the P10 of the NPV distribution. In other words, the oil company wants to optimize an NPV value which they are 90% confident of achieving or exceeding.

As described, the problem is neither trivial nor overly complex. A high plateau rate doesn’t lose any reserves, but it does increase costs with extra wells and larger facilities. However, facility costs per unit decrease with a larger throughput, so choosing the largest allowed rate and selecting a facility and number of wells to match might be appropriate.

This is just one example of how Palisade can provide personalized risk solutions for your business needs. We offer custom software development services as well as software developer kits to create your own applications integrating @RISK, RISKOptimizer, and other Palisade technology. We can also help automate Palisade software using VBA in Microsoft Excel or Project.

» Learn more about Palisade's Custom Development

You may also be interested in the following:

Free Webcast this Thursday: "Exploring Oil & Gas Applications of @RISK and the DecisionTools Suite" – See more at: https://blog.palisade.com/blog/risk-and-decision-analysis-news/free-webcast-this-thursday-exploring-oil-and-gas-applications-of-risk-and-the-decisiontools-suite#sthash.sev3ym2s.dpuf

Cost Risk Analysis Example Movie: Palisade's Custom Development Team uses @RISK's XDK for this custom application
 

 

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.

5 New Example Models from Palisade

Price Evolution in Markov Chain (Minimum edition: @RISK 6.0 Standard)
A Markov chain is a process observed through time where the probability distribution of the next state of the process, given the current state, is independent of the past states. When dealing with Markov chains, it is always interesting to see whether future statistics depend much on the starting state.
» Download Example Model

Projecting Oil Prices (Minimum edition: @RISK 6.0 Industrial)
This model illustrates one possible way oil prices might change through time, as influenced by the market. @RISK's distribution fitting tool is used to simulate future absolute price changes based on historical daily oil prices.
» Download Example Model

Hydroelectric Power (Minimum edition: @RISK 6.0 Standard)
This model illustrates one possible simulation of hydroelectric power generation for a 120-month horizon. There are three sources of uncertainty: monthly desired power (as a percentage of the maximum possible output), monthly rainfall, and monthly evaporation. This model is based on Roy L. Nersesian's book Energy Risk Modeling.
» Download Example Model

Solar and Wind Power 1 (Minimum edition: @RISK 6.0 Standard)
This model illustrates the daily output of a combination of solar and wind energy units. The solar unit is affected by cloud cover with a temperature adjustment. The wind unit is modeled by a random wind speed and an ouput function of this wind speed. The @RISK outputs include hourly and total daily output values for solar, wind, and combined solar and wind. This model is based on Roy L. Nersesian's book Energy Risk Modeling.
» Download Example Model

Solar and Wind Power 2 (Minimum edition: @RISK 6.0 Industrial)
This example extends the previously listed Solar and Wind Power 1 model. Its goal is to use RISKOptimizer to find the best combination of solar and wind units to match uncertain energy demands for a 100-day period. This model is based on Roy L. Nersesian's book Energy Risk Modeling.
» Download Example Model

 

The above example files deal with risk analysis solutions in the marketing and energy sectors.  Palisade also offers example models in a variety of other industries including academia, agriculture, construction, pharmaceutical, transportation and more!

» View other @RISK and the DecisionTools Suite example models

» Download a free trial version of the DecisionTools Suite to view the models in full

 

See also: New in DecisionTools Suite 6: New Examples and Tutorials

 

 

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 models according to the guildelines of the handbook.

Air Force Missile Cost 0 – Deterministic Model
The handbook stresses the importance of first developing a deterministic model, based on point estimates.

Air Force Missile Cost 1 – Basic @RISK Model
This Basic @RISK Model builds upon the previous deterministic model by incorporating uncertainty according to the guildelines in the Air Force handbook.

Air Force Missile Cost 2 – Correlated Model
This model builds upon the previous model by incorporating explicit correlations between selected inputs, again according to the guildelines in the Air Force handbook.

» Example Models
» Download a trial version of @RISK

 

 

@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 contingencies, and a link to real-time cash flows in an NPV calculation model.

@RISK Project Management Example Models@RISK probability distributions have been assigned to the durations of several tasks in the schedule, some with a distribution and others using Risk Categories. The uncertain task times are assumed to be uncorrelated.

A Risk Register lists three possible risk events that could impact the project schedule and costs. By using the RiskProjectAddDelay function, these risks introduce schedule delays and associated costs. Specifically, this function allows the model to generate new tasks dynamically, depending on whether the risks occur or not. Changes are reflected at run time only, so it is necessary to run a simulation to see the impact and results of the Risk Register.

The example also contains a model of cash flows that leads to the NPV of the project. In particular, the project costs create a Timescaled Data report. This collects the total cumulative costs during a simulation. After a simulation, you can see the total cumulative costs for the project as they grow over time.

The other reports generated are the NPV and the Contingency for the Risk Register, at different confidence levels. Finally, the cash flow also includes a Revenue Adjustment calculation that takes the portion of the year in which Sales are initiated and applies a discount to the predicted annual revenue.

 » Download the model. (Requires @RISK 6.x Professional or higher)
 » Download a trial of @RISK 6.1 Industrial

See also: Contingency Calculation in Cost Risk Analysis

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 standard deviation is 50,000 STB.

The output in the model is the NPV of the reserves for the first 10 years of production. Other factors considered include the decline rate, the gas-oil ratio (GOR), the prices of oil and gas, as well as the rate of increase of the prices of oil and gas. The only input factor that contains an @RISK probability distribution function is reserves, but you could make the model more realistic by using distribution functions to describe the decline rate, GOR, price of oil, etc.

» Download the example