CB Tools Example Models |
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| Model: Magazine Checkout Sales From: Decisioneering (D) CB Tool: Batch Fit Detail: This model shows the power of the Batch Fit tool in Crystal Ball 2000. This model contains historical, non-time-series data for past sales of four magazines and Crystal Ball can fit that data with distributions to predict future sales. | 
For: Crystal Ball Level: Simple |
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| Model: Futura Apartments From: Decisioneering (D) CB Tool: Bootstrap Detail: This is the most basic of our models, and the basis of the Crystal Ball tutorial. As the owner of several apartments, you need to determine your potential profit given the uncertainties of number of apartments rented and amount of expenses you will have. |
For: Crystal Ball Level: Simple |
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| Model: Portfolio Allocation From: Decisioneering (D) CB Tool: Correlation Matrix Detail: This portfolio allocation model, used in our basic optimization tutorial, requires you to define decision variables and run OptQuest to determine an optimal investment strategy. The model uses standard deviation to limit risk. Includes optimizations setting file. | For: Crystal Ball & OptQuest Level: Simple
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| Model: Multi-Zone Reserve Estimation From: Stephen Hoye, Decisioneering, Inc. (D) CB Tool: Correlation Matrix Detail: When estimating reserves for wells or prospects with multiple producing zones, it is important to account for the dependencies that often occur not only between reservoir rock properties on a zone-by-zone basis, but also to quantify dependencies from one zone to another that may be the result of the geologic structural or stratigraphic framework associated with the pay zones. This model shows an approach that can be used to estimate multi-zone reserves accounting for uncertainty in each zone's reservoir parameters, and also incorporating in-zone and across-zone dependencies as a result of plausible observed or known reservoir and geologic information about the prospect. |
For: Crystal Ball Level: Simple |
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| Model: Oil Field Development From: Decisioneering (D) CB Tool: Decision Table Detail: Oil companies need to assess new fields or prospects where very little hard data exists. With little actual data available, the discovery team wants to quantify and optimize the Net Present Value (NPV) of this asset. Includes optimizations setting file and uses a percentile objective and a lookup table based on a decision variable. |
For: Crystal Ball Level: Simple |
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| Model: Risk Assessment at a Toxic Waste Site From: Decisioneering (D) CB Tool: Scenario Analysis Detail: This simple spreadsheet model predicts the cancer risk to the population from a toxic waste site. The pollutant at the waste site and the population close to the site are both sources of uncertainty, which complicates the calculation of a risk assessment value. Overestimating the population risk can mean a waste of resources on unnecessary remediation, while underestimating the risk can pose a very real danger to the local population. |
For: Crystal Ball Level: Simple |
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| Model: Reliability of a Helical Spring From: Decisioneering (D) CB Tool: Tornado Chart Detail: In this example, a design engineer is given the task of choosing the best material to use for a helical spring. |
For: Crystal Ball Level: Simple |
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| Model: Risk Assessment at a Toxic Waste Site From: Decisioneering (D) CB Tool: 2D Simulation Detail: This simple spreadsheet model predicts the cancer risk to the population from a toxic waste site. The pollutant at the waste site and the population close to the site are both sources of uncertainty, which complicates the calculation of a risk assessment value. Overestimating the population risk can mean a waste of resources on unnecessary remediation, while underestimating the risk can pose a very real danger to the local population. |
For: Crystal Ball Level: Simple |
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