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APPLICATION SUCCESS STORY


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For Fentress Inc., Crystal Ball Pro is the Key to Forecasting Prison Populations

CUSTOMER OF THE MONTH (APRIL, 1999)

Fentress Incorporated, a Baltimore-Washington based consulting firm, specializes in resource planning for various U.S. criminal justice organizations. Tom Rubino, an Economist/Statistician at Fentress, is responsible for designing studies that address such long-range issues as personnel requirements and workload planning for these organizations. In the mid-1990s, when the Prisoner Services Division of the US Marshals Service (USMS) had limited success in predicting prisoner populations, they turned to Fentress, Rubino, and Crystal Ball Pro for a solution.

Forecasting the complex flow of prisoners through the criminal justice system is difficult due to the variety of potential paths and the length of time involved along each particular path. For example, one prisoner might quickly plea bargain and go to prison while another might plead innocent and go to a lengthy trial. In addition, the USMS is made up of 94 districts, each with distinct pathways and prison populations. Until very recently, only limited prisoner data was available to the USMS, which meant that only certain statistical methods, such as Regression Methods, Moving Average Methods, and Simple Trending Models, could be applied for forecasting.

The results of these methods posed a serious problem for the USMS because it ran the risk of operating under a budget shortfall if the forecasts were lower than the actual number of incarcerated prisoners. Given this situation, it was decided - through a collaborative effort that included Fentress Incorporated, the Bureau of Prisons and the USMS - that simulation modeling was the best method for understanding dynamic prisoner populations.

The Prisoners Services Division approached Fentress for a simulation-based solution, which Fentress demonstrated through a combination of spreadsheet models and Crystal Ball distributions. These models forecast the monthly prisoner population by federal judicial district. Next, each district's monthly forecasts were averaged to create an average annual prisoner population statistic. The Prisoners Services Division hired Fentress and adopted the models, which finally allow USMS managers to realistically plan their budgetary needs. "More importantly," Rubino added, "we are giving our clients credibility in requesting resources. The bottom line is to provide efficient criminal justice, and that can't be done with inefficient budgeting."

Rubino regularly employs the Bootstrap Extender to build confidence intervals around prisoner population forecasts, and these intervals define the probability of underfunding the program. Rubino also regards Crystal Ball's automated Report function and the Developer Kit as essential to his work. Crystal Ball reports help Fentress and client managers explain the simulation results to a general audience and to increase the efficiency with which Fentress builds each of the 94 district models. The Developer Kit has been used to automate the simulation process and report creation for each district model.

Two other organizations that Fentress consults for are the Executive Office for US Attorneys and the Administrative Office of the US Courts. For the US Attorneys, Fentress's models predict personnel requirements, specifically how many Full Time Equivalent (FTE) positions are required to cover work generated in future fiscal years. Long-range facility planning studies for the US Courts identify the need for new federal courthouses around the country, and they plan for the courthouse usage over the next thirty years.

As for the future, Rubino expects that CB Pro will be a regular tool for forecasting projects. Fentress anticipates applying optimization to their prisoner population models, and they will probably move to CB Turbo to reduce the time required for scenario testing. "CB Pro opened the doors for a number of new contracts and has given us credibility throughout the Department of Justice," said Rubino.

 
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