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<p>The Navy's Selective Reenlistment Bonus (SRB) Management System uses a model known as ROGER, which was developed as a Windows-based application in the mid 1990s to assist the Navy Enlisted Bonus manager in developing and analyzing SRB plans during the execution year. Substantial changes in the structure of the SRB program have led to increasing levels of predictive error in the model. Under-prediction of SRB Program costs by the model leads to over execution of the SRB budget, and necessitates the reprogramming of funds from other enlisted programs.  The objective of this thesis is to assess the performance of the Navy's ROGER model, which is used to forecast the reenlistment behavior of sailors in Zones A, B, and C, and estimate the budget costs of the SRB Program. The thesis will assess the accuracy of the reenlistment-forecasting model and identify factors that lead to prediction errors. In addition, the thesis will analyze the role of ROGER in the SRB planning process, which involves Naval Personnel Command, Enlisted Community Management Branch (BUPERS-32), as well as the Office of the Chief of Naval Operations (OPNAV). Finally, the thesis will outline methods to improve the identification of the population of SRB-eligible sailors in all Zones and the predictive accuracy of the ROGER model.
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<br>Subjects: Bounties, Military; United States; Prediction theory; Mathematical models</div>
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Alloway, Diana J.</div>
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