Decision model for forecasting projected Naval Enlisted Reserve attainments

Abstract

The intent of this MBA Project is to forecast naval enlisted reserve attainments for a given fiscal year, so Commander, Navy Recruiting Command (CNRC) can adequately establish goals. Forecasting is based on historical data from various sources. Three levels of data are examined. These levels include CNRC data broken down by total yearly accessions, CNRC data sorted by accessions and ratings, and Defense Manpower Data Center (DMDC) data sorted by accession source (Naval Veteran, Other Service Veteran, Non-Prior Service) and ratings. We compare all three sets of data to each other as well as previous research to ensure that data is accurate and to try to determine if there are trends. We use moving average, weighted moving average, and exponential smoothing on all data to determine which method is best in forecasting future attainments. In addition, a regression model is developed for the CNRC yearly accession data and compared to the other models to determine if it is a better forecasting model. We use DMDC data to try and determine where specific reserve attainments are coming from and forecast future attainments. We use this model to forecast the possibility of a Naval Veteran (NAVET) or Non-Prior Service (NPS) individual in joining the Naval Reserve and use this data to help Navy Recruiting Command establish more accurate reserve recruiting goals.