Analysis of the U.S. Marine Corps' steady state Markov model for forecasting annual first-term enlisted classification requirements

Abstract

The Marine Corps accesses approximately 29,000 to 36,000 new recruits annually. Determining how to classify these new enlistees into more than 200 Military Occupational Specialties is a critical task. These classification estimates must be precise, so the units within the Fleet Marine Force will have the necessary personnel to accomplish their mission. At the same time, these manpower planners must also balance the force structure to minimize personnel overages which could lead to excessive labor and training costs as well as promotion delays. The purpose of this research is to validate and, if necessary, improve the steady state Markov model currently being utilized by the manpower planners at Headquarters, U.S. Marine Corps (Code MPP-23) to forecast the annual personnel classification requirements of new recruits. From a mathematical perspective, all the essential elements of their model were present; however, some of the components like the year 1 continuation rate were not computed according to standard practice, and their estimates of the classification stocks are imprecise due to rounding errors inherent in their forecasting procedure. As a result, a revised model was developed to improve the accuracy and timeliness of the personnel classification forecasts. The recommendations were to implement the revised model and to review the computation of the continuation rates