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Forecasting the Marine Corps' Enlisted Classification Plan: Assessment of An Alternative Model

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Master's thesis

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In a given fiscal year, the United States Marine Corps accesses approximately 30,000 enlisted personnel into its ranks. This labor supply of recruits is classified into various Military Occupational Specialties MOSs according to the forecasted requirement for new personnel into a particular MOS. The Classification Plan is the primary initial training input into the Training Input Plan, which allocates all training resources for Training and Education Command. The current Classification Model is based on a steady-state Markov Model that estimates the first-term inventory of each initial training MOS inventory of personnel. A performance comparison was made against a transient Markov Model that solves for an optimal classification plan over the course of a four-year planning horizon. First, the validity of the steady-state assumption is tested and found to produce a variance of annual targets for each MOS throughout the Future Years Defense Plan that is prohibitively high. Next, a comparison of each models ability to forecast annual attrition by MOS between the years 2001 and 2011 is tested. Results indicate that the transient model produced a more accurate forecast for 5,321 out of 7,379 design points approximately 72 of the observations. The transient model achieved a Mean Absolute Proportional Error that was on average 14 percentage points smaller than that of the steady-state model. In over 25 of the cases, this difference exceeded 20 percentage points. Based upon this improved performance, it is recommended the Marine Corps adopt the enhanced transient Markov Model as the foundation for forecasting its annual Enlisted Classification Plan.

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  • Military Forces and Organizations

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