An Analysis of Proposed Changes to the U.S. Marine Corps Permanent Change of Station Policy: The Fiscal and Readiness Impacts

Abstract

This thesis analyzes two U. S. Marine Corps permanent changes of station (PCS) policy alternatives that, if initiated, could save millions of dollars. The analysis examines the quantitative and qualitative effects of: (1) increasing tour lengths for billets within the Continental United States (CONUS) ; and, (2) increasing lengths of unaccompanied overseas a billet tours. Longer tours mean fewer PCS moves and less expense; however, until now, no formal analysis has gone beyond the 'back of the envelope' to find how much can be saved. A financial analysis of the proposed policy changes finds that an unconstrained implementation of the alternatives could reduce the U. S. Marine Corps personnel budget by: (1) $13 million for longer CONUS tours; and, (2) $34 million for longer unaccompanied overseas tours. A PCS movement simulation using the Markov Chain Model finds that extending the unaccompanied overseas billet tours is a superior alternative to both current policy and the proposed change to CONUS tours. However, statistical analysis of data obtained from the 1993 Marine Corps Quality of Life Survey infers that a longer unaccompanied overseas tour may harm readiness.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1994
Accession Number
ADA280127

Entities

People

  • Thomas R. Hunt

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Business Administration
  • Data Analysis
  • Employment
  • Families (Human)
  • Information Science
  • Management Personnel
  • Marine Corps Personnel
  • Military Personnel
  • Personnel Management
  • Probability
  • Quality Of Life
  • Social Sciences
  • Statistical Analysis
  • Statistics
  • Surveys
  • United States

Readers

  • Life Cycle Cost Analysis
  • Military Leadership and Professional Education.
  • Military Mobilization and Reserve Forces Studies.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference