Forecasting PCS (Permanent Change of Station ORT Moves Using Tree Classifications

Abstract

This report describes a simple, objective method to produce Permanent Change of Station (PCS) move forecasts for use in budget development and execution. The forecasts assume that current PCS move policies will remain in effect throughout a 3 year forecast horizon. The forecasts are produced using a technique called tree classification, which stratifies Officer and Enlisted members into groups based on their Projected Rotation Date and Accounting Category Code. Each group's move behavior is projected separately, then combined to produce aggregate forecasts. Tree classification is used in this report to develop PCS move forecasts for FY89, FY90, and FY91 based on FY88 actual move behavior. The move projections are produced at levels of detail sufficient to develop the Military Personnel Navy, budget and to monitor budget execution. Forecasts are produced for Officer/Enlisted, month, detailing branch, and move type (Operational, Rotational, and Training. the method and forecasts are useful for a variety of reasons. The forecasts are based on simple, known assumptions and provide an objective estimate of future PCS moves. Thus, these forecast can be used as a starting point for what-if analyses, since these forecasts assume no changes in current PCS policies. Finally, the projections can be used as a baseline for comparison to detailer-specified move requirements.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1989
Accession Number
ADA213235

Entities

People

  • Chester Pabiniak
  • Robert M. Holmes

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Artificial Intelligence
  • Business Administration
  • Classification
  • Delphi Method
  • Determinants (Mathematics)
  • Engineering
  • Financial Management
  • Human Resources
  • Management Personnel
  • Military Personnel
  • Naval Operations
  • Navy
  • Plastic Explosives
  • Social Sciences
  • Training

Readers

  • Atmospheric Science/Meteorology
  • Regression Analysis.
  • Robotics and Automation.