Application of Absorbing Markov Chains to the Assessment of Education Attainment Rates within Air Force Materiel Command Civilian Personnel

Abstract

Increasing the education levels of an organization is a common response when attempting to improve organizational performance; however, organizational performance improvements are seldom found when the current and future workforce education levels are unknown. In this research, absorbing Markov chains are used to probabilistically forecast the educational composition of the Air Force Materiel Command civilian workforce to enable organizational performance improvements. Through the purposeful decoupling of effects resulting from recent workforce arrivals and education level progressions, this research attempts to determine the implications that stationarity assumptions have throughout the model development process of an absorbing Markov chain. The results of the analysis indicate that the four combinations of stationarity assumptions perform similarly at representing the historical data and that the forecasted educational attainment rates of the Air Force Materiel Command civilian workforce are expected to increase significantly.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2019
Accession Number
AD1077502

Entities

People

  • Matthew C. Ledwith

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Aircraft Design
  • Attrition
  • Business Administration
  • Civilian Personnel
  • Corporations
  • Data Set
  • Department Of Defense
  • Education
  • Employment
  • Engineering
  • Enlisted Personnel
  • Management Personnel
  • Manpower
  • Markov Chains
  • Military Education
  • Military Personnel
  • Military Science
  • Operations Research
  • Organizational Structure
  • Personnel Management
  • Personnel Retention
  • Probabilistic Models
  • Probability
  • Stochastic Processes
  • Students
  • United States
  • United States Government

Readers

  • Computational Modeling and Simulation
  • Logistics and Supply Chain Management.
  • STEM Education