A Time Series Analysis of U.S. Army Enlisted Force Loss Rates.

Abstract

The analysis and prediction of personnel loss behavior is critical to effective manpower planning and to the U.S. Army's Enlisted Personnel Strength Management System (EPSMS). In support of efforts to modernize the EPSMS, this thesis examines the method by which the Enlisted Loss Inventory Model (ELIM) analyzes loss rates and forecasts them into the future. Time series analysis techniques seek to identify patterns in data and forecast them into the future via time based extrapolations. Four such methods were used to construct loss rate forecasts from data. These methods were the arithmetic mean, exponential smoothing (the current ELIM method), seasonal exponential smoothing and an autoregressive moving average model. Forecasted rates were used to project force strengths which were in fact known. The resulting errors in forecasted strength were analyzed, compared and contrasted with respect to the methods. Error analysis revealed no significant performance differences between the methods. Hence, the simplest methods (mean and exponential smoothing) may be viewed as more economical and preferred.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1996
Accession Number
ADA326760

Entities

People

  • Edward T. Dewald

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Army Personnel
  • Business Administration
  • Computer Programs
  • Computers
  • Employment
  • Enlisted Personnel
  • Error Analysis
  • Errors
  • Inventory
  • Linear Programming
  • Management Personnel
  • Mathematical Analysis
  • Mathematical Models
  • Recruiting
  • Statistical Analysis
  • Time Series Analysis
  • United States Naval Academy

Readers

  • Computational Modeling and Simulation
  • Personnel Management and Statistics in the Military and Department of Defense
  • Regression Analysis.