Developing an Air Force Retention Early Warning System Concept and Initial Prototype / David Schulker [and 8 others].

Format:

Book

Author/Creator:

Schulker, David, author.

Contributor:

Project Air Force (U.S.)

Rand Corporation

United States. Department of the Air Force

Language:

English

Subjects (All):

United States. Air Force--Recruiting, enlistment, etc--Mathematical models.

United States.

United States. Air Force--Personnel management--Mathematical models.

Other Title:

Developing an Air Force Retention Early Warning System

Place of Publication:

Santa Monica, Calif. RAND Corporation 2021

Summary:

RAND Project Air Force was tasked with developing a new capability for planners: a retention early warning system (REWS) that alerts policymakers when a subgroup of U.S. Air Force (USAF) military members is at risk for future shortages. The goal of the research project was to develop a forecasting model for retention, operationalized within a prototype decision-support application, that can alert decisionmakers to emerging problems and thus allow them enough time to consider adjusting accession and retention policies before shortages occur. The authors' overall approach to designing the system drew on widely used paradigms for solving data science problems. These paradigms emphasize understanding the business problem, drawing on a wide array of data sources and types, testing several flexible prediction approaches to optimize performance, and operationalizing the information for decisionmaking. To gain an understanding of the data sources that would be desirable for this application, the authors performed an extensive review of the turnover literature and identified gaps in existing USAF data collection efforts.

Contents:

Chapter One: Introduction

Chapter Two: What Information Is Most Relevant to Predicting Retention?

Chapter Three: Available Sources of Information for Predicting Air Force Retention

Chapter Four: Modeling Approaches and Performance Levels

Chapter Five: How Retention Predictions Can Be Used to Generate Warnings

Chapter Six: Next Steps for Further Development and Implementation

Appendix A: Creating the Analytic Data File

Appendix B: Machine Learning Algorithms

Appendix C: Decomposition Methodology

Appendix D: Literature Review Methodology

Appendix E: Considerations and Challenges in Applying Data Science to Air Force Human Resource Problems

Appendix F: Policy Impact Methodology.

Notes:

Desciption based on electronic resource