Data Analytics Development from Military Operational Data

Authors

  • James Downey
  • Zachary Ellis
  • Ethan Nguyen
  • Charlotte Spencer
  • Paul Evangelista United States Military Academy

DOI:

https://doi.org/10.37266/ISER.2021v9i2.pp76-82

Keywords:

National Training Center (NTC), Data Analytics

Abstract

Each year, the National Training Center (NTC) located at Fort Irwin, California, hosts multiple Brigade-level rotational units to conduct training exercises. NTC’s Instrumentation Systems (NTC-IS) digitally capture and store characteristics of movement and maneuver, use of fires, and other tactical operations in a vast database. The Army’s Engineer Research and Development Center (ERDC) recently partnered with Training and Doctrine Command (TRADOC) to make some of the data available for introductory analysis within a relational database. While this data has the potential to expose capability gaps, uncover the truth behind doctrinal assumptions, and create a sophisticated feedback platform for Army leaders at all levels, it is largely unexplored and underutilized. The purpose of this project is to demonstrate the value of this data by developing a prototype information system that supports post-rotation analytics, playback capabilities, and repeatable workflows that measure and expose ground-truth operational and logistical behavior and performance during a rotation. The Army modeling and analysis community will use these products to systematically curate and archive the database and enable future analysis of the NTC-IS data.

Author Biography

Paul Evangelista, United States Military Academy

Director, Engineering Management Program

Department of Systems Engineering,

United States Military Academy

Mahan Hall, Bldg 752, Room 420

West Point, NY 10996, USA

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Published

2022-01-15

How to Cite

Downey, J., Ellis, Z., Nguyen, E., Spencer, C., & Evangelista, P. (2022). Data Analytics Development from Military Operational Data. Industrial and Systems Engineering Review, 9(2), 76-82. https://doi.org/10.37266/ISER.2021v9i2.pp76-82

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