Developing a Solution to the TRADOC Analysis Center’s Big Data Problem: A Big Data Opportunity


  • Lee Bares
  • Daniel Davis
  • Daniel Min
  • Kenneth Rau
  • Matthew Dabkowski



As data production, collection, and analytic techniques grow, emerging issues surrounding data management and storage challenge businesses and organizations around the globe. The US Army Training and Doctrine Command’s Analysis Center (TRAC) is no exception. For example, among TRAC's many tasks is the evaluation of new materiel solutions for the Army, which typically necessitates the use of computer simulation models such as COMBAT XXI. These models are computationally expensive, and they generate copious amounts of data, straining TRAC's current resources and forcing difficult, suboptimal decisions regarding data retention and analysis. This paper addresses this issue directly by developing "big data" solutions for TRAC and evaluating them using its organizational values. Framed in the context of a use case that prescribes system requirements, we leverage Monte Carlo simulation to account for inherent uncertainty and, ultimately, focus TRAC on several high potential alternatives.


Arthur, L. (2013). What is Big Data? Forbes. Retrieved from

Carlucci, R. & Zoller, N. (2016). Analysis of Alternatives (AoA) Process Improvement Study (CAA-2016058). Fort Belvoir, VA: Center for Army Analysis. Retrieved from

COMBAT XXI. Fort Leavenworth, KS: TRADOC Analysis Center. Retrieved from

Griggs, B. (2011). The Commodore 64, that 80’s computer icon, lives again. CNN. Retrieved from

Parnell, G., Driscoll, P., & Henderson, D. (Eds.). (2011). Decision Making in Systems Engineering and Management (2nd ed.). Hoboken, NJ: John Wiley & Sons, Inc.

TRADOC Analysis Center. (2017). US Army TRADOC Analysis Center (TRAC): Overview [PowerPoint slides].

TRADOC Analysis Center. (2017). USMA DSE Capstone Visit [PowerPoint slides].



How to Cite

Bares, L., Davis, D., Min, D., Rau, K., & Dabkowski, M. (2019). Developing a Solution to the TRADOC Analysis Center’s Big Data Problem: A Big Data Opportunity. Industrial and Systems Engineering Review, 6(2), 82-87.

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