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Improving U.S. Navy Campaign Analyses with Big Data

Author

Listed:
  • Brian L. Morgan

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Harrison C. Schramm

    (CANA Advisors, Pacific Grove, California 93950)

  • Jerry R. Smith, Jr.

    (Naval Surface Warfare Center, Bethesda, Maryland 20817)

  • Thomas W. Lucas

    (SEED Center for Data Farming, Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Mary L. McDonald

    (SEED Center for Data Farming, Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Paul J. Sánchez

    (SEED Center for Data Farming, Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Susan M. Sanchez

    (SEED Center for Data Farming, Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Stephen C. Upton

    (SEED Center for Data Farming, Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

Abstract

Decisions and investments made today determine the assets and capabilities of the U.S. Navy for decades to come. The nation has many options about how best to equip, organize, supply, maintain, train, and employ our naval forces. These decisions involve large sums of money and impact our national security. Navy leadership uses simulation-based campaign analysis to measure risk for these investment options. Campaign simulations, such as the Synthetic Theater Operations Research Model (STORM), are complex models that generate enormous amounts of data. Finding causal threads and consistent trends within campaign analysis is inherently a big data problem. We outline the business and technical approach used to quantify the various investment risks for senior decision makers. Specifically, we present the managerial approach and controls used to generate studies that withstand scrutiny and maintain a strict study timeline. We then describe STORMMiner, a suite of automated postprocessing tools developed to support campaign analysis, and provide illustrative results from a notional STORM training scenario. This new approach has yielded tangible benefits. It substantially reduces the time and cost of campaign analysis studies, reveals insights that were previously difficult for analysts to detect, and improves the testing and vetting of the study. Consequently, the resulting risk assessment and recommendations are more useful to leadership. The managerial approach has also improved cooperation and coordination between the Navy and its analytic partners.

Suggested Citation

  • Brian L. Morgan & Harrison C. Schramm & Jerry R. Smith, Jr. & Thomas W. Lucas & Mary L. McDonald & Paul J. Sánchez & Susan M. Sanchez & Stephen C. Upton, 2018. "Improving U.S. Navy Campaign Analyses with Big Data," Interfaces, INFORMS, vol. 48(2), pages 130-146, April.
    • Handle: RePEc:inm:orinte:v:48:y:2018:i:2:p:130-146
    DOI: 10.1287/inte.2017.0900
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    References listed on IDEAS

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    1. Jack P.C. Kleijnen, 2015. "Design and Analysis of Simulation Experiments," International Series in Operations Research and Management Science, Springer, edition 2, number 978-3-319-18087-8, December.
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    3. Thomas W. Lucas & W. David Kelton & Paul J. Sánchez & Susan M. Sanchez & Ben L. Anderson, 2015. "Changing the paradigm: Simulation, now a method of first resort," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(4), pages 293-303, June.
    4. M Kress & I Talmor, 1999. "A new look at the 3:1 rule of combat through Markov Stochastic Lanchester models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(7), pages 733-744, July.
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