Improving U.S. Navy Campaign Analyses with Big Data

Brian L. Morgan (), Harrison C. Schramm (), Jerry R. Smith, Jr. (), Thomas W. Lucas (), Mary L. McDonald (), Paul J. Sánchez (), Susan M. Sanchez () and Stephen C. Upton ()
Additional contact information
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

Interfaces, 2018, vol. 48, issue 2, 130-146

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.

Keywords: enterprise risk assessment; project management; data farming; defense; simulation (search for similar items in EconPapers)
Date: 2018
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