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Maximizing the U.S. Army’s Future Contribution to Global Security Using the Capability Portfolio Analysis Tool (CPAT)

Author

Listed:
  • Scott J. Davis

    (United States Army, Warren, Michigan 48397)

  • Shatiel B. Edwards

    (United States Army, Warren, Michigan 48397)

  • Gerald E. Teper

    (United States Army, Warren, Michigan 48397)

  • David G. Bassett

    (United States Army, Warren, Michigan 48397)

  • Michael J. McCarthy

    (United States Army, Warren, Michigan 48397)

  • Scott C. Johnson

    (United States Army, Warren, Michigan 48397)

  • Craig R. Lawton

    (Sandia National Laboratories, Albuquerque, New Mexico 87185)

  • Matthew J. Hoffman

    (Sandia National Laboratories, Albuquerque, New Mexico 87185)

  • Liliana Shelton

    (Sandia National Laboratories, Albuquerque, New Mexico 87185)

  • Stephen M. Henry

    (Sandia National Laboratories, Albuquerque, New Mexico 87185)

  • Darryl J. Melander

    (Sandia National Laboratories, Albuquerque, New Mexico 87185)

  • Frank M. Muldoon

    (Sandia National Laboratories, Albuquerque, New Mexico 87185)

  • Brian D. Alford

    (Booz Allen Hamilton, Huntsville, Alabama 35806)

  • Roy E. Rice

    (Teledyne Brown Engineering, Huntsville, Alabama 35805)

Abstract

Recent budget reductions have posed tremendous challenges to the U.S. Army in managing its portfolio of ground combat systems (tanks and other fighting vehicles), thus placing many important programs at risk. To address these challenges, the Army and a supporting team developed and applied the Capability Portfolio Analysis Tool (CPAT) to optimally invest in ground combat modernization over the next 25–35 years. CPAT provides the Army with the analytical rigor needed to help senior Army decision makers allocate scarce modernization dollars to protect soldiers and maintain capability overmatch. CPAT delivers unparalleled insight into multiple-decade modernization planning using a novel multiphase mixed-integer linear programming technique and illustrates a cultural shift toward analytics in the Army’s acquisition thinking and processes. CPAT analysis helped shape decisions to continue modernization of the $10 billion Stryker family of vehicles (originally slated for cancellation) and to strategically reallocate over $20 billion to existing modernization programs by not pursuing the Ground Combat Vehicle program as originally envisioned. More than 40 studies have been completed using CPAT, applying operations research methods to optimally prioritize billions of taxpayer dollars and allowing Army acquisition executives to base investment decisions on analytically rigorous evaluations of portfolio trade-offs.

Suggested Citation

  • Scott J. Davis & Shatiel B. Edwards & Gerald E. Teper & David G. Bassett & Michael J. McCarthy & Scott C. Johnson & Craig R. Lawton & Matthew J. Hoffman & Liliana Shelton & Stephen M. Henry & Darryl J, 2016. "Maximizing the U.S. Army’s Future Contribution to Global Security Using the Capability Portfolio Analysis Tool (CPAT)," Interfaces, INFORMS, vol. 46(1), pages 91-108, February.
  • Handle: RePEc:inm:orinte:v:46:y:2016:i:1:p:91-108
    DOI: 10.1287/inte.2015.0824
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    References listed on IDEAS

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    1. Warren P. Adams & Stephen M. Henry, 2012. "Base-2 Expansions for Linearizing Products of Functions of Discrete Variables," Operations Research, INFORMS, vol. 60(6), pages 1477-1490, December.
    2. Juan Pablo Vielma & Shabbir Ahmed & George Nemhauser, 2010. "Mixed-Integer Models for Nonseparable Piecewise-Linear Optimization: Unifying Framework and Extensions," Operations Research, INFORMS, vol. 58(2), pages 303-315, April.
    3. Warren Adams & Hanif Sherali, 2005. "A Hierarchy of Relaxations Leading to the Convex Hull Representation for General Discrete Optimization Problems," Annals of Operations Research, Springer, vol. 140(1), pages 21-47, November.
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    Cited by:

    1. Michael F. Gorman, 2021. "Contextual Complications in Analytical Modeling: When the Problem is Not the Problem," Interfaces, INFORMS, vol. 51(4), pages 245-261, July.

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