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Embedded analytics: improving decision support for humanitarian logistics operations

Author

Listed:
  • Daniel A. Griffith

    (Air Force Institute of Technology)

  • Bradley Boehmke

    (Air Force Institute of Technology)

  • Randy V. Bradley

    (The University of Tennessee)

  • Benjamin T. Hazen

    (Air Force Institute of Technology)

  • Alan W. Johnson

    (Air Force Institute of Technology)

Abstract

Analytical techniques continue to advance in efficacy, as well as complexity. However, it is sometimes unrealistic to employ complex analyses during time-constrained humanitarian disaster operations. We propose that simple, embedded analytics tools can provide an effective and practical means toward managing humanitarian operations. In this paper, we demonstrate a real-world application of our technique in a patient evacuation context. This paper contributes to literature and practice by showing how simple analytic methods and open-source imagery tools can offer significant value to the humanitarian operations literature. The application also highlights some challenges to drawing a clear picture from disparate data sources in the humanitarian operations domain.

Suggested Citation

  • Daniel A. Griffith & Bradley Boehmke & Randy V. Bradley & Benjamin T. Hazen & Alan W. Johnson, 2019. "Embedded analytics: improving decision support for humanitarian logistics operations," Annals of Operations Research, Springer, vol. 283(1), pages 247-265, December.
    • Handle: RePEc:spr:annopr:v:283:y:2019:i:1:d:10.1007_s10479-017-2607-z
    DOI: 10.1007/s10479-017-2607-z
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    References listed on IDEAS

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    7. Jesus Gonzalez-Feliu & Mario Chong & Jorge Vargas-Florez & Irineu de Brito & Carlos Osorio-Ramirez & Eric Piatyszek & Renato Quiliche Altamirano, 2020. "The Maturity of Humanitarian Logistics against Recurrent Crises," Social Sciences, MDPI, vol. 9(6), pages 1-22, May.
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    10. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.

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