Embedded analytics: improving decision support for humanitarian logistics operations

Daniel A. Griffith (), Bradley Boehmke (), Randy V. Bradley, Benjamin T. Hazen () and Alan W. Johnson ()
Additional contact information
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

Annals of Operations Research, 2019, vol. 283, issue 1, No 12, 247-265

Abstract: 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.

Keywords: Business analytics; Big data analytics; Humanitarian logistics; Decision tree; Embedded analytics; Data science (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s10479-017-2607-z

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