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Embedded analytics: improving decision support for humanitarian logistics operations

  • Applications of OR in Disaster Relief Operations
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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.

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Notes

  1. Using guidance from Martin (1993), we address only the truly avoidable costs incurred when choosing between organic and contracted patient evacuation flights, instead of the absolute cost of flying an aeromedical evacuation mission. Hence, we assume that the US Department of Defense (DoD) will not reallocate committed resources as a result of contracting aeromedical evacuation missions. We also assume the DoD will not completely privatize aeromedical evacuation and will maintain an organic aeromedical evacuation capability. When the DoD determines to “consume” an additional aeromedical evacuation mission, they do not need to increase the resources supplied by a full “mission’s worth” if they have excess capacity that is already committed to the mission (Kaplan and Cooper 1998). For example, the pilots, aeromedical evacuation crews, aircraft, runways, and control towers are committed resources and will not change with an additional mission. Therefore the only additional costs incurred are the truly variable costs such as fuel, maintenance, and supplies. In the same manner, aeromedical evacuation missions, like all services, cannot be inventoried and have no residual value.

  2. While this is the optimal choice, in this scenario, the system is incapable of reaching Urgent patients located further than 2500 miles or Priority patients further than 3900 from Ramstein Air Base.

  3. While this is the optimal choice, in this scenario, the system is incapable of reaching Urgent patients located further than 2500 miles from Ramstein Air Base.

  4. Civilian air ambulances go to South Africa if located more than 3900 miles from Ramstein.

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Authors and Affiliations

  1. Department of Operational Sciences, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH, USA

    Daniel A. Griffith, Bradley Boehmke, Benjamin T. Hazen & Alan W. Johnson

  2. Department of Marketing and Supply Chain Management, The University of Tennessee, 304 Stokely Management Center, 916 Volunteer Boulevard, Knoxville, TN, 37996-0560, USA

    Randy V. Bradley

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  1. Daniel A. Griffith
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  2. Bradley Boehmke
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  4. Benjamin T. Hazen
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Correspondence to Benjamin T. Hazen.

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Griffith, D.A., Boehmke, B., Bradley, R.V. et al. Embedded analytics: improving decision support for humanitarian logistics operations. Ann Oper Res 283, 247–265 (2019). https://doi.org/10.1007/s10479-017-2607-z

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