Embedded analytics: improving decision support for humanitarian logistics operations

• Published in: Annals of operations research Vol. 283; no. 1-2; pp. 247 - 265
• Main Authors: Griffith, Daniel A., Boehmke, Bradley, Bradley, Randy V., Hazen, Benjamin T., Johnson, Alan W.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2019; Springer; Springer Nature B.V
• Subjects: Applications of OR in Disaster Relief Operations; Business and Management; Combinatorics; Complexity; Data science; Decision analysis; Decision support systems; Humanitarian aid; Humanitarianism; Imagery; Logistics; Management; Mathematical analysis; Operations research; Operations Research/Decision Theory; Source code; Technology application; Theory of Computation; United States; Data science; Humanitarian logistics; Big data analytics; Decision tree; Business analytics; Embedded analytics
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-017-2607-z

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.