BDAC and Humanitarian Relief Operations
With the rise of modernity, basic humanitarian disaster relief activities have given way to sophisticated applications of policy, technology, and methodology. “The Federal Emergency Management Agency (FEMA) can trace its beginnings to the Congressional Act of 1803. This Act generally considered the first piece of disaster legislation, provided assistance to a New Hampshire town following an extensive fire” (History of Federal Disaster Mitigation, 2005). In the century that followed, a staggering array of disaster relief organizations have grown in response to hurricanes, earthquakes, floods, and other disasters.
Disasters are naturally unpredictable; however, one constant is the need for a wide variety of organizations to come together quickly to integrate and coordinate their approaches. Towards this end, modern analytics have become essential, particularly when it comes to humanitarian relief supply chains. Dubey et al. (2018) explain that “coordination in humanitarian relief supply chains may appear horizontal or vertical” (p. 487) with government and military organizations preferring vertical coordination, while non-governmental organizations tend to prefer horizontal coordination. Meyerson et al. (1996) have coined the term “swift trust,” which is essential for bringing temporary teams together with a clear purpose and common task for a finite period of time.
Increasingly, big data analytics capability (BDAC) is being employed as an effective means to generate swift trust coordination and to span differences in culture, language, and organizational forms (Dubey et al., 2018), and has proven an effective means to collaborate and share information in complex environments. Prasad, Zakaria, & Altay (2018) explain that “humanitarian operations in developing world settings present a particularly rich opportunity for examining the use of big data analytics” (p. 383). A specific example can be found in the application of “the fuzzy Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique” (Venkatesh et al., 2019, p. 1537). This framework was designed for NGOs involved in continuous aid collection activities to help them select suitable supply partners. What are some other applications of BDAC to humanitarian relief operations?
References
Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B. T., & Douglas, M. A. (2018). Big data and predictive analytics in humanitarian supply chains. The International Journal of Logistics Management, 29(2), 485-512. doi:10.1108/IJLM-02-2017-0039
Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120-136.
History of federal disaster mitigation: Evolution of the federal emergency management agency. (2005). Congressional Digest, 84(9), 258.
Meyerson, D., Weick, K.E. and Kramer, R.M. (1996), Swift trust and temporary groups, in Kramer, R.M. and Tyler, T.R. (Eds), Trust in Organizations: Frontiers of Theory and Research, Sage Publications, CA, pp. 166-195.
Prasad, S., Zakaria, R., & Altay, N. (2018). Big data in humanitarian supply chain networks: A resource dependence perspective. Annals of Operations Research, 270(1-2), 383-413. doi:10.1007/s10479-016-2280-7