Asset supply networks in humanitarian operations: A combined empirical‐simulation approach

Abstract

International humanitarian organizations (IHOs) respond to mega disasters while maintaining development programs in the rest of the world (ROW). This means an IHO's asset supply network must perform the challenging task of supporting a fast disaster response while simultaneously maintaining cost‐effective ROW development programs. We study how supply network asset flows are impacted during a mega disaster response and find that resource fluidity, the capability to reallocate resources quickly, impacts both mega disaster and ROW program asset flows within these supply networks. Using data from a large IHO's response to a mega disaster and econometric models, we find a dependency between ROW asset flow and mega disaster asset flow in IHOs with resource fluidity. As mega disaster flow increases, there is a decrease in hub‐to‐hub ROW asset flows and an increase in other ROW asset flows. This is contrary to most humanitarian operations research, which typically assumes independent asset flows. Because of resource fluidity, the combination of these flows does not compromise ROW operations. We use these empirical results to feed a simulation analysis that extends our research to IHOs without resource fluidity and provides actionable insights for varying types of IHOs in various demand scenarios. Simulation insights illustrate that resource fluidity impacts IHO asset supply network costs and optimal configurations.

Avoiding epistemological silos and empirical elephants in OM: How to combine empirical and simulation methods?

“…A simulation can help overcome endogeneity concerns by incorporating omitted variables that were not in the data used for theory testing. Stauffer et al (2018) in their paper entitled Asset Supply Networks in Humanitarian Operations: A Combined Empirical‐Simulation Approach in this issue use Monte Carlo simulations to include omitted variables in their analyses of how changes in resource flows affect the optimal supply network of an international health organization, and its performance in the aftermath of disaster. Simulation can also help to probe further into the direction of causal relationships and help overcome reverse causality issues.…”

The implications of complexity for humanitarian logistics: a complex adaptive systems perspective

“…Inspired by this, Day (2014) concluded that the differences in the flows of finance, information, and resources are so great that HL efforts cannot be managed in the same manner as unidirectional, linear SCs, but should instead be examined as dynamic and complex systems to better understand HL and to 'generate more reliable disaster relief performance' (Day 2014(Day , p. 1985. Other scholars have characterised HL as a highly complex (Seybolt 2009;L'Hermitte et al 2015;Stauffer et al 2018) or 'wicked' problem (Tatham and Houghton 2011). However, definitions of the actors, processes and contexts that comprise the dynamic and complex system of HL are under-developed in the literature.…”

Peer Review #2 of "A survey on exponential random graph models: an application perspective (v0.1)"

“…This problem has been formulated in the form of asset supply networks. (Stauffer et al, 2018) used ERGMs as an empirical model to understand the asset flows during a crisis. The applications of ERGMs have even been extended to the analysis of online drug distribution networks.…”