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

• Author: Lu (Lucy) Yan,Alfonso J. Pedraza‐Martinez,Luk N. Van Wassenhove,Jon M. Stauffer
• DOI: http://doi.org/10.1016/j.jom.2018.07.002
• Date: 01 November 2018
• Published date: 01 November 2018

Contents lists available at ScienceDirect

Journal of Operations Management

journal homepage: www.elsevier.com/locate/jom

Asset supply networks in humanitarian operations: A combined empirical-

simulation approach

Jon M. Stauffer

a,∗

, Alfonso J. Pedraza-Martinez

b

, Lu (Lucy) Yan

b

, Luk N. Van Wassenhove

c

a

Mays Business School, Texas A&M University, 4217 TAMU, College Station, TX 77843, USA

b

Kelley School of Business, Indiana University, 1309 E 10th St, Bloomington, IN 47405, USA

c

Technology and Operations Management, INSEAD, Fontainebleau, France

ARTICLE INFO

Keywords:

Humanitarian operations

Simulation

Network analysis

Exponential random graph models

Resource fluidity

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 chal-

lenging task of supporting a fast disaster response while simultaneously maintaining cost-effective ROW de-

velopment 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.

1. Introduction

The January 2010 Haiti earthquake was a very large (mega) disaster

that impacted the supply networks of many international humanitarian

organizations (IHOs) for years afterward. For example, some IHOs

created a temporary hub in Haiti to facilitate the flow of relief assets

supporting the mega disaster response. While asset shipments (flow) to

support the Haiti mega disaster response increased dramatically, IHOs

continued to support long-term development programs and minor dis-

asters in the rest of the world (ROW). This challenge of simultaneously

maintaining ROW development programs while supporting a mega

disaster response motivates our research.

We explore this simultaneous asset flow problem by considering the

supply networks for secondary support assets, which are often over-

looked in literature, but are critical for the last mile distribution of aid.

Secondary support assets include 4WD vehicles, light trucks, and power

generators, which support humanitarian operations and allow the dis-

tribution of primary demand items such as food, water, and medicine.

While primary demand items are typically consumed at their first de-

ployment location, secondary support assets are reused for multiple

operations in different countries over a long period of time. Secondary

support assets also require extensive preventive maintenance and re-

pairs and are more costly on a per unit basis (Stauffer et al., 2016).

Hereafter, we refer to secondary support assets simply as assets.

An IHO with resource fluidity can more freely move and maintain

these assets around the globe to support both mega disaster response

and ROW development programs. Resource fluidity is characterized by

the availability of slack resources, standardized processes and assets,

which are owned centrally and used locally, and a mobile staffthat can

quickly refocus their attention on new projects and locations (Doz and

Kosonen, 2008a;2008b). To facilitate resource fluidity in their supply

networks, several IHOs created global leasing programs, which manage

and distribute assets while exhibiting key aspects of resource fluidity.

The global leasing programs are supported by hubs around the world.

The more hubs the global leasing program manages, the more decen-

tralized the corresponding asset supply network becomes. At these

https://doi.org/10.1016/j.jom.2018.07.002

Received 5 April 2017; Received in revised form 29 June 2018; Accepted 5 July 2018

∗

Corresponding author.

E-mail addresses: jstauffer@mays.tamu.edu (J.M. Stauffer), alpedraz@indiana.edu (A.J. Pedraza-Martinez), yanlucy@indiana.edu (L.L. Yan),

luk.van-wassenhove@insead.edu (L.N. Van Wassenhove).

Journal of Operations Management 63 (2018) 44–58

Available online 09 August 2018

0272-6963/ © 2018 Elsevier B.V. All rights reserved.

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