Building a Better Approach to Disaster Recovery


In the aftermath of a natural disaster such as a hurricane, the speedy replenishment of housing stock that was damaged or destroyed in the storm is critical to the recovery of stricken communities. A model being developed by the Zaragoza Logistics Center takes a novel approach to the rebuilding of storm-ravaged neighborhoods by framing the process as a supply chain challenge.

Supply chains are basically systems that source raw materials and components, transform them into finished products, and deliver these products to end customers. This is analogous to the way post-storm recovery programs operate. Hurricane victims who have lost their homes are “customers” who urgently need finished products in the form of housing. Also, a supply chain relies on the efficient flow of materials, information, and finance to achieve its operational goals, and the recovery phase of a disaster is subject to similar flows.

Although these parallels might appear obvious, research on humanitarian supply chains tends to focus on immediate responses rather than long-term recovery. Yet communities often struggle to replace the infrastructure lost during a severe storm, mainly because they are ill-prepared for such disasters.

Take, for example, the devastation caused by Hurricane Sandy that slammed into the US East Coast in 2013, inflicting huge damage in the densely populated New York/New Jersey region. “Cities were not prepared, and problems such as rezoning restrictions and unwieldy regulations frameworks caused bottlenecks that delayed the construction of new housing for years after the hurricane hit,” says Rafael Díaz, Professor of Supply Chain Management, MIT-Zaragoza International Logistics Program, Zaragoza Logistics Center, Zaragoza, Spain. He is developing the new recovery model in collaboration with Joshua G. Behr, Research Associate Professor, Old Dominion University, Norfolk, VA, U.S.

Geographies that are prone to extreme weather events need to have plans in place that enable communities to avoid such bottlenecks and expedite the recovery process when disaster strikes.

An example is the U.S. Hampton Roads region near the mouth of the Chesapeake Bay that includes two large cities, Norfolk and Portsmouth. This coastal urban area is experiencing more frequent flooding that stems, in part, from land subsidence and rising sea levels. Local communities are at risk from catastrophic housing losses caused by severe storms.

Díaz is demonstrating how supply chain management methods can be used to make the region more resilient using his recovery model. This involves the development of plans that anticipate levels of destruction and evaluate the resources/processes required to re-house displaced persons in the event of a disaster.

“We use what-if scenarios to analyze the dynamics of displaced populations, and what is needed to enhance new housing production and repair capacity in order to speed-up the return to normalcy,” explains Díaz.

The potential of this approach was demonstrated in a real-world crisis. The St. Bernard Project, a charity that supports disaster victims, achieved a dramatic reduction in the time taken to repair housing damaged by Hurricane Katrina, the storm that devastated parts of the U.S. Gulf Coast in 2005. The non-profit applied lean manufacturing principles to the management of volunteers and materials, and reduced repair times from an expected 120 days to 66 days.

Díaz wants to “simulate the application of these principles on a much larger scale, but grounded in initiatives such as the project in New Orleans”

Three information streams underpin his model:

  • The vulnerability of households and neighborhoods in terms of their social and financial profiles, and available resources.
  • The capacity to repair and replace damaged or destroyed housing with different types of structures. A Discrete-Event simulation approach is used to replicate supply chain interactions as well as regional capacity for recovery over time.
  • The likelihood and potential magnitude of storm occurrences consistent with relevant risk assessments. The researchers employ HAZUS, a tool developed by FEMA (Emergency Management Agency) in the U.S. to estimate the severity of storms.

It is important that the model reflects actual and prospective staffing and resource levels in the region being studied, and how these might change in a crisis situation. As Díaz points out, the impact of a storm is usually very uneven across the strike zone, owing to variations in the hurricane’s strength and track.

To make sure that the model is firmly rooted in reality, the researchers use a three-pronged approach to model building. First, the local functional relationships and housing stock replenishment dynamics are mapped through interactions with stakeholders and observations. Second, a literature search and further discussions provide more detailed information. Lastly, more data is gathered via larger, more formal iterative group model building events or guided conversations.

The model should be complete in about a year, says Díaz. Ultimately, he would like to extrapolate it to other disaster situations such as earthquakes, as well to regions in different parts of the world. “Applying the model, in, say Africa or Europe, will involve other factors, for instance there will be different materials flow constraints, but the basic supply chain principles are the same,” he says.