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June 29, 2015
Humanitarian Supply Chain Analytics
By: Dinesh Natarajan & Jeff Keene

Ten years ago it was Hurricane Katrina. Five years ago it was the Haiti Earthquake. And this year, it is the Nepal Earthquake. Natural disasters are no longer anomalies, but rather a norm often associated with changing weather patterns and adverse human activities. In today’s world, where disasters seem to be striking humanity with great vengeance, the importance of humanitarian logistics and supply chain is indisputable.


Humanitarian logistics is a branch of logistics which specializes in organizing the delivery and warehousing of supplies during natural disasters or complex emergencies to the affected area and people. There are typically 4 phases in humanitarian logistics:

  1. Mitigation: Activities that reduce the chance of a disaster happening such as building dams and levees.

  2. Preparation: Activities to improve the ability to respond to a disaster such as strategically locating warehouses.

  3. Response: Activities immediately following a disaster such as transportation of supplies, scheduling volunteers etc.

  4. Recovery: Activities after the disaster, helping rebuild the community.

Supply Chain Analytics certainly finds its application in the Mitigation, Preparation and Response phases. However, advanced solutions in humanitarian supply chain are not as prominent as they are in the commercial supply chain. The main reason for this is the complicated nature of humanitarian logistics. In commercial supply chain, your key objective is to maximize your revenue or profits. However, in a humanitarian supply chain, your goal is far more complex than maximizing the revenue; it is ensuring you minimize people’s sufferings while also reducing the cost to serve. The table below highlights some of the major differences between the two supply chains:

 

Commercial Supply Chain

Humanitarian Supply Chain

Strategic Goals

Maximize profitability and customer satisfaction

Minimize the loss of life and reduce sufferings

Demand Pattern

Relatively predictable; demand occurs at fixed locations in set quantities

Demand is generated from random events and is very unpredictable

Lead Time

Lead Time varies from days to months depending on the type of industry

Lead Time varies from hours to days; zero Lead Time is essential

Information Systems

Typically well-defined and uses advanced technology

Information is often unreliable, incomplete or non-existent

Does this mean that it will be impossible to employ analytics in humanitarian supply chain? Absolutely not! In fact, the academic world is already helping humanitarian organizations solve their supply chain challenges. Here are some of the problems they are trying to solve:

  • Facility Location Problem - One of the most common problems in a humanitarian supply chain, just as in a commercial supply chain, is deciding where to locate distribution centers. However, the problem is multi-layered here. Initially, we have to determine a set of permanent locations for DCs that receive and store essential goods in the ‘Preparation’ phase. We also have to determine locations for temporary storage sites and point of distribution sites in the ‘Response’ phase after a disaster.

  • Inventory Positioning and Optimization – The more supplies, the better it is for the affected area. However, the distribution centers do not have infinite space to hold inventory. Inventory optimization will help identify the right amount of inventory levels at the right locations in the ‘Preparation’ phase. One of the main challenges in modeling supply chain problems in the ‘Preparation’ phase is the inability to estimate demand accurately. Demand is highly unpredictable in the humanitarian supply chain. The recent developments in predictive analytics can help in estimating the demand with greater accuracy.

  • Transportation and Vehicle Routing Problem - These problems occur during the ‘Response’ phase, when an organization has to deploy its goods to the affected area. Some of the questions that can be answered are, “what is the best mode of transport to deliver goods within a particular lead time?” or “what is the best route for a truck to follow to satisfy demand in the shortest time possible?” According to a senior logistics officer in Asia at United Nations World Food Program, the humanitarian supply chain activities for the Nepal disaster have been severely challenging because of the hilly terrains in Nepal. In such areas, analytics can certainly help solve this challenge by modeling geographical constraints in the vehicle routing problem.

Another opportunity is risk mitigation. In From Superstorms to Factory Fires, describes a method developed by David Simchi-Levi to manage unpredictable supply chain disruptions. It helps prioritize the financial impact of risk through the Risk Exposure Index (REI). This enables companies to focus their mitigation efforts on the most important suppliers and risk areas instead of ignoring them or using an exhaustive approach. This method was successfully applied at [Name Redacted].

The [Name Redacted] method is beginning to influence thinking beyond the corporate world as in a recent article published by the United Nations office of disaster risk reduction: Flood Risks and Impacts Future Research Questions and Implication to Private Investment Decision-Making for Supply Chain Networks.

This paper investigates the impact of disruptions on the global economy through supply chains, in order to propose what components should be considered to measure supply chain risk. In particular, it takes a deep look at Thailand’s 2011 flood since this was the most notable example of the impact of disruptions both on specific industries and the whole economy. Since the prolonged floods affected the primary industrial sectors in Thailand, the automotive and electronics industries, the impact on the whole economy was devastating.

While the focus here is on commercial impact the same analysis can be made to measure humanitarian impact and make the appropriate mitigation investments up front.


There are numerous possibilities when it comes to solving supply chain and operations problems using mathematical modeling and analytics. By leveraging solutions used in industry together with ‘big data’ and the ever-growing computing power, the humanitarian supply chain can certainly operate at greater efficiencies and thus help in saving many more lives in times of distress.


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