Digital Innovation for Humanitarian Logistics
To help aid organizations better plan their responses to humanitarian crises, researchers and graduate students work in collaboration with them to develop innovative decision-making tools that enable robust, cost-efficient, and timely relief solutions.
Call to Action
In April 2015, a magnitude 7.8 earthquake hit Nepal, killing nearly 9,000 people and leaving roughly 4 million people in need of humanitarian assistance. In September 2017, Category 5 hurricanes Irma and Maria caused more than 3,000 fatalities and catastrophic damage in the Caribbean. Eastern Africa currently faces severe humanitarian crises caused by conflicts, poverty, and recurrent droughts leading to chronic food insecurity problems. Earthquakes, hurricanes, and other sudden-onset disasters are difficult to predict. When these types of disasters occur, timely actions and management plans can significantly mitigate damages and losses. For prolonged humanitarian crises, such as chronic food insecurity, poor infrastructure and weak supply chains often hinder efficiency.
In all these situations, the lack of reliable information management processes and collaborative preparedness initiatives can compromise humanitarian relief initiatives. Proper data gathering and processing, from satellite imaging to social network information, can improve situational awareness of authorities and enhance evidence-based decision-making. In addition, collaborative decision-making and coordination of relief efforts can maximize the efficiency of humanitarian action, all with the goal of reducing casualties and fatalities resulting from such crises.
The Digital Innovation for Humanitarian Logistics research program, led by professor Marie-Éve Rancourt in collaboration with several researchers and graduate students from HEC Montréal and other institutions, enable the development and testing of an innovative methodological framework based on data analytics and optimization to support humanitarian operations. This framework can be used to inform preparedness, response, or recovery strategies and policies in order to create cost-efficient, robust, and timely relief responses.
Innovation stems from a methodological framework to process data gathered from several sources and integrate these into a decision-support tool, which uses analytics techniques. The objective is to model and solve complex logistics problems to support and provide solutions for humanitarian efforts. This framework has been successfully applied to three humanitarian settings so far.
First, in collaboration with the Caribbean Disaster and Emergency Management Agency, a collaborative pre-positioning network was created to strengthen the disaster preparedness of Caribbean countries. A novel insurance-based method to allocate costs was integrated to a stochastic programming model, which determines the locations and amounts of relief supplies to store, as well as the investment to be made.
Second, in the aftermath of the 2015 Nepal earthquake, 1.1 million people were left without access to drinkable water. A recovery plan was established by the Red Cross, but designing a water supply system in remote mountainous areas was problematic. An optimization tool to locate community water taps and design a network of pipelines was developed. The solution minimized the number of water taps and connection costs, while considering several constraints and the geography of the area.
Third, in collaboration with the United Nations Humanitarian Response Depot (UNHRD), an analytical framework was developed to assess the performance of a new network structure to support the supply chain of the humanitarian community in East Africa. The UNHRD, a major logistics services provider in the region, achieved a reduction of its operational costs to maximize aid delivery to its partners.
This methodological framework was mobilized to create innovative strategies for managing emergency assistance and development efforts. The framework has helped solve difficult collaborative planning problems efficiently, relief infrastructure building, and difficulties in providing timely humanitarian aid.
For the Caribbean network, the first impact was the designing of software that allows information gathering on all partner countries’ logistics and transportation resources. Using statistical analysis and optimization methods, this software accomplishes several key objectives:
- Reduces the cost and response time of the collaborative network
- Identifies the best locations for warehouses and the amount of stocks to be stored in each
- Lowers the investment required by each country for emergency supplies
- Predicts the amount of supplies to be shipped to each country by each transportation mode
For the Nepalese initiative, innovative integration of field data provided by the Red Cross and satellite imagery allowed for an infrastructure-building problem to be modeled mathematically. Using this model, a solution was calculated that reduced the cost of the water taps network and the distances to be traveled by villagers from their home to water taps.
In East Africa, fieldwork data, simulation, optimization, and statistical analyses were used to assess the benefits of adding a distribution center in Uganda. A robust stocking solution for pre-positioning relief items in Kampala, Uganda, and improving the regional humanitarian distribution network efficiency was created. The UNHRD has already implemented this solution, which should result in cost reduction of around 21 percent.
Caribbean Disaster Emergency Management Agency; International Federation of Red Cross and Red Crescent Societies; Austria Red Cross; The World Food Program (WFP); The United Nations Humanitarian Response Depot (UNHRD); The Institute for Data Valorisation (IVADO); Salzburg University