The approach of the Zero Hunger Lab
Tilburg University has been working on optimizing the supply chain within emergency aid for a number of years now. In addition to optimizing this emergency aid, the Zero Hunger Lab also wants to use data science for more efficient and effective aid during rehabilitation and to help regions to become self-sufficient and resilient in the long term.
Partners are essential
The Zero Hunger Lab works with aid organizations that help to deliver relief aid, with rebuilding, or to provide support to farmers, businesses and governments locally. Partners each have their own specific questions that the Zero Hunger Lab can answer.
For example, an emergency aid partner wants to know how best to acquire, transport and distribute food. While another partner working on development in another region, for example, wants to know how to improve irrigation to rebuild local agriculture capacity. Moreover, when it comes to providing help to improve the food supply in the long term, other questions arise. The Zero Hunger Lab uses data science to answer all these questions in the best possible way.
How the Zero Hunger Lab Works
The Zero Hunger Lab combines data and research to design new food systems. Agricultural research provides the input for production data, while mathematical optimization can provide a better supply chain and infrastructure.
A mathematical model calculates, on the basis of available data and smart formulas (algorithms), the best way to organize food aid for a certain area. Such a model implicitly assesses millions of scenarios: more than our human brain can contain.
Tilburg University has been working on optimizing the supply chain within food aid for a number of years now; with results. The Optimus optimization model was designed in collaboration with external PhD candidate Koen Peters and the World Food Programme. The Zero Hunger Lab not only optimizes emergency aid but also aid during rehabilitation and for long-term food supply.
Koen Peeters over het optimalisatiemodel Optimus
The difference between life and death
" On a daily basis, the World Food Programme takes up the challenge of making decisions based on large amounts of data; decisions that often mean the difference between life and death. Optimus is a game changer for the WFP."
It is a good example and reminder of how data science can have a significant impact on humanitarian aid. Optimus has already made a big impact. That's why the project won the DCHI Jury Award for Best Humanitarian Innovation 2018.”
Roza Freriks, innovation manager DCHI (Dutch Coalition for Humanitarian Innovation)
How does the Zero Hunger Lab do that? For example, it looks at the influence of uncertainty. Uncertainties about prices, quantity of food, capacity, and conditions. But also factors such as the use of locally produced food, the reduction of CO2 emissions, and influencing political choices. In addition, the Lab aims to make models more accurate: from monthly decisions, to weekly or even daily ones. In this way, better choices can be made at tactical, operational, and strategic levels.
The most important innovations we will be working on in the coming years are
- Optimizing the supply chain from supplier to user.
- Exploring the possibilities when we turn the question around: what happens when we optimize the supply chain from food basket to production?
- Making maximum use of local economies.
The local economy
In order to solve hunger, disease, and poverty, a healthy ecosystem is needed within a country. A system based on equality. Now, local farmers, logistics service providers and governments only form a small part of the solution to the world hunger problem.
The Zero Hunger Lab project aims to support local economies and help them to develop sustainably. The region will be in charge of this. They can improve their production, resilience, and profits thanks to guidance, collaboration, and support. The Zero Hunger Lab helps experts and their organizations to eradicate hunger by providing research-based solutions.
" Hunger is a major global problem. I appreciate the fact that I’m contributing to solving it. In my opinion, Data Science and Operations Research can make a huge contribution to Zero Hunger. This is also reflected in my research into how best to deal with the uncertainty of the prices of local goods in a food aid operation."
Robert Poos, MSc student Business Analytics and Operations Research
From research to practice
The Zero Hunger Lab develops models for issues concerning emergency aid, reconstruction, and chronic food supply. The issues differ in structure and, therefore, also in solution. The Zero Hunger Lab supports partners in optimizing the help they offer. The research is carried out based on the questions of partners. So that these partners can then put the results into practice and thus make a difference.
Working together to make an impact
In the Zero Hunger Lab, Tilburg University works on the development of supply chain solutions. We cannot make impact on our own. Data is needed to feed our models, and we need partners to put the solutions into practice. The Zero Hunger Lab is therefore looking for partners for collaboration and co-creation. Please contact us for an interview. Who are involved in the Tilburg University Zero Hunger Lab? How is the Zero Hunger Lab organized?
Working on research. What does the research agenda look like?
1. Generic aspects of hunger
- What factors determine hunger?
- How do we measure hunger?
2. Solutions for the development of countries/regions
- The supply side: e.g., which crops should be grown where?
- The demand side: how much food is actually needed?
- The infrastructure side: where should we store food, how are the transports organized?
- The investment side: should we invest in irrigation or in loans to farmers?
3. Emergency relief solutions
- How can we support WFP even better?
- What is best: supply money or food?
- What are the best models for very specific situations; e.g., after a flood or earthquake.
- Where should food stocks be strategically located for emergency relief?
4. Development of models
- that can deal with uncertainty,
- that can display and analyze even larger areas.
- which results are actually used?
If you are interested in finding out what contribution you can make or if you already know that you would like to become our partner or colleague, let’s meet and talk.