EURO 2024 Copenhagen
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2447. The Value of Demand Prediction for Improved Food Security

Invited abstract in session TB-21: Demand Forecasting in Humanitarian Operations, stream OR in Humanitarian Operations (HOpe).

Tuesday, 10:30-12:00
Room: 49 (building: 116)

Authors (first author is the speaker)

1. Alborz Hassanzadeh
NEOMA Business School
2. Valérie Bélanger
Logistics and Operations management, HEC Montréal
3. Marie-Ève Rancourt
HEC Montreal
4. Feyza Sahinyazan
Technology & Operations Management, Simon Fraser University

Abstract

Hunger and famine pose great risks to global health and are the second in the UN's seventeen sustainable development goals to be achieved by 2030; however, they prove to be quite challenging to eradicate or alleviate. To mitigate their devastating impact, each year, aid agencies deliver tons of food commodities to populations in need. However, the delivery of food commodities is often expensive, and because of the complex intertwining factors shaping food security, it is very difficult to definitively predict future outcomes and demand for food aid. Without a timely identification of vulnerable populations, food aid often fails to arrive in the right place at the right time. We develop a stochastic optimization framework to assess the value of information: our analytical results quantify the advantages of incorporating food insecurity predictions in decision-making. Such predictions facilitate informed prepositioning decisions. Since acquiring relevant data might require heavy investments, we also analyze the delicate balance between allocating the limited available budget to the prepositioning of food commodities and investing in the acquisition of accurate data.

Keywords

Status: accepted

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