Call for Partners | RECOMMENDS — Predicting the impact of food improvements on household waste
Food quality naturally declines along the supply chain, contributing to loss and waste, with significant impact on people, planet and profit. A significant part of food waste occurs in households — where consumer decisions to discard food are influenced by a dynamic interplay of product attributes, personal habits, and environmental factors. While different preservation methods help reduce losses earlier in the chain, their real impact on household waste remains hard to measure and substantiate.
Partner up for impact
RECOMMENDS will generate data-driven insights into how consumers perceive product quality and why they decide to eat or discard food - opening up new communication strategies, marketing claims, and design interventions to reduce household food waste.
We are looking for
We invite companies and organizations committed to reducing food waste in their value chains and who want to better understand consumer perceptions and involved in:
- Ingredient supply
- Food manufacturing and product development
- Technology solutions (coatings, packaging, cooling, freezing etc).
Partners will contribute with a financial contribution and with relevant expertise and/or products, with opportunities to:
- Guide research directions and test specific products and preservation methods
- Gain exclusive early access to predictive models and consumer insights
- Possibility to exchange knowledge and experiences with other partners
About the project
RECOMMENDS will combine experimental consumer studies with advanced modeling techniques. We will use experimental methods to determine consumer perceptions of product quality, measure purchase/discard probabilities, and characterize the product journey from retail to household. This will be complemented with the use of modeling techniques to predict and compare household waste (and economic losses) in different scenarios (e.g., ingredients, packaging, freezing, post-harvest treatments) - including state-of-art knowledge-guided machine learning.
Let's connect
For more information about the project or to collaborate, please contact our expert.
SB (Sanne) Stroosnijder, MSc
Program manager Food Loss and Waste Prevention
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