For us, eating has long since ceased to be just "taking in food" – it is a lifestyle, a philosophy, a sense of belonging. Accordingly, the topic of diet is very important in our society. Sadly, this does not change the fact that every year millions of tons of food are not consumed but thrown away.
This is also the case in Germany. The interesting thing is: approximately 11 million tonnes, or 60 percent of this food, is already destroyed during the production process in the value chain. The culprits are overproduction, which takes place due to false prediciton of future demand, and quality-related rejects, which occur because food is processed incorrectly.
The complexity of the food industry makes it difficult to prevent these abuses. But what if modern technology offered a solution?
This is exactly the approach you from Augsburg University of Applied Sciences are pursuing with your project: focusing on the dairy, meat and bakery value-added networks, where the greatest losses occur in Germany, you have developed a concept for how artificial intelligence (AI) can help prevent food waste.
The goal is to reduce overproduction and avoid waste at the same time. On the one hand, AI should improve future demand forecasting – not only within the company, but across the entire industry. In addition, the use of AI should make production less wasteful – for example, by improving system control.
Experts estimate that implementing the steps defined in your project will reduce food losses in the industry by about 2-5 percent – a significant result given the absolute amount of food destroyed!
For this initiative and farsightedness, dear team behind the REIF (Resource Efficient, Economic and Intelligent Foodchain) project, we are happy to award you the "Golden Like". And we look forward to seeing what else you achieve in this area with the help of AI!
Avoiding or at least reducing food waste has not only ethical implications but also economic and, most importantly in our times, ecological ones. With digitalisation, data analysis and even machine learning, not only can food products be produced better, but their demand can also be estimated more accurately. This leads to less waste during production and fewer goods that are not accepted. What is thrown away at the end of the – quite complex and long – chain does not have to be produced in the first place. All this saves resources, preserves the environment and, in the end, is also economical.
Dear Trivadis team, as the REIF consortium we would like to thank you very much for the encouragement in what we are doing and for the spotlight you put on our project. Stay tuned to REIF and pass on the results! Thank you very much!
Stefan Braunreuther, Professor at the Augsburg University of Applied Sciences
In our format "The Golden Like", we regularly honour individuals or institutions with a short eulogy who, in our eyes, have earned special praise for their achievements or where the appreciative thank you is far too often neglected.