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Code: EDI-2020-4-AN

Domain: Agri-Food

Summary

To improve the prediction of the ingredients that will be received for the production of animal feed.

Proposed by

 

Grupo AN is a centenary cooperative and a leader in the Spanish agri-food sector. Grupo AN is a second-degree cooperative formed by 160 agricultural cooperatives and 40,000 farmers and livestock owners.
Grupo AN is the largest producer of cereal in Spain. Mainly producers of agricultural products, including fruits and vegetables of worldwide commercial interest, offer the products to consumers in all possible formats: fresh, refrigerated and canned.
Grupo AN is also one of the largest operators on the poultry market and an indisputable leader in the supplier market. Grupo AN commercializes fertilizers and fuel, which is distributed in Spain through a network of petrol stations, which are opened to the public.
Apart from that Grupo AN also has a network of Processing companies. Some of them are the Industry of Vegetable preserves and the IV & V range processing company.

Description

The challenge is to improve the prediction of the ingredients that will be received for the production of animal feed.
For the production of animal feed, formulations are made at the beginning of each month.
In order to make the right formulas, it is necessary to know what characteristics the cereals will have. For example, in the case of wheat it is necessary to know characteristics such as: protein, moisture, fiber…
These data are unknown. Currently, a forecast is made based on previous data and taking into account the place of origin of the ingredients. In addition, it is also necessary to take into account the time of year and how the weather conditions have been.
Therefore, a supporting tool to improve the predictions of the characteristics of the ingredients would be very useful.

Data

The challenge has the following sample datasets available for download

Expected outcomes

Improve the accuracy of predictions.

As explained above, a tool to support predictions is needed. There is no specific % to achieve.

Currently, if the predictions are not correct, the nutritional quality of the feed production decreases.

Therefore, the closer to reality, the better final quality of products.

The expected result is to be able to know with great accuracy how the cereal that will be received will be.

Some of the parameters are: humidity, specific weight, protein, fiber, fat …

How do we apply?

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