Arla Foods, Basking Ridge, N.J., developed a new artificial intelligence (AI) tool designed to better predict milk intake from farmer-owners.
Every year, Arla collects around 13 billion kilos of milk from its 10,300 farmer-owners across Northern Europe. This AI tool will predict how much milk 1.5 million cows will produce in the future. The improved milk intake forecast means that 200 million kilos of milk can be utilized better each year.
“The better we are at predicting what our milk intake will be, the better we can plan and optimize our entire value chain, which both improves profitability for our farmer-owners and drives sustainability. The new AI tool provides us with an insight into our supply of milk that we have never had before,” says Michael Bøgh Linde Vinther, director of global milk planning.
By using AI technology, Arla is able to create the milk intake forecast from a richer data foundation. The tool includes things like seasonal changes, the number of farmers converting to new milk types, the farmers’ geographical characteristics and how much milk is produced on a daily basis.
“We are now able to make important strategic decisions on a more informed basis. The data has become more valid as it is now formalized in a bulletproof system rather than based on individual knowledge. It’s amazing to see how this new technology is able to optimize and improve a, up until now, very time-consuming task,” Linde Vinther says.
For example, it is now possible to make the distinction between how much milk should be collected from farmers in North Germany and West Germany 3-5 months ahead of time.
“This kind of knowledge is very valuable, as it makes us able to plan and adjust the number of Arla trucks travelling across the country. In that way, we can both reduce cost and save the environment for unnecessary CO2 emissions,” Linde Vinther adds.
The new milk intake forecasting tool is implemented in all Arla’s markets across Europe, including Denmark, Germany, Sweden, the UK, Belgium, Luxembourg and the Netherlands.
