Production of raw materials or inputs where you are dependent on many internal and external influences can be unpredictable. Especially if you are in the production of perishable goods. Having a solution that could predict outcomes, such as the quantity and quality of your outputs, can be beneficial for you as the producer and for the next person in the supply chain, the buyer.
Our goal was very simple. We needed to create a solution that will forecast the quantity and quality of milk. A solution that can be used across a greater number of farms.
For that, we needed to understand what drives milk production. We discovered that milk production is dependent on the animal and its life cycle, feed intake, and external influences such as weather. Cows are nowadays milked by milking machines and sensors that measure how long is the milking time, how much milk could you get from one animal, the quality of the milk, and such. Based on that we generated data for each animal and how much milk will it produce.
We attributed numbers to each animal and recorded historical data based on their lactation periods. This could lead to determining an animal’s production cycle and what to expect from a certain animal in the future.
A machine learning forecasting solution was applied to determine how much milk could be produced, how much could be sold, and what expected trends could be. This is valuable for both the producer and the processor since these solutions could level the supply and demand throughout certain periods.
Every herd is different and it will give different results and show different discrepancies and anomalies. Different farms hold different breeds, they prolong lactation periods or breed cows at different rates. Each situation affects the lactation period and how much milk could a certain cow produce. Various external influences that producers can’t influence, such as diseases and cow death, also affect the milk quantity. Each factor needs to be used in the solution to reach maximum efficiency and accuracy of predictions. Using daily data (daily milk production) in the solution leads to more accuracy and a more stable model.
Bigger quantities of data and a solution that is continuously learning (with the usage of machine learning), lead to trends overview, forecasts, and data visualizations on which producers can base their strategic decisions and reach a certain level of security.
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