Dairy Lab Forecasting System
The Application Domain
The dairy industry plays a crucial role in global
food systems, providing essential nutrition while
facing increasing pressure to operate in a more
sustainable way. This goes hand in hand with
pressure on milk price and increased costs for
reporting on sustainability parameters. An answer is
to optimize processes and production plans in the
dairy cooperative, making use of data-driven
decision-making.
In this context, dairy cooperatives have a strong interest in accurate forecasts of milk quantity and quality. Reliable predictions enable better planning of operations across business units, logistics, and processing facilities. By aligning supply with processing capacity and consumers’ demand, cooperatives can reduce waste, optimize resource use, and improve overall efficiency. Enhanced forecasting capabilities support not only economic performance but also sustainability goals, by minimizing losses, reducing energy consumption, and enabling more resilient and transparent dairy value chains.
The Challenge
Forecasting milk quantity and quality is inherently
complex, as both are influenced by a wide range of
factors across multiple domains. These include feed
composition and feed harvest quality, milk prices,
animal health and diseases, meteorological
conditions, and individual farm management
practices. The heterogeneity and variability of
these drivers make it difficult to establish robust
and scalable forecasting approaches. A key question
is not only which parameters to use for forecasts,
but also over which time horizon these parameters
influence the targeted milk forecast parameters and
which precision can be achieved.
To address this challenge, a comprehensive deep-dive analysis was conducted. The findings indicate that forecasting milk parameters at a regional level is feasible when combining meteorological datasets - such as ERA5 reanalysis and ECMWF forecasts - with historical data of milk quality and quantity. This approach reveals relevant patterns and enables reliable short-term forecasts covering a forecast horizon of 15 days.
Key results
A scalable forecasting system was successfully
implemented to support dairy cooperatives in
data-driven decision-making. The system enables near
real-time collection of both dairy and
meteorological data, which are processed and
aggregated into consistent regional time series.
By leveraging AI, through the application of machine learning models with automated model selection, robust performance over time is ensured, allowing adaptation to changing data patterns while maintaining forecasting accuracy.
The system further provides user-friendly access through an interactive dashboard and an API (Application Programming Interface), allowing test and validation by end-users as well as seamless integration into existing workflows and operational processes. In the remaining project phase, the focus will be on evaluating and refining practical use cases of these forecasts, aiming to maximize their value for planning, optimization, and sustainable operation across the dairy value chain.
