Input intake forecasting solution
Forecasting inputs intake to stay on track with costs and production planning
How we developed a solution that measures and forecasts input intake in your production, so you have a clear overview of future spending and outputs.
Project scope
Project goal:
- The project goal was to predict input spending a few days ahead and reduce waste and costs.
Main issues:
- To use historical and current data with machine learning to predict trends and outcomes in output quantity
- To create a fully automated, self-evolving cloud solution
- To develop a system that learns through daily data intake and becomes better at predicting
Our approach:
- Making sure that the necessary data is in place
- re-engineering of parts of the existing data pipeline is made in order to make necessary data available
- Understanding the data and making new discoveries
- Discovering what does a certain piece of data represent, where and how to extract information for predictions, why are there anomalies in the process
- Giving attributes to certain information subjects
- Creating a tailor-made data prediction solution in accordance with the uniqueness and characteristics of the data source (subject) – every business has a different process (execution time, usage period of resources, etc.)
- Making adjustments according to errors, missing information, bad data, or non-quality data
- Unlocking the possibilities of a self-learning system as new data is added daily
- Building in systems for continuous performance tracking
Solution:
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.
What we did:
A machine learning forecasting solution was applied to determine how much milk can be produced, how much could be sold, and what trends could be expected. 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.
Solution:
- 10% better predictions compared to the baseline
- The ability of predictions accuracy to increase based on continuous information intake and machine learning
- Fully automated and self-evolving cloud solution
- Accurate forecasting that enables faster decision making, regulates costs and improves production management
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Croatia
Zadarska 77, Zagreb
9494949 49494949
info@digitalpoirots.com