Machine learning – such a powerful and popular term that has taken over the software development industry, especially data-driven software solutions. It’s a method of data analysis that is based on the concept that systems can learn from data, identify patterns, and forecast future movements. For many companies that either generate or collect vast amounts of data, machine learning can provide a window into the future on how will certain parts of the market behave.
The applicability of machine learning can range from simple predictions to more complex ones. It can be used in different industries and markets, depending on the objective. Our experience with it is that even though getting precise predictions can be hard and sometimes daunting, it also allows for more possibilities to maximize data utilization.
Machine learning applications in the manufacturing industry
Even though our use cases have been used in farming, their concept and machine learning methods can be applied across various industries. No matter the commodity or product, the basic principle stays the same, and what we know and what we’ve learned can only help in making the next model more accurate and better performing.
Two of the main solutions where machine learning was applied are input and output forecasting and those solutions are adaptable to any industry and production. Those two solutions predict future outcomes on input or output quantity, quality, and results.
But, what are some cases where your output is dependent on changeable and fluctuating factors? In farming, you as a producer are counting on your animals’ best performance and you have to be on top of the game to keep those animals in good condition.
In the commodities industry, like milk, cheese, and other dairy products, you are heavily influenced by cows and their state on the farm. The quantity and quality of dairy products, mainly milk, are the result of the cow’s life cycle and productive phase. A producer’s output is defined by the number of animals on that farm and how many of them can provide satisfactory results.
To make the future a bit more stable and adaptable to farm and market changes, some of the cases regarding machine learning were used to predict how animals will behave. It was important to have a model that could give an overview of animals’ performance.
Not many are familiar with each factor that could influence milk production and cows. That’s why our machine learning models were focused on doing just that – predicting factors that are a part of a cow’s productivity.
Can you predict declines in animal products?
Our first model was applied in the breeding analysis. In times of breeding, a cow cannot provide milk that will be used in future production. So, it was important to determine when a certain cow will not be available, so farmers can adjust expectations and planning on milk quantities. Based on the historical data of past breeding and breeding processes, it can be predicted when the animal will not give milk. It also helps predict when a cow will be in the breeding phase, so farmers can anticipate and plan for the birth of calves. They can also plan when is the best time for breeding if the goal is to grow the number of animals on the farm.
The next model covers potential diseases in cows. If an animal is sick or has some health issues, it will not be able to provide results. Predicting such occurrences is beneficial for farmers so they can react on time. For example, mastitis occurrence in cows will affect milk production. If farmers can predict future occurrences on time, they can react quickly regarding cow’s health and milk quality. Based on historical data on disease and data on cow’s life cycle, breed, and such can help build models to anticipate mastitis.
Machine learning can also be applied to track an animal’s life cycle. It can predict survival by the season of birth or even anticipate the length of its life. If you track data on all influences on animals and their personal information on health, breed, or productivity, you can build models that can forecast when a certain animal’s life or health is in decline.
Machine learning applicability can be seen through the whole life cycle of an animal. You can use data collected through their life, to help you understand how it will behave in the future and what is their productivity, meaning how much product, or in our case milk, they can provide.
Why machine learning?
Such models help minimize uncertainty and allow for quicker reactions that will help stabilize and optimize production. They provide insights and metrics that give an overview of the whole production and how will it operate in the future. Machine learning helps create scenarios where users can spot potential problems, errors, and disruptions. It’s a tool whose goal is to help prepare the user for the future.
The usage of ML is on the rise and it will continue so. The possibilities it offers are endless, and it can utilize data to your biggest advantage. In a world that revolves around information, the next step is to use it to predict future outcomes. Any company that devels into such endeavors will certainly benefit in long term, especially in establishing its competitive advantage.
