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3 ways you can apply machine learning in your factory

According to IBM, “machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.” While the road to implementing machine learning (ML) in factories used to be much further, the gap has narrowed since the intense digital transformation of 2020 and 2021.

Today, as operators rush to ensure that the factory remains productive even when workers have to work remotely, more factories have incorporated sensors and data analytics tools to track machine data in real-time. These tools form the foundation of building an ML architecture in the factory.

As we work towards a smart factory future, here are 3 ways you can apply machine learning in your factory.

1. Improve efficiency and reduce costs: ML can be used to optimise factory operations. ML systems can learn routine processes and help in automating and optimising them, thereby ensuring manufacturers save on costs even without constant human monitoring.

2. Predicting demand and supply and reducing waste: Overproduction is one of the key sunk costs factory owners have to bear. By leveraging ML technology, factories can adjust their output to market demands, and reduce the amount of wastage at the end of the day.

3. Quality control: An ML system can be designed to accurately identify faulty products. This eliminates human error from manual inspection and ensures that the manufacturers are accountable to their consumers with a high standard of quality control.

ML technologies will shape the future of factories. If you’d like to explore smart technologies for your factory, simply contact us at to learn more.

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