How Manufacturing Can Benefit From Machine Learning

By  //  January 28, 2021

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Artificial intelligence leads to tremendous advancements, not just in data science and the tech industry, but across all industries. Image recognition, data analytics, complex event processing, and digital assistants are just some of the everyday advantages AI has brought to the table.

Whether they’re startups or large organizations alike, odds are that a business in the U.S. is going to benefit from AI in some form or another.

Machine Learning Explained

It’s the concept of machine learning (ML) that powers AI advancements today. Machine learning platforms are able to use complex computer algorithms to find patterns in data without the explicit need for human intervention or programming. These algorithms are divided into four basic categories.

■ Supervised Learning: In this model, machines are shown examples of tasks through labeled datasets and can then apply what they’ve “learned” to new situations.

■ Unsupervised Learning: Machines ingest unlabeled data and draw inferences or establish structures based on it.

■ Semi-supervised Learning: A combination of the previous two methods that typically relies more on unlabeled data. It’s believed this approach can improve the accuracy of learning under certain circumstances.

■ Reinforcement Learning: Machines uncover errors and rewards by exploring data repeatedly.

While machine learning may sound like the stuff of science fiction, many industries already make great use of it. The manufacturing industry, which is comprised of repetitive and often unsafe tasks, has been an excellent candidate.

In order to find the best vendors for ML solutions, take a look at the Gartner Magic Quadrant Data Science and Machine Learning report.

This is a research publication compiled by Peter Krensky, Pieter den Hamer, Erick Brethenoux, Jim Hare, Carlie Idoine, Alexander Linden, Svetlana Sicular, and Farhan Choudhary. Here are just some of the areas in which machine learning platforms have been improving the manufacturing industry.

Image Recognition

Machine vision systems are incredibly popular on assembly lines since they can effectively replace, and even improve upon, the human eye on the production floor. Image recognition is great for the rapid inspection of assembled products at the end of the line. The machine is able to learn what a completed product should look like and inspect all its parts in real-time. If it sees anything unusual, it can flag it in the system immediately.

These image systems use a combination of cameras and machine learning algorithms to observe and make decisions. Beyond assembly, these vision systems can also look out for unsafe conditions on the floor and send alerts.

Data Processing

As with any business, data is a valuable resource for manufacturing companies, and ML data processing and analytics tools are key to extracting that value. With real-time data processing, organizations can be sure that the data they’re receiving is accurate and current, so they can make informed decisions. With the most accurate data about their process efficiency and real-time information constantly funneling from their production lines, manufacturing companies can come up with ways to improve their productivity and bottom lines.

It goes beyond the assembly lines, too. By combining data analytics with the Internet of Things, manufacturers can better track their inventory, fleets, and other assets for improved supply chain management.

Product Development

Machine learning solutions are able to gather and analyze business intelligence like consumer data far faster than manual research methods, or even many research information systems.

This helps manufacturers learn which of their products are selling the most and which ones could use an extra dash of marketing or be cut altogether. Accurate and current customer data also helps to eliminate risks when developing new products since companies will have even greater insights into their target audiences.

These are just some of the benefits machine learning currently offers manufacturers. There’s no doubt that more solutions will be discovered as technology improves, and it may even become common to see robots working the floors as collaborators with human workers.

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