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How Machine Learning Is Changing Manufacturing

Posted October 20, 2019

Machine learning is revolutionizing the way industries handle data, largely thanks to the degree of automation it allows. As more and more enterprises turn to digital platforms and technologies, data stores continue to grow, beyond a monumental size.

Image credit: Pixabay (Free Pixabay licence)

Machine learning injects another element into the bloodstream of modern data technologies. In the past, large data stores would be collected, processed and maybe even organized. However, the data wasn’t actionable in its raw form, which meant there was a delay in understanding what was coming in.

This technology can handle data in real-time, near-instantly, with little to no input from humans. It can also be used to sift through existing data stores to find relevant information, such as correlating patterns and trends. In manufacturing, this manifests in many ways. The most substantial change is that the manufacturing industry now has access to real-time insights and on-demand opportunities.

Discover changes happening in the manufacturing industry as a result of machine learning.

1. Enhanced Quality Control and Better Production

Machine learning technologies provide a variety of support opportunities to manufacturers, all of which contribute to enhanced quality control and improved production.

Neural networks can be used to monitor assembly machines — highlighting equipment that needs to be repaired or replaced. The faster this process plays out, the better the resulting operations. What machine learning introduces is the option to establish more of a proactive and preventative approach as opposed to reactive, when something has already gone wrong.

Machine learning can also incorporate incoming data to make ongoing adjustments throughout the production process. For instance, if clientele were to respond negatively to a particular design or manufacturing feature, the offending element can be changed almost immediately. It introduces more of a cyclical development process that remains ongoing, even after the launch of a product.

2. Real-Time and On-Demand Operations

Imagine a customer ordering direct from a manufacturer with near-instant development waiting in the wings. That is the kind of on-demand and real-time opportunities that machine learning introduces.

Not only do manufacturers gain real-time insights about their operations, but also additional feedback from clients, partners and the rest of the industry. It allows for incredibly nuanced adjustments that can be applied to influence the development of any supply line.

When coupled with 3D printing technologies, machine learning can further enhance the way manufacturers do business. Closer to home, components can be made in-house, right when they’re needed. This process eliminates the downtime usually required while waiting for parts to ship.

3. Eliminating Bottlenecks and Boosting Profits

Manufacturing processes can be complex to manage. Improving the performance of the entire operation is never as simple as cranking up the speed at which everything — and everyone — runs.

Sometimes errors or slowdowns, also called bottlenecks, significantly lower performance. At times, these bottlenecks are visible, and others are not. Machine learning can identify and solve bottlenecks by analyzing live data. Machine learning tools also point out problems that management teams didn’t notice.

Machine learning unlocks access to advanced data analytics, substantially improving our understanding of operations.

4. Reducing the Labor Shortage Impact

There is a chronic labor shortage sweeping across the industry. In the U.S., more than 500,000 manufacturing jobs remain vacant. Finding the appropriate talent in a desolate market is no small feat, and nearly every company in the industry is going to suffer as a result.

Many have turned to automation to help alleviate the growing demand for manual labor. However, there’s a limit to what automation can handle, as some operations require a human touch.

Machine learning solutions can help take on this challenge by scouring the talent market for qualified individuals based on their capabilities, experience and strengths.

5. Increasing Sustainability

Green and sustainable practices are becoming more necessary in today’s landscape, as customers tend to look for manufacturers and brands that align with their values. In fact, 88% of consumers want companies they do business with to improve their environmental and social footprints. The benefits of doing so stretch beyond consumer demand. More sustainable practices also mean lower operating costs and increased net profits.

Machine learning, predictive analytics and cloud technologies make it possible to increase sustainability. More importantly, the technology allows continuous operations on par with traditional techniques, all while using fewer resources.

Machine Learning Is on the Rise

As you can see, machine learning is shaping the future. From more sustainable practices to real-time insights and on-demand operations, there’s no question whether or not the technology will change manufacturing as we know it — it’s already happening.

Author’s Bio:

Emily covers topics in manufacturing and environmental technology. You can follow her blog, Conservation Folks, or her Twitter to get the latest updates.

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