How Machine Learning Is Revolutionizing the Manufacturing Sphere

Image of human brain taking in information gathered via machine learning

Industry 3.0, or the third industrial revolution, was all about automation and more efficient production processes. Toward the end of that era, many manufacturers and suppliers began to incorporate advanced machines capable of carrying out highly optimized operations using robotics and sophisticated assembly line systems.

We are now moving into Industry 4.0, or the fourth industrial revolution, which is still very much about enhanced efficiency, but it’s being achieved in remarkably different ways. Thanks to today’s highly digitized landscape — brought about through the adoption of more modern technologies — just about everything is data-driven. The manufacturing industry is no exception, seeing major change brought on by smart technologies and the Internet of Things (IoT).

As the World Economic Forum says, the fourth revolution will involve a combination of artificial intelligence (AI), advanced robotics, additive manufacturing (3D printing), and the Internet of Things. Put simply, Industry 4.0 is an amalgamation of more manual, efficiency-driving processes and digitally optimized, insight-driven systems. At the core of all of this, of course, are artificial intelligence and machine learning.

Both of these technologies are poised to revolutionize the current state of manufacturing, development, and supply chain operations. Here’s what they will enable and how they will improve the landscape:

More Predictive and Informed Decisions

Big Data systems and analytics platforms introduce one incredibly useful and profound opportunity: the ability to leverage accurate data. Incoming data streams can be used to inform and act in the now, but they can also be used to build algorithms and profiles for future strategies.

This is exactly where AI and machine learning technologies have the most to offer. Machine learning is cognitive, much like the human brain, and learns over time as more and more data becomes available. Developers can build an algorithm, sometimes one that is updated constantly, which will drive the system to action.

This allows for a streamlined channel of actionable intel — coming in almost endlessly — that can be used to make smarter decisions. As an example, imagine knowing how consumers might react to a new assembly process before you even implement it.

This goes well beyond simple operations, however. Such systems can also be used to predict and react to just about anything, from equipment and systems maintenance to cybersecurity. In fact, professional services firm PriceWaterhouseCoopers (PWC) claims that the adoption of machine learning and analytics as a means to improve predictive maintenance in manufacturing will increase by 38% over the next five years.

Demand and Supply Forecasting

Along with predictive and more informed data-driven operations come the opportunity to react in real time to the world around you. Within the manufacturing sphere, this is vital to smooth operations, as even the slightest hindrance can cause issues that ripple all along the supply chain.

If a particular supplier, for instance, is experiencing a shortage, then manufacturers can plan accordingly and either look for alternate materials or come up with new development processes. This differs in the world of Industry 4.0, as these processes can all be done seamlessly, without severing the production line. The predictive capabilities also allow for the discovery and identification of growing trends.

Closer to the consumer, it’s also possible to accurately measure sentiment and demand. This can be rolled back into production in a number of ways that ultimately improve waste management and reduce operational costs. If supply is low, for instance, manufacturers can cut down on their output to reduce energy costs and meet lower supply limits.

Improved Quality Control

Put simply, if you know more about what’s happening with your processes and systems, then it’s much easier to optimize quality and output. Manufacturers can discover complications or inefficiencies much earlier in the timeline and adjust accordingly. Data coming in from various sources — across production, logistics, transportation, distribution, and even sales — can be used to hone in on what’s affecting quality, good or bad.

Industry 4.0 will shift the entire manufacturing and supply chain landscape from a reactive entity to a more proactive one. It’s no longer necessary to wait for extended periods of time to understand or identify what’s happening with various processes, goods, and assemblies. Instead, you can see exactly what’s occurring in real time, allowing you to take action much sooner. AI and machine learning can enhance this even further, providing constant monitoring that sifts through incoming data streams and information, highlighting what’s most important. And this, in turn, will translate directly to higher-quality control results.

Machine Learning: A Necessary Tool for Data-Driven Companies

In the end, AI and machine learning will provide the means to sift through and process the many hundreds — if not thousands — of data streams flowing in across various industries. Monitoring manufacturing-related data, for instance, will require looking at hardware and machinery reports, production levels, quality control, total output, customer response, and other similar elements. Industry 4.0 sees us shifting to smarter, more data-driven processes and operations, and that means a near-endless supply of data that will need to be analyzed and leveraged.

AI and machine learning are really the saving grace in this process, and the cognitive, deep-learning capabilities offered by these technologies mean that they will only get better with time.

 

Image credit: whiteMocca/Shutterstock.com 

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