Artificial intelligence (AI) and its subsets are benefitting tons of fields, but you’d be hard-pressed to find one that’s taking more advantage from them than the manufacturing sector. Major companies around the world are heavily investing in machine learning (ML) solutions across their manufacturing processes and seeing impressive results.
From bringing down labor costs and reducing downtime to increasing workforce productivity and overall production speed, AI – with the help of the Industrial Internet of Things – is ushering in the era of smart manufacturing. The numbers speak for themselves; recent estimates predict that the smart manufacturing market will grow at an annual rate of 12.5% between this year and next.
It certainly makes sense. Numerous businesses are already experiencing the advantages of ML in several ways and working with QA testing services to refine what they are getting out of it. Here are some examples of current implementations.
1. General process improvement
One of the first things that come to mind when thinking about ML-based solutions is how they can serve daily processes across the entire manufacturing cycle. By using this technology, manufacturers are able to detect all kinds of issues on their routine methods of production, from bottlenecks to unprofitable production lines.
By combining machine learning tools with the Industrial Internet of Things, companies are taking a deeper look into their logistics, inventory, assets and supply chain management. This brings high-value insights that uncover potential opportunities not just in the manufacturing process but in the packaging and distribution as well.
A great example of this can be found in the German conglomerate Siemens, which has been using neural networks to monitor its steel plants in search of potential problems that might be affecting its efficiency. Through a combination of sensors installed in its equipment and with the help of its own smart cloud (called Mindsphere), Siemens is capable of monitoring, recording, and analyzing every step involved in the manufacturing process. This dynamic is what some people call Industry 4.0, a trademark of the smarter manufacturing era.
2. Product development
One of the most widely adopted uses of machine learning involves the product development phase. That’s because the design and planning stage of new products, and the improvement of existing ones, are tied to a multitude of information that has to be taken into consideration to yield the best results.
Thus, ML solutions can help in gathering consumer data and analyze it to understand demands, uncover hidden needs and detect new business opportunities. This all ends up in better products from the existing catalog as well as new ones that can open new revenue streams for the company. Machine learning is especially good at reducing the risks associated with the development of new products, as the insights it provides feed the planning stage for more informed decisions.
Coca Cola, one of the biggest brands in the world, is using machine learning for product development. In fact, the launch of the Cherry Sprite was the result of the company’s use of ML. The company used interactive soda fountain dispensaries where customers could add different flavors to the base drinks of its catalog. Coca Cola collected the resulting data and used machine learning to identify the most frequent combinations. The result? The detection of a large enough market to introduce a new beverage nationwide.
3. Quality control
When put to good use, machine learning can improve the final product quality up to 35%, especially in discrete manufacturing industries. There are two ways in which ML can do this. First and foremost, finding anomalies in products and their packaging. Through a deep examination of the manufactured products, companies can stop defective products from ever reaching the market. In fact, there are studies that talk about an up to 90% improvement in defect detection when compared with human inspections.
And then there’s the possible enhancement of the quality of the manufacturing process. Through IoT devices and ML applications, businesses can analyze the availability and performance of all the equipment used in the manufacturing process. This allows for predictive maintenance, which estimates the best time to attend to specific equipment to extend its life and avoid costly downtimes.
General Electric is one of the biggest investors in the quality control department, especially in everything related to predictive maintenance. It has already created and deployed its ML-based tools in over a hundred thousand assets throughout its business units and customers, including the aerospace, power generation and transportation industries. Its systems work to detect early warning signs of anomalies in its manufacturing lines and provide prognostics with long-term estimations of behavior and life.
Since all of these machine learning solutions rely on apps, operating systems, networks, cloud and on-premise platforms, the security of the mobile apps, devices, and data being used is a must for modern manufacturers. Fortunately, machine learning has an answer in the Zero Trust Security (ZTS) framework. With this technology, user-access to valuable digital access and information is heavily regulated and limited.
Thus, machine learning can be used to analyze how individual users access certain protected information, which applications they use and how they are connecting to it. Delimiting a strong perimeter around the digital assets, machine learning can determine who accesses what and who doesn’t but can also detect anomalies that can quickly trigger warnings or actions.
Unfortunately, the use of zero trust architectures and frameworks isn’t precisely a standard for the manufacturing industry. On a recent survey, only 60% of respondents said they were working or planning to introduce Zero Trust approaches into their digital landscapes.
Finally, some of the most well-known collaborators for manufacturers are getting smarter with machine learning: robots. The use of artificial intelligence in robots allows them to take on routine tasks that are complex or dangerous for humans. These new robots surpass the assembly lines that they once were relegated to, as their ML capabilities allow them to tackle more complicated processes than before.
That’s precisely what KUKA, a Chinese-owned German manufacturing company, is aiming at with its industrial robots. Its goal is to create robots that can work alongside humans and act as their collaborators. And, in that sense, the company is bringing its robot – LBR iiwa – into the fold. This intelligent robot is equipped with high-performance sensors that allow it to perform complicated tasks while working beside humans and learn how to improve their productivity.
KUKA itself uses its robots in its factories, but there are other major manufacturers that do so as well. BMW, the famous auto brand, is one of its biggest customers, and one of the businesses that’s already finding that robots can reduce human-related errors, boost productivity and add value throughout the entire manufacturing chain.
Some closing thoughts
Saying that the manufacturing industry is a technically-advanced sector is probably obvious up to this point. For decades, manufacturers have been early adopters of all kinds of technologies, from automation to robotics, and sophisticated digital solutions. So, it’s no surprise to learn that manufacturers around the world are already investing in machine learning solutions to empower their processes.
The results of said adoption are already here. Increased productivity, reduced equipment failures, better distribution and the introduction of enhanced products are but just a few of the perceived benefits of using machine learning in manufacturing. And while we are far from the widespread adoption of these solutions, the path is already paved, and numerous companies are leading the way to a smarter way of manufacturing the products we use.