Companies worldwide are investing in artificial intelligence (AI) and machine learning (ML) to boost business growth, enhance productivity, and lower costs. Arguably, no sector has invested more than manufacturing and has seen more significant results.
We’ll look at machine learning, share five ways it’s transforming various aspects of the manufacturing process, and explore what the future might hold.
Machine learning (ML) is the process of teaching computers how to learn from inputted data so they can make decisions or predictions.
For example, you don’t tell ML how to recognize a cat. Instead, you show many pictures with and without cats. Over time, ML learns what characteristics must be present for an item to be correctly identified as a cat. The computer hasn’t been programmed to know what a cat is; it’s programmed to learn what a cat is.
ML is used to solve problems. In contrast, AI is about getting a computer to perform like an intelligent human. ML algorithms are used in AI, but ML is not AI.
Manufacturers are increasingly using ML because of its ability to learn what to expect and its high level of accuracy when predicting outcomes. Consider the following five current ML use cases that are transforming manufacturing.
Manufacturers use ML-based solutions across the entire production cycle. These help detect various issues present in current operating methods, including bottlenecks and unprofitable production lines.
The marriage of ML and industrial Internet of Things (IoT) objects — like sensors — presents exciting possibilities. Companies can take a much deeper look into logistics, inventory, assets and supply chain management. This analysis can uncover high-value insights in manufacturing as well as packaging and distribution processes.
A great example of this can be found in the German conglomerate Siemens. The company’s SICEMENT Automation system helps cement manufacturers monitor flow, pressure and temperature in the grinding process for optimal production. It also monitors the health of motors and bearings as part of its “smart anomaly detection” system.
Product development is one of the most widely adopted machine learning uses. New product plans and designs — as well as improvements — are tied to a vast trove of information that must be taken into consideration to yield the best results.
ML solutions can help gather consumer data and analyze it to understand demands, uncover hidden needs, and detect new business opportunities. This knowledge leads to better products from an existing catalog and new products that can open additional revenue streams for the company.
Machine learning is especially good at reducing the risks associated with new product development because its insights feed the planning stages, resulting in more informed decisions.
For example, Coca-Cola, one of the biggest brands in the world, uses machine learning for product development. In fact, ML fueled the launch of Cherry Sprite. The company used interactive soda fountain dispensers where customers could add various flavors to its catalog’s base drinks. 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.
When put to good use, machine learning can improve final product quality significantly in two ways:
General Electric is one of the most prominent investors in quality control, especially related to predictive maintenance. It has already created and deployed its ML-based tools in over 100,000 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.
Getting product quality right creates a virtuous circle by building customer trust, which prompts them to recommend the product to others. Additionally, maintenance and customer care costs will be much lower over the lifecycle of a product that’s made well.
Machine-learning solutions rely on apps, operating systems, networks, cloud and on-premise platforms, and other attack vectors. For this reason, mobile app, device and data security are critical concerns for modern manufacturers. Fortunately, machine learning has an answer in the Zero Trust Security (ZTS) framework. With this technology, user access to valuable digital resources and information is heavily regulated and limited.
Thus, machine learning can analyze how individual users access specific protected information, which applications they use, and how they connect to it. By enforcing a strong perimeter around digital assets, machine learning can determine who accesses what and can detect anomalies that quickly trigger warnings or actions.
Manufacturers must take security seriously. According to IBM’s X-Force Threat Intelligence Index, 61 percent of manufacturers’ operational technology was subject to attack. This means manufacturing is the most-attacked industry, taking the top spot away from the financial sector.
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 they once were relegated to, as their ML capabilities allow them to tackle more complicated processes.
That’s precisely what KUKA, a Chinese-owned German manufacturing company, aims for with its industrial robots. Its goal is to create robots that can work alongside humans and act as collaborators. 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 learning how to improve their productivity.
KUKA and other major manufacturers use its robots in their factories. BMW, the famous auto brand, is one of KUKA’s biggest customers,. Robots can serve by reducing human-related errors, boosting productivity, and adding value throughout the entire manufacturing chain.
Manufacturing is traditionally a technically advanced sector. For decades, manufacturers have been early adopters of various technologies, including automation, robotics and sophisticated digital solutions. It’s no surprise to learn that manufacturers worldwide are investing in machine learning solutions to improve their processes.
The results of the ML adoption are already here. Increased productivity, reduced equipment failures, better distribution and enhanced products are just a few of the perceived benefits of using machine learning in manufacturing.
While we are far from widespread adoption, a path is being paved. Numerous companies are leading the way to a smarter way of manufacturing the products we use, and this trend is sure to continue in the coming years.
Mark Fairlie contributed to this article.