Companies around the world are investing in artificial intelligence (AI) and machine learning (ML) to drive business growth, increase productivity, and reduce costs. Perhaps no other industry is investing more and reaping greater rewards than manufacturing.
Learn about machine learning, share five ways it's transforming different aspects of the manufacturing process, and explore what the future holds.
How machine learning can improve manufacturing
Manufacturers are increasingly using ML because of its ability to learn predictions and its accuracy in forecasting outcomes. Consider these five current ML use cases that are transforming manufacturing:
1. ML improves common processes in manufacturing.
Manufacturers are using ML-based solutions throughout the production cycle to detect various issues with their current operational methods, such as bottlenecks and unprofitable production lines.
Combining ML with Industrial Internet of Things (IoT) objects (such as sensors) opens up exciting possibilities. It allows companies to gain a deeper understanding of logistics, inventory, assets and supply chain management. This analysis can provide valuable insights into manufacturing, but also into packaging and delivery processes.
A good example is German conglomerate Siemens, whose SICEMENT Automation system helps cement manufacturers monitor flow, pressure and temperature in the grinding process to ensure optimal production. It also monitors the condition of motors and bearings as part of the company's “smart anomaly detection” system.
2. ML is transforming product development.
Product development is one of the most widely adopted applications of machine learning: planning, designing, and refining new products involves a huge amount of information that must be considered to achieve the best results.
ML solutions help collect and analyze consumer data to understand demand, uncover hidden needs, and find new business opportunities. This knowledge can lead to better products from the existing catalog or new products that can bring new revenue streams to your company.
Machine learning is particularly good at reducing the risks associated with new product development, as its insights can be fed back into the planning stage, leading to more informed decision-making.
For example, Coca-Cola, one of the world's largest brands, uses machine learning in product development. In fact, the launch of Cherry Sprite was driven by machine learning. The company used an interactive soda fountain dispenser that allowed customers to add different flavors to a base drink from their catalog. Coca-Cola collected the resulting data and used machine learning to identify the most frequent combinations. As a result, they found a market large enough to introduce the new drink on a national scale.
3. ML is evolving quality control.
When used effectively, machine learning can significantly improve the quality of the final product in two ways:
- ML can spot anomalies in products and packaging. By thoroughly inspecting manufactured products, companies can prevent defective products from reaching the market. In fact, according to McKinsey, ML improves defect detection by up to 90 percent compared to human inspection.
- ML can improve the quality of manufacturing processes. Through IoT devices and ML applications, companies can analyze the availability and performance of all the equipment used in their manufacturing processes, which enables predictive maintenance, predicting the optimal time to maintain equipment in order to extend the lifespan of specific equipment and avoid costly downtime.
General Electric is one of the companies most committed to quality control, especially predictive maintenance. The company has already developed and deployed ML-based tools on over 100,000 assets across its business units and customers, including the aerospace, power generation, and transportation industries. Their systems detect early warning signs of anomalies in manufacturing lines and provide forecasts along with long-term predictions of behavior and lifespan.
4. ML enhances security.
Machine learning solutions rely on apps, operating systems, networks, cloud and on-premise platforms, and other attack vectors. This makes mobile app, device and data security a key concern for modern manufacturers. Fortunately, machine learning has an answer: the Zero Trust Security (ZTS) framework. With this technology, user access to valuable digital resources and information is tightly regulated and limited.
Thus, machine learning can analyze how individual users access specific protected information, what applications they use, how they connect, etc. By enforcing a strong perimeter around digital assets, machine learning can determine who has accessed what and detect anomalies that can immediately trigger alerts or actions.
Manufacturers must take security seriously: According to IBM's X-Force Threat Intelligence Index, 61% of manufacturers' operational technologies are subject to attack, making manufacturing the most attacked industry, overtaking the financial industry for the top spot.
5. ML is driving robotics technology.
Introducing artificial intelligence to robots allows them to take on routine tasks that are complex or dangerous for humans. These new robots are moving beyond the assembly lines they once served, as their ML capabilities allow them to tackle more complex processes.
This is exactly what the Chinese-owned German manufacturing company KUKA is aiming for with industrial robots. Their goal is to create robots that can work alongside humans and act as collaborators. The company is introducing the LBR iiwa robot into the field: an intelligent robot equipped with high-performance sensors that learns how to work alongside humans to carry out complex tasks and increase productivity.
KUKA and other large manufacturers use the company's robots in their factories. The well-known car brand BMW is one of KUKA's largest customers. Robots help reduce human errors, increase productivity and add value along the entire production chain.
ML and AI have their role, but so do humans. When deciding whether to hire a human or a robot, consider if there's a middle ground that lets you get the best of both worlds.
The future of machine learning in manufacturing
Manufacturing is traditionally a technologically advanced sector. For decades, manufacturers have been early adopters of various technologies, including automation, robotics, and advanced digital solutions. It is not surprising that manufacturers around the world are investing in machine learning solutions to improve their processes.
The results of ML adoption are already visible: increased productivity, reduced equipment failures, improved distribution, and enhanced products are just some of the benefits of using machine learning in manufacturing.
While it's far from widespread, the path is being paved. Numerous companies are leading the way toward smarter ways of manufacturing the products we use, and this trend is sure to continue in the coming years.
Mark Fairley contributed to this article.
