Companies worldwide are investing in artificial intelligence (AI) and machine learning (ML) to boost business growth. Arguably, no sector has invested more than manufacturing; there have already been some remarkable results.
ML is proving to be a game-changer for manufacturers; it’s helped to boost product quality, reduce waste and prevent costly equipment breakdowns. We’ll look at ML, share how it’s transforming various aspects of the manufacturing process and explore what the future might hold.
How ML is improving manufacturing
Manufacturers are increasingly using ML because the technology can learn what to expect and is highly accurate when predicting outcomes.
John Rossman, managing partner at technology and innovation consultancy Rossman Partners, says ML’s impact on manufacturing will be significant. “The adoption of ML in manufacturing is setting the stage for a future where efficiency, precision and customer-centric innovation thrive,” Rossman explained. “As manufacturers invest in these capabilities, the true differentiator will be in their ability to ‘think big, but bet small’ and develop a systematic approach to the experimentation required.”
Consider the following five current ML use cases that are transforming manufacturing.
1. ML is improving manufacturing general processes.
Manufacturers use ML-based solutions across the entire production cycle. These systems help detect various issues present in current operating methods; such issues include bottlenecks, unprofitable production lines and quality control challenges.
The marriage of ML and Industrial Internet of Things (IIoT) objects — like sensors — presents exciting possibilities. Companies can gain deeper insights into logistics, inventory, business assets and supply chain management. This analysis can uncover high-value insights into manufacturing, packaging and distribution processes.
An excellent example of this is Intel’s use of ML in microchip manufacturing. Its system captures a series of detailed, high-resolution photographs of the thin silicon slices that comprise a chip during production. ML analyzes these images for defects like scratches or bubbles, which may be difficult for human inspectors to detect.
If an anomaly is found, the system can immediately halt the production process. This way, the manufacturing team can identify and fix the issue before producing additional defective chips.
2. ML is transforming product development.
Product development is one of the most widely adopted ML applications. New product plans, designs and improvements are tied to a vast trove of information that must be considered 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 insight allows companies to improve existing products and develop new ones — opening additional revenue streams while reducing the risks associated with launching unique innovations.
ML is especially good at reducing the risks associated with developing unique new products; this is because its insights feed the planning stages, resulting in more informed decisions.
Val Neicu, CEO and co-founder of SmartSKN, shared how this company leverages ML to revolutionize skincare product development. “By analyzing vast amounts of consumer skin data, ML enables us to create hyper-personalized formulations that align precisely with each customer’s needs, reducing the risks associated with launching new products,” Neicu explained.
At SmartSKN, clients use an AI-powered dermoscope to assess their skin condition. ML then formulates a personalized skincare product tailored to each client’s profile. “Our manufacturing system is powered by ML to produce products on demand, minimizing waste and ensuring efficient inventory management,” Neicu shared. “This dynamic approach allows us to respond in real time to customer demands without overproducing.”
This strategy enhances production efficiency while creating a highly interactive customer experience that combines cutting-edge technology and convenience.
3. ML is advancing quality control.
ML can improve final product quality significantly in two ways:
- ML can find product and packaging anomalies. ML helps companies prevent defective products from reaching the market. In fact, according to Averroes, ML boosts defect detection rates by 60 percent compared to human inspections.
- It can enhance manufacturing process quality. Through IoT devices and ML applications, businesses can analyze the availability and performance of all equipment throughout the manufacturing process. These insights guide predictive maintenance, helping extend equipment lifetimes and avoid costly downtime.
For example, Danish brewer Carlsberg uses ML-powered quality control to detect subtle taste differences between beers that human testers often miss. The company collects detailed data on each beer’s aroma and taste profile during production. When the beer is brewed, the ML analysis model can detect flavor inconsistencies or defects much earlier in the process. This approach reduces the volume of sub-par products leaving the factory and ensures customers receive the products they expect.
General Electric offers another example of ML-driven quality control. The company has deployed ML-based tools across more than 100,000 assets in its business units and customers’ operations; it spans industries like aerospace, power generation and transportation. Its systems detect early warning signs of manufacturing anomalies and provide long-term prognostics to estimate equipment behavior and lifespan.
4. ML is enhancing security.
ML solutions operate across apps, operating systems, networks, cloud computing platforms, on-premise systems and other attack vectors. As a result, mobile app, device and data security are critical concerns for modern manufacturers. Fortunately, ML plays a vital role in the Zero Trust Security (ZTS) framework. This approach heavily regulates and limits user access to valuable digital resources and data, helping protect your business’s sensitive information.
ML enhances security by analyzing user behavior and access patterns. It evaluates how individual users access specific protected information, which applications they use and how they connect to them. By enforcing a strong perimeter around digital assets, ML can determine who accesses what and detect anomalies that quickly trigger warnings or actions.
Manufacturers must take security seriously. According to IBM’s X-Force Threat Intelligence Index, manufacturing has been the most attacked industry for three consecutive years; it accounts for over 25 percent of security incidents. This sustained targeting of the manufacturing sector highlights the absolute need to implement preventative and robust measures.
5. ML is fueling robot technology.
AI technology allows robots to handle routine tasks that are complex or dangerous for humans. With ML capabilities, robots can go beyond the assembly lines they once were confined to and tackle more intricate processes.
That’s precisely what KUKA, a Chinese-owned German manufacturing company, is achieving with its industrial robots. KUKA aims to create robots that can collaborate with humans, enhancing productivity and safety. The company is bringing its robot, LBR iiwa, into the fold. This intelligent and adaptive robot is equipped with high-performance sensors that allow it to perform complicated tasks while working alongside humans; this way, they can learn how to improve their productivity.
Dr. Peter Gorm Larsen, vice head of section at Aarhus University and coordinator of the RoboSAPIENS project, is pursuing many of the same objectives.
“Adaptive robots usually behave by continuously monitoring their environment, analyzing the data collected, changing [their] plans, if needed, and then executing the new plans, accumulating knowledge as it goes along,” Larsen explained. “This is called a MAPE-K (Monitor-Analyze-Plan-Execute-Knowledge) control loop.”
However, Larsen emphasized that this process lacks a step to verify whether certified guarantees of safety and trustworthiness are maintained during behavioral changes. “The future of safety and trustworthiness of automated industrial robots comes when the robot can change its behavior while maintaining — or even increasing — its expected performance and staying at least as safe and robust as before it made the change,” Larsen noted.
Larsen is currently working on the aforementioned RoboSAPIENS project to develop the underlying technology to make adaptive robotics safer and more efficient. These advancements will help reduce human-related errors, boost productivity and add value across the entire manufacturing chain.
ML and AI have their place, but so do humans. When deciding whether you want to hire a human or a robot, consider if there’s a middle ground where you can benefit from the best of both.
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