Though it used to look like something out of a science fiction film, a factory full of robot employees is now a real-world example of how artificial intelligence is being used by manufacturers. Applications of AI can help manufacturers in several ways. That’s why in this article, we’ll look at a few AI use cases in manufacturing that company executives should investigate now and consider eventually implementing in the future.
Given the volume of data generated every day by industrial IoT and smart factories, artificial intelligence has various uses in the manufacturing sector. AI in manufacturing refers to the application of deep learning neural networks and machine learning (ML) systems with sophisticated data analysis and decision-making skills to optimize manufacturing processes.
Predictive maintenance is an often-mentioned AI use case in the industrial sector. Businesses can more accurately anticipate and prevent machine failure by utilizing AI on industrial data. Thus, costly downtime in industrial operations is decreased. There are numerous other potential applications and advantages of AI in production, including better demand forecasting and decreased raw material waste.
AI and manufacturing are a logical fit since industrial manufacturing environments already demand close collaboration between humans and machines.
Top AI Use Cases in Manufacturing
1. Demand is predicted by machine learning algorithms:
Machine learning algorithms employed by AI systems can identify human purchasing trends and provide manufacturers with useful information. For instance, certain machine learning algorithms can identify purchasing trends that prompt factories to increase the production of a specific product. Predicting consumer behaviour allows manufacturers to produce high-demand goods before retailers need them.
2. Benefits of AI in SCM (Supply Chain Management):
(SCM) Supply chain management is one effective application of AI in manufacturing. Big manufacturers usually have supply chains that handle millions of orders, purchases, and ingredients or resources. People’s time and resources are greatly wasted when these tasks are handled manually, which is why more businesses are starting to integrate AI in logistics.
For instance, a car manufacturer may get bolts and nuts from two different vendors. The carmaker will need to know which vehicles were built using those particular nuts and bolts if one supplier unintentionally sends a defective batch of them. It will be simpler for manufacturers to recall vehicles from dealerships if an AI system is allowed to identify which vehicles were produced with faulty hardware.
3. Humans and cobots collaborate:
Cabot’s, also known as collaborative robots, are often used as an additional pair of hands alongside human workers. Cabot’s can learn several tasks, whereas autonomous robots are designed to carry out the same activity repeatedly. They can operate alongside and with human workers thanks to their agility and spatial awareness, which also allow them to recognize and avoid obstacles. Cabot’s are usually assigned to heavy lifting or assembly line jobs by their manufacturers. Cobots, for instance, can lift and hold heavy automobile pieces in place while human employees secure them. Large warehouse items can also be located and retrieved by cobots.
4. RPA takes on tiresome duties:
Cabot’s are utilized by manufacturing organizations in the front lines of production, although RPA software is more beneficial in the back office. RPA software can oversee repetitive or high-volume operations, as well as inquiries, calculations, data transmission across systems, and record maintenance. RPA software saves time by automating tasks like order processing, which eliminates the need for humans to manually enter data and look for errors. RPA has the ability to reduce labour and time in this way. RPA is renowned for its ability to manage downtime and server problems. RPA can reboot and reconfigure servers in the event of these kinds of issues, which will ultimately result in decreased IT operating expenses.
5. Using digital twins can improve performance:
Businesses can employ digital twins to gain a deeper understanding of the internal workings of complex accessories. A digital twin is a computerized representation of a real object that gathers data from the real object’s intelligent sensors. The digital twin aids in providing a deeper understanding of the object through the use of AI and other technologies. Businesses are able to track an item during its whole life and receive important information, such as maintenance and inspection alerts.
For instance, sensors installed on the engine of an airplane will send data to the engine’s digital twin each time the aircraft takes off or lands, giving the manufacturer and airline vital knowledge about the engine’s operation. With this data, an airline can run simulations and foresee problems.
6. Predictive maintenance reduces expenses and increases safety:
In order to foresee maintenance needs, manufacturing facilities, railroads, and other heavy equipment users are beginning to employ AI-based predictive maintenance or PDM. When organisations neglect to maintain their equipment, they run the danger of losing both money and important time. On the one hand, if they do equipment maintenance too soon, they squander money and resources. However, waiting too long can result in significant wear and tear on the device. The latter may also put employees in danger.
PDM systems can help businesses overcome such hazards by forecasting the types and timing of replacement components.
7. Lights-out factories are cost-effective:
The lights-out factory is a very uncommon but promising application of AI in manufacturing. A lights-out factory runs on a fully robotic workforce and requires very little human involvement thanks to artificial intelligence, robotics, and other next-generation technologies. Lights-out factories have the potential to save costs for manufacturers because robotic laborers don’t require the same accommodations as human laborers. For instance, lights and other environmental pitfalls, such as air conditioning and heating, are not needed in a factory with robots working there. By replacing the workforce with AI-enabled robots, manufacturers can save a lot of money.
In comparison to human workers, robots can work around the clock without getting sick or tired and can theoretically generate more goods with fewer errors.
8. Monitoring inventory avoids delays:
AI technologies are being used by some industrial organisations to better manage their inventory demands. AI programs are able to monitor supplies and notify users when they need to be refilled. In addition, manufacturers can program AI to find inefficiencies in industry supply chains as well. A pharmaceutical manufacturer might, for instance, use an ingredient with a limited shelf life. Artificial intelligence (AI) systems have the ability to forecast the arrival time of ingredients and the impact of delays on production.
9. AI programs can identify flaws:
Automated visual inspection tools are highly useful for manufacturers to look for problems on production lines. Visual inspection tools, such as machine vision cameras, can identify flaws in real-time, frequently faster and more precisely than the human eye can. For instance, a small, complicated object like a cell phone can have flaws that are quickly discovered by visual inspection cameras. The AI system that is attached has the capability to notify engineers of any defects before the product is received by eventual disgruntled customers.
10. AI tools facilitate accelerated product creation:
As is the case with pharmaceutical companies, some businesses are using AI systems to help with faster product development. AI is capable of analysing data from manufacturing processes and experiments. Manufacturers can cut prices, expedite replication processes, and shorten the time it takes to produce products by utilising the knowledge they have learned from the data analysis.
Conclusion
The idea of “Industry 4.0,” the trend toward more automation in the manufacturing sector, and the huge data collection and transfer in manufacturing settings all depend on artificial intelligence. To ensure that businesses can extract value from the massive volumes of data generated by manufacturing machines, artificial intelligence (AI) and machine learning (ML) are crucial to implement. AI-powered data application to the manufacturing process can optimize supply chains, reduce costs, increase safety, and provide a number of other advantages.
However, AI programs need to be more than a didactic exercise. In the end, it all comes down to increasing productivity and minimizing costs. It is advised that you get in touch with Sphinx Solutions to find out more about how AI and ML can improve your industrial system.
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