Applications of artificial intelligence in manufacturing

AI is no longer just a buzzword in news stories about IT giants. Every day, it becomes more and more essential for every business to succeed against the competition.

Together with expert Yevhen Kasyanenko, we will explain in more detail how artificial intelligence in manufacturing helps businesses work faster, cheaper, and with higher quality.

How is AI changing manufacturing?

Artificial intelligence in manufacturing is becoming a real strategic tool that helps optimize all processes. Below, we have compiled more specific information about what AI does in industry.

Reduces the number of defects through machine vision

Computer vision is the eyes of AI in a factory. Cameras monitor every detail, and algorithms find even micro-scratches and barely noticeable defects. The result:

• Less waste, less loss. The system sees what even an experienced inspector might miss.
• Consistent quality. Controls are in place at every stage, from raw materials to finished product packaging.
• Quick response. When a defect is found, the system immediately alerts production, which can then correct the process without waiting for a mountain of damaged parts to accumulate.

For example, Foxconn uses Google Cloud Visual Inspection AI to inspect smartphone components, significantly reducing the defect rate.

Improves demand forecasting accuracy

AI in forecasting is like an experienced analyst who sees not only today’s figures, but also what will happen in a month. It studies past sales, seasonal peaks, market trends, and provides the most accurate forecast possible. As a result:

• We produce exactly as much as we need. No mountains of products that then gather dust in the warehouse.
• Optimal inventory. AI will tell you when to replenish the warehouse and when to hold off on purchases so as not to tie up excess cash.
• Planning without surprises. Purchasing and logistics work seamlessly because the system calculates in advance how much raw material will be needed and when.

Example: Danone uses machine learning to forecast demand, reducing calculation errors and optimizing supply chains.

Automates warehouse operations and logistics

Here, AI becomes the brain of the warehouse: it controls robotic systems, calculates logistics routes, and makes sure everything goes according to plan. The result:

• Speed. Automated systems fill orders several times faster than manual ones.
• Routes without unnecessary detours. AI takes into account the location of goods and the load of areas to minimize movement.
• Minimum errors. Mis-sorting and loss of goods are a thing of the past because the system controls the process from receipt to shipment.

“There used to be warehouses where people spent hours looking for the right box, but now AI can tell the robot loader where it is and how to get it to the shipping area faster. This not only speeds up the work, it completely changes the logic of inventory management,“ notes Yevhen Kasyanenko

Reduces equipment maintenance costs

Instead of waiting for the machine to break down at the most inopportune moment, AI warns in advance that ”this bearing has about a week left.” As a result:

• Prevention instead of emergencies. Scheduled maintenance is cheaper and faster than calling technicians on short notice.
• Less downtime. Production does not stop because everything is repaired on time.
• Equipment lasts longer. Regular monitoring extends the service life of equipment.

“When the system tells you in advance that a particular component will last another week, it gives you time to calmly order a replacement part and replace it without stopping the line. This approach saves both money and nerves,” says Yevhen Kasyanenko.

Advanced scenarios for using AI in manufacturing

Continuing the theme, below we will look at how AI is used in more advanced scenarios — where technologies seemed like science fiction just a few years ago.

Digital twins — testing without risk or loss

Imagine that you have a virtual clone of your factory where you can experiment as much as you want without breaking anything in reality. The result is an opportunity to:

• Test ideas faster and cheaper. No need to shut down the shop floor to check what would happen if…
• Find bottlenecks. Models show where production is wasting time or resources.
• Optimize production processes. AI helps find more efficient workflows without costly trial and error.

Microsoft is already helping manufacturers model all of this as part of the Digital Twin Consortium initiative.

Internet of Things (IoT) + AI – the eyes and ears of a smart factory

Sensors, cameras, meters, temperature and pressure sensors – all of these can be combined into a single network that transmits data to the AI system in real time. As a result, the factory becomes a “living” organism that controls itself, and the owner of the production facility reaps only benefits:

• Instant control. From the temperature in the workshop to engine vibration.
• Autonomous optimization. AI regulates the operation of equipment on its own.
• High safety. For example, a gas sensor can detect a leak, and the AI will automatically shut down the equipment and sound an alarm.

General Electric is already using IoT sensors to monitor turbines and aircraft engines, preventing critical failures, while Elon Musk’s Tesla updates car software remotely, improving their safety and performance.

Robotic process automation (RPA) – when AI does the routine work

Manufacturing also involves mountains of paperwork. RPA AI bots take care of it instead of people, leaving them time for tasks that require brains rather than copy-paste. The result:

• Automatic processing of orders and documents. No typos, delays, or endless edits.
• Elimination of repetitive tasks. Filling out forms, preparing reports, entering data—bots do it all.
• Acceleration of processes. Document flow is faster, and employees can focus on what really requires their attention.

For example, Schneider Electric has already implemented AI bots that automatically prepare production documentation, which has reduced staff routine and eliminated any delays due to paperwork.

Advantages of implementing AI in manufacturing

Technologies are implemented for the tangible benefit of business, and artificial intelligence has a whole range of advantages in this regard:

• Cost reduction. AI automation is like a smart economist who is constantly looking for ways to save money. Less manual labor, accurate resource calculation, no overspending on materials. Predictive maintenance systems also promise savings on repairs based on the principle of “eliminating the problem before it turns into a costly accident.”
• Increased productivity. AI doesn’t get tired, drink coffee every half hour, or ask for vacation time. It works 24/7 without compromising quality. The result is more production in the same amount of time, faster order fulfillment, and less human error.
• Minimization of defects. Automated control using computer vision increases standards and customer loyalty, as customers are confident in the quality of the products.
• Employee safety. Complex and dangerous work can be assigned to robotic systems. They can also monitor working conditions in general, warn of dangers, and help prevent accidents.
• Flexibility in management. The market is changing, demand is fluctuating, and AI reacts faster than any planning meeting. Forecasts allow you to adjust production volumes so as not to clog warehouses with excess products and not face shortages during peak periods.

The future of AI in manufacturing

We now live in a time when the phrase “factory of the future” is no longer just a scene from a science fiction movie. All of this is already in development, and some of it is already being implemented by companies around the world. AI is becoming not just an assistant, but an engine that drives the entire industry.

In the near future, there are several promising areas of AI development in industry, which are discussed in more detail below.

Generative AI – when machines also become inventors

Imagine an engineer who doesn’t sleep or eat and spends the night going through thousands of design options until he finds the best one. This is generative AI. Examples:

• Nissan already models car bodies in such a way that they cut through the air rather than resist it.
• BMW optimizes its assembly lines so that every bolt is tightened in the perfect place at the right time.
• The jewelry brand J’evar uses AI to design jewelry, from the first sketches to the finished layout.

Collaborative robots (cobots) – colleagues who never tire

These are not the huge robots from movies that scare people with their size. Cobots can be miniature or the size of an average person and work right alongside humans: they help carry heavy loads, turn identical parts, and do everything that seems like an eternity to a person at the end of a shift. They are easy to retrain for new tasks, and they will work around the clock without complaining about back pain.

Big data, big decisions

The old principle of “solving a problem when it arises” is becoming a thing of the past. AI processes such volumes of data that it can predict what will happen to your production in a week, a month, or even a year, for example:

• It sees where the supply chain is starting to falter.
• It predicts when the warehouse will run out of a necessary part.
• It suggests how to restructure the process in order to meet a new order.

And that’s just what we can see today. It will get even more interesting in the future: smart factories where everything from design to delivery works as a single organism, all under the supervision of artificial intelligence.

Conclusion

AI is gradually becoming a sought-after and powerful tool that is changing manufacturing. It helps automate routine tasks, optimize processes, and produce consistently high-quality products. Companies spend less, defects are a thing of the past, and employees work in safer conditions.

But here’s the thing: you can’t just implement AI at the snap of your fingers. You need the right data, competent integration with your systems, and an understanding of how these technologies can be applied to your specific production. At KISS Software, led by Yevhen Kasyanenko, we know how to do this without unnecessary experimentation and with maximum benefit for your business.

Want to stay ahead of the competition and make your business faster, cheaper, and smarter? Contact us now, and we will help you take your business to the next level.