Manufacturing has become one of the main areas of application of artificial intelligence technologies. According to the Journal of Advanced Manufacturing & Processing, the global smart manufacturing market will grow to $238.8 billion in 2028. However, these are not only AI solutions, but also the Internet of Things, Big Data, and Augmented Reality. Absolutely, machine learning will play a connecting role in this.

Today, AI algorithms solve a whole pool of problems in enterprises. For example, computer vision monitors whether employees are using personal protective equipment. Such solutions are in demand in hazardous and food manufacturing.

Neural networks increase production processes' efficiency, help control product quality, and manage warehouses. So, what else can you expect from AI solutions as a manufacturing business owner?

In this article, we will discuss the various AI solutions for manufacturing, the benefits they can offer for your businesses, and how the Geniusee team can help you develop AI-powered manufacturing solutions.

Benefits of AI for manufacturing

What additional opportunities does the integration of artificial intelligence into the industry provide? Why do manufacturers need tools like machine learning and computer vision? Let's determine their main advantages.

Increased efficiency and productivity

Artificial intelligence allows you to shorten the production cycle and deeply automate repetitive tasks, such as assembly, quality control, packaging, and transportation of products.

Minimizing costs and risks

AI helps prevent equipment failure and reduce scrap, as well as reduce production waste, making it more environmentally friendly.

Improving product quality and innovation

Machine learning tools help develop products taking into account new market requirements and consumer preferences. They also simplify the introduction of new materials and technologies into production.

Production optimization

AI algorithms can analyze data sets and determine the best options for solving production problems. They also allow the creation of forecasts and simulation scenarios that help the manufacturer optimize operations and adapt to market conditions.

Reducing human error and increasing safety

AI allows you to reduce the risks associated with human involvement in the production process (errors, negligence, etc.). AI tools can also eliminate the need for humans to work in hazardous environments and help monitor safety compliance.

New quality of service

Automation of customer service and support using AI provides a significant improvement in the quality of service: it allows you to process consumer requests faster, provide them with more relevant information, and offer personalized offers and products.

Therefore, the role of AI in production can hardly be overestimated; in times of fierce competition for consumers, it can become one of the main factors of success.

Use of AI technologies in manufacturing

The scope of production is extremely wide: this includes high-tech mechanical engineering, agricultural production, and the production of consumer goods. Therefore, there is room for all available AI technologies in this niche. Let's name the main ones among them.

Machine learning (ML)

This term refers to a mechanism that allows a computer to learn from structured data and adjust its algorithms without explicit human programming. There are many types of machine learning. They are distinguished by the purpose of learning, the types of data, the scope of application, and the approach to learning (with a teacher, without a teacher, with reinforcement). Typical examples of using ML are voice recognition, text sentiment detection, and medical and technical diagnostics.

Deep learning (DL)

It is a type of ML that uses neural networks to process large amounts of unstructured data. A neural network is a whole system of algorithms that provide a comprehensive interpretation of data at several levels. Deep Learning is used, in particular, in machine translation of texts, search engines, image analysis, etc.

Generative AI

This is an artificial intelligence system that can not only recognize and analyze data but also generate new content based on this analysis: textual, visual, and multimedia. Generative AI is very close to creating a full-fledged creative product: ChatGPT can generate texts, DALL-E and Stable Diffusion models create images, etc.

Natural Language Processing (NLP)

This is the direction of development of AI, based on the use of machine and deep learning methods to interpret, process, and determine the meaning of human-written text by machines. NLP methods and large language models allow artificial intelligence to interact with a person in the format of regular correspondence, process documents, etc.

Speech recognition

Compared to NLP, this technology has another level of complexity — recognition of human spoken speech, for conversion into text, further analysis, and processing. Modern voice interfaces and assistants of any level of complexity are built on AI speech recognition technologies.

Computer vision

This development uses machine learning and neural networks to analyze visual data —  videos, images, broadcasts from cameras, etc. Its goal is to teach AI to perceive visual information as flexibly as humans do. Using computer vision, you can implement a facial recognition system, environmental monitoring, or visual control of product quality.

It is still difficult to say which AI technologies will become decisive in the future, which technologies will appear tomorrow, and what role of artificial intelligence in industry will ultimately become the leading one. However, we can talk about the obvious advantages of AI and practical examples of its implementation. Let's look at them below.

icon mail icon mail

X

Thank you for Subscription!


4 AI use cases in the manufacturing industry

Employing artificial intelligence on a grand scale is changing the way we live and work. Developments in generative AI and machine learning algorithms are transforming industries across the board. From art to medicine to supply chain management, AI systems are slowly creeping into every aspect of our lives. Similarly, AI adoption is impacting the manufacturing industry.

Are you wondering, “How is AI used in manufacturing?” Then, examine our breakdown of the technologies and their potential benefits for manufacturing businesses.

AI technology

How it’s used

Predictive analytics

Predictive maintenance (equipment);

Optimize assembly line processes;

Demand forecasting (resources, inventory, order management)

Computer vision

Detect product defects (quality control)

Robots and mechanical arms

Perform tasks that demand precision or heavy lifting

Digital twins

Create simulations to predict and improve outcomes on the shop floor

Robotic process automation

Automate processes to save time for manufacturing engineers and workers


Now that you have an idea about the use cases of AI in manufacturing, it’s time to dive deeper.

Predictive maintenance

Did you know inefficient equipment maintenance can decrease production capacity by up to 20%? Simultaneously, predictive maintenance costs 5-10% less and increases uptime by 10-20%.

Predicting equipment malfunction

If you want to improve productivity in your manufacturing processes, the solution is simple. Using AI and ML models will keep your shop floor operations going. AI-powered analytics take the data from your equipment sensors and allow you to create maintenance schedules. That way, you can prevent costly equipment failures and missed deadlines.

But how does it work? First, you need to set up equipment sensors. These sensors should feed real-time data, including weather conditions, into the system. Then, the AI models analyze data based on past performance and several parameters. As a result, you will get an evaluation of current performance, potential breakdown estimates, and actionable recommendations.

Benefits of AI-powered predictive analytics

The benefits of these AI algorithms in manufacturing plants are numerous:

  • Decreased equipment downtime,

  • Extended equipment life,

  • Reduced maintenance time and cost,

  • Increased productivity,

  • Improved workplace safety,

  • Better quality control.

These benefits make it easy to see how AI use in manufacturing drives profitability.

Airbus uses AI-powered analytics in aircraft production

Airbus is leading the way in AI use in manufacturing and aviation. In addition to real-time aircraft performance monitoring, they use smart sensors in their manufacturing processes. These sensors monitor performance and conditions, such as temperature and pressure, to predict potential issues in assembly lines. Then, if the parameters are not within set limits, they can halt machines and redistribute workloads to save time and resources. The result: machine life increases by 20-40%, saving the company millions of dollars.

Visual inspection with AI

Visual inspection is a vital part of quality control in manufacturing. The problem is that human workers can miss issues due to many factors. Unlike artificial intelligence, they could be distracted, tired, overworked, hungry, stressed, etc. Or, the anomaly can be so slight that it’s difficult to detect. On the other hand, AI-powered computer vision can detect even the most subtle changes on the assembly line.

Inspecting products for defects

While utilizing AI and machine learning to detect anomalies does require an initial investment, the results are compelling. Feeding manufacturing data into deep learning algorithms trains the system to recognize even the subtlest surface defects and deformations in production. The more data you feed into the system, the better it gets.

Advantages of AI-powered inspections in smart manufacturing

Here are some of the benefits manufacturers get from AI-powered computer vision:

  • 95% defect detection rate,

  • 99% less false positives,

  • 25-90% reduction in inspection time,

  • 30-80% increase in inspection accuracy,

  • 10-30% reduction in labor cost.

It sounds almost too good to be true. So, what’s the secret to AI in manufacturing inspections? Well, machines don’t get tired. And they don’t overlook things. A well-trained AI model can analyze complex shapes and surfaces in seconds. Then, it compares what it sees with training data, flagging inconsistencies. So, you get accurate, consistent inspection results for numerous products or parts in seconds.
Flex uses computer vision to boost operational efficiency by 30%

Flex manufactures printed circuit boards for electronic devices. These intricate parts need thorough inspection and are in high demand. The company started implementing AI and machine learning in defect detection to improve production speed and battle quality issues. And it paid off. Replacing traditional quality control inspections with automated systems increased operational efficiency by 30% and product yield by 97%.

speech generation

More from our blog:

Nonfunctional requirements in synthetic speech generation

Explore how to make AI audio generative systems effectively to improve your business operations.

Read now


Process optimization with AI-driven analytics

AI analytics are a powerful tool in the manufacturing industry. They can optimize each part of the product development process, from material sourcing to order management.

Identifying inefficiencies and bottlenecks

Artificial intelligence can analyze your production processes and identify inefficiencies and bottlenecks. When you know what hinders your productivity, you can implement steps to tackle it. But it all starts with data inputs. Having AI-enabled sensors in your manufacturing facility is a good start. These can help prevent equipment malfunctions through predictive maintenance, removing one source of inefficiency.

Another common issue in the manufacturing industry is defective products returned for rework, which wastes company materials and time and decreases customer satisfaction. By monitoring the number of these products and their issues, AI algorithms can pinpoint the origin of the problem. You can have issues with raw materials, faulty equipment, human error, or poor quality inspections down the assembly line. Whatever it is, artificial intelligence will give you an unbiased, data-driven answer. Better yet, it will propose a solution to improve efficiency.

But here’s the best part. You don’t have to go through a lot of trial and error. AI capabilities, such as digital twins, can help you run simulations. A digital twin is a virtual replica that tests different scenarios and collects data. For example, you can create a digital twin for your assembly lines and input different materials into your manufacturing operations. Then, you can see if the end products will perform better with various raw materials.

Optimizing scheduling and resource use

Once you know what problems you have in your manufacturing processes, you can tackle them with smart planning. For example, if you need to do proactive maintenance, artificial intelligence can help you schedule it when the demand for your products isn’t at its highest. That way, you can tackle critical challenges that could hinder production before they happen. Or, you can use a digital twin to see if your production capacity can handle increased product demand.

Furthermore, AI-powered analytics can help with resource allocation:

  • Artificial intelligence can help track inventory, supplies, and the workforce involved in the manufacturing process.

  • Then, you can plan orders, track them through supply chains, and automate processes as needed.

Manufacturing AI use cases boil down to improving production and cutting operational costs. The basis for this is data. Whether it’s generative AI, a digital twin, AI-powered robots, or other AI applications, the true potential of artificial intelligence is in a holistic view of manufacturing processes that consider data from production lines, quality assurance points, and supply chains. This scale of analysis would be overwhelming for humans and could give inaccurate predictions. Thankfully, technology enables manufacturers that adopt AI to tackle risks and seize opportunities with real-time insights.

Condals uses AI-driven analytics to reduce scrap by 45%

Condals Group Foundry is a leading iron and steel casting provider in Spain and Slovakia. However, producing massive amounts of iron casting also led to high scrap rates in their manufacturing processes. To tackle this challenge, they partnered with Data Prophet. The first step was employing AI in manufacturing to collect and analyze production environment data. Then, the algorithm could detect issues and suggest improvements. By getting an overview of operations and tracking their resources, Condals reached a 45% decrease in scrap. By saving resources and minimizing rework, they also improved their profitability.

Demand forecasting and supply chain management

One of the most impactful uses of AI in manufacturing could be accurate demand forecasting. While internal analysis of product development processes is useful, it means nothing if you can’t meet external demand, which is where big data comes into play.

Predicting product demand

Manufacturing industry trends and fluctuations in market demand can tell you whether you need to scale up or down. In downtime, you can plan maintenance, train employees, and run simulations to prepare to scale up. But how do you know when customer demand will rise? And how do you beat your competitors to it? Well, you will again need a little help from AI in manufacturing. Pairing your AI-powered systems with external sources can give you the benefits of big data analytics.

Information is everything, but unstructured information is difficult to analyze. Artificial intelligence algorithms can drive the entire manufacturing industry forward. By analyzing past data, seasonal trends, sales information, trending topics, and customer inquiries, AI can help companies gauge production needs and adapt.

Optimizing inventory levels and supply chain efficiency

New research has shown that using a particular material or process leads to a longer product lifespan, and the media is buzzing about it. You use that material or process in developing your products.

AI will detect the increased interest, analyze the supply and demand, and help you prepare for the demand surge. It can:

  • Predict how your sales will increase and how much stock you should have. 

  • Warn you about supply chain management issues before competitors start rushing to your suppliers.

  • In a well-integrated system, AI and machine learning will also suggest equipment maintenance in advance so that you don’t have to halt production and delay delivering your products.

Danone uses AI-powered supply chain optimization to reduce lost sales

Danone is a multinational food-products corporation based in France. It showcases one of the stellar uses of AI in manufacturing. So, what did they do? They used artificial intelligence to predict market demand shifts and optimize inventory management. Through data-based planning, they reduced lost sales and product obsolescence by 30%.

We’re here to support you if you want to start using AI in manufacturing but aren’t sure how. Our team of versatile experts can help guide your digital transformation. Whether you’re interested in generative design or predictive maintenance or want to streamline processes, Geniusee can develop a customized solution tailored to your needs.

Conclusion

It is impossible to ignore the topic of using AI in manufacturing because, in the next decade, artificial intelligence will become one of the main drivers of labor productivity growth.

We don't yet have all the comprehensive data on how AI is impacting manufacturing. But its tools already provide businesses with many competitive advantages: deep automation, precise quality control, increased security, and new opportunities in design, marketing, service, etc.

AI use cases in manufacturing range from predicting equipment breakdowns to quality inspections and process improvement to inventory and supply chain management, but that’s not all.

AI capabilities can also improve the manufacturing sector through:

  • Generative design and additive manufacturing using generative AI,

  • Robotic process automation for repetitive tasks,

  • Digital twin technology for simulations,

  • Robotic workers and collaborative robots (co-bots).

Of course, not every manufacturer has all the necessary resources and competencies to implement their own AI implementation programs. However, such companies can secure long-term development prospects if they seek help from external IT teams with the necessary technologies, specialists, and experience.