In 2022, AI can considerably improve the workflows of manufacturing companies from various industries. Read this article to get to know how exactly manufacturers can benefit from AI-powered solutions.

By 2026, the market evaluation of AI-powered solutions for the manufacturing industry is expected to reach $16.7 billion. AI technology can help manufacturers to increase efficiency, reduce downtime, and improve the quality of their products. Customers, employees, and employers should equally benefit from the innovation.

Read this article to find out why exactly manufacturing companies might want to integrate AI-based solutions.

Safety in Workplace

Here are just a few examples of what AI can do to enhance occupational safety:

• Identify employees
• Track their contacts and interactions
• Measure temperature
• Monitor sewage in the workplace
• Make sure the equipment operates as intended

AI can detect potential problems before they occur and inform human managers so that they take measures. The number of workplace accidents such as trips, falls, and slips will decrease as well as the number of mechanical breaks that might result in death.

Besides, robots can work side by side with humans. Robots can replace people in such circumstances where the manufacturing process might pose a life-threatening risk. Instead of lifting weights with their own hands, humans can use robotic exoskeletons.

Security in Workplace

To control buildings 24/7, manufacturers might want to install advanced cameras and building management systems. These solutions can detect the following types of activities:

• Strange movements
• Theft
• Shooting
• Injury
• Delivery of vehicles
• Arrival of tourists
• Smoke
• Fire

The cost of deploying and maintaining such systems will be much lower than hiring a team of human security professionals.

Manufacturers might want only authorized employees to be able to access information, equipment, or specific areas. For this purpose, they can install the following AI-powered solutions on the factory floor:

• Facial recognition software
• AI cameras
• Biometric tags
• Integrated sensors

Besides, AI is indispensable for cybersecurity. Algorithms can noticeably reduce unknown threats from hackers thanks to their ability to process and sort through huge sets of data. AI can help manufacturing facilities withstand hacking attacks when they take place. Moreover, this technology can identify potential vulnerabilities long before someone would try to use them and strives to get rid of them.

Quality Checking

Even the most experienced human inspectors might fail to notice minor defects in products. Conventional methods of quality assurance might also fail to detect some flaws. Customers might find out about the defect only after they start using the product — and that might undermine their trust in the manufacturer.

To minimize risks, manufacturing companies should resort to machine learning and vision. Such solutions should help them to achieve the following goals:

• Track errors in the production line
• Detect imperfections
• Mark flaws
• Send pictures to human experts for recommendations or corrections

It’s enough to install AI-enabled cameras on the machines — and they will be able to detect even minor errors. The manufacturer can correct these errors before sending the product to the customer.

Predicting Maintenance

Manufacturers don’t need to wait until the equipment stops working and requires repairs. AI will inform them in advance about any issues that might be likely to take place. It’s always cheaper and easier to prevent mechanical defects than to fix them. There will be fewer periods of equipment inactivity and the manufacturer’s losses will decrease.

To carry out predictive maintenance, manufacturing companies should connect devices to sensors and advanced mathematical systems that will identify problems and respond to alerts. AI will process information, make conclusions about the state of machinery, and predict the need for maintenance.

Increased Customization

Consumers value customized products. But customization requires time and effort, so smaller manufacturers often can’t afford it. Here is how AI and ML tools can help them:

1. AI indicates the desired parameters for materials, costs, and manufacturing methods.

2. Generative design programs explore all possible solutions and design iterations within those parameters.

3. The manufacturer compares all the available virtual alternatives and picks the optimal one.

The company doesn’t need to release a single physical product to detect the best customization variant. This method significantly reduces the time, funds, and effort that the manufacturer needs to spend on customization.

Some companies might want to take one step further and test their generative designs in virtual reality that imitates the real world. That’s an efficient approach to detecting flaws before assembling the physical product.

Smart Manufacturing

Businesses might be happy with traditional manufacturing systems as long as they stick to their usual production schedule and work in standard conditions. But when one or several parameters change drastically, the conventional manufacturing system might fail to adapt to it. It might be able to modify output only in the most basic way.

The term “smart manufacturing” means that sensors are attached to various elements of the manufacturing facility. These sensors report to AI and ML algorithms about the changes in temperature, material, or process. Algorithms can stop or adjust the manufacturing process to avoid ruining materials or delivering a low-quality product. Such an approach should enhance the quality of products and reduce costs.

Improved Sourcing

Let’s imagine such a situation:

• The manufacturer is waiting for thousands of containers to arrive — but they’re still waiting for shipment overseas
• Local railroads, ports, and trucking lines are suffering from labor shortages
• When goods arrive, there are further delays and bottlenecks
• Expenses are skyrocketing

AI can’t fix these troubles — but it can detect and analyze shipping data patterns and raw materials expenses. Based on these statistics, the manufacturer can better understand how and when raw materials and finished goods should move to and from their facility. By analyzing trends, they can forecast the optimal time for ordering and shipping. Such an approach to risk management should help them to reduce costs, limit delays, and curtail expensive waste.

Final Thoughts

Hopefully, this article came in handy and now you better understand the importance of AI for manufacturing. AI- and ML-powered solutions enhance safety and security in the workplace and help manufacturers with quality checking and predicting maintenance. Companies can benefit from smart manufacturing, increased customization, and improved sourcing. AI enables businesses to release better products, optimize their processes, and maximize revenues.