10 Ways Manufacturers Can Use AI in 2022


10 Ways Manufacturers Can Use AI in 2022

AI enables manufacturers to create better products, cut down their expenses, and boost their revenues. Read this article to get to know how exactly manufacturing businesses can benefit from AI in 2022.

More and more manufacturers are integrating AI into their workflows. It enables them to improve business operations, provide better maintenance for equipment, and deliver products of higher quality. Plus, AI enhances safety on the production floor, allows human professionals to concentrate on top-priority tasks, and reduces the company’s expenses. From this article, you’ll get to know how exactly AI-powered solutions can improve the manufacturing process in 2022.

10 Ways AI Is Improving Manufacturing In 2022

Approximately 60% of global manufacturers today rely on AI-based solutions. The following gives the percentage of manufacturing businesses that already employ AI in various parts of the planet:

In other countries and regions, the AI penetration is lower — but keeps growing every year. The demand for AI spiked when the COVID-19 pandemic broke out and manufacturers lost some of their human labor force.

Predictive Maintenance

AI keeps analyzing the data that comes from sensors on the production floor 24/7. It can use this information to identify potential downtime and accidents. AI forecasts when or if functional equipment will fail and notifies manufacturers so that they can take predictive measures. If businesses can provide maintenance and repair to the equipment before the failure occurs, it will cost them less than fixing or replacing the machines that are out of order.

Generative Design

This type of design relies on machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers should introduce the following parameters into generative design software:

  • Materials
  • Size
  • Weight
  • Strength
  • Manufacturing methods
  • Cost constraints

The software will offer a list of all the possible outcomes that can be created with those parameters. In a very short time, the company will get thousands of design options for one product without investing a single cent in its manufacturing.

Price Forecasting of Raw Material

Prices of raw materials can be extremely volatile. That’s a big challenge for manufacturers because price fluctuations impact their margins. AI can predict these fluctuations with the maximum possible accuracy. Plus, it can learn from its mistakes.


Here are just a few examples of tasks that industrial robots can carry out on the production floor:

  • Assembly
  • Welding
  • Painting
  • Product inspection
  • Picking
  • Placing
  • Die casting
  • Drilling
  • Glassmaking
  • Grinding

Robots were first introduced to manufacturing plants in the late 1970s. The modern generation of robots, powered by AI, can monitor accuracy and performance as well as improve over time. Robots can take care of repetitive tasks and minimize the risk of human error. Human professionals, meanwhile, can focus on more delicate and high-priority tasks.

Edge Analytics

Edge analytics enables companies to achieve three important goals:

  • Track staff health and safety with the help of wearables
  • Detect early signs of deteriorating performance
  • Enhance production quality and yield

AI collects data from sensors on the manufacturing equipment and processes the results. It delivers fast and decentralized insights to manufacturers so that they save time and effort on analyzing the data themselves.

Quality Assurance

This term denotes the process of maintaining the required level of quality in a service or product. Modern assembly lines consist of networks that meet the following criteria:

  • Autonomous
  • Interconnected
  • Data-driven

Human professionals feed sets of algorithms and parameters to these networks. This is how AI-based solutions get guidelines on producing end-products with the desired characteristics.

Defect Detection

For a human eye, it might be tricky to detect defects in the assembly line. Conventional code algorithms can cope with this task more efficiently — but they can’t learn from previous experiences and improve over time. These algorithms might deliver too many false positives that human professionals will need to check manually. AI with self-learning capabilities can significantly reduce the number of false positives and detect issues much more efficiently than any other solution.

Inventory Management

AI-powered tools are equally good at the following types of tasks:

  • Planning the supply
  • Forecasting the demand
  • Promoting inventory planning activities

Businesses can be sure that they will always have enough products in stock to meet the demand but won’t face the problem of overstocking.

Process Optimization

The ultimate goal of process optimization is to secure sustainable production levels. AI is indispensable for detecting potential bottlenecks in the manufacturing processes long before they take place. Plus, it can come up with smart suggestions on how to avoid these bottlenecks.

Let’s consider an example of a manufacturer that has several production facilities in different regions. Using a process mining tool, this business can compare the performance of different regions. The company can identify the factory that will be able to deliver the required goods to a specific customer timely and accurately.

AI-Powered Digital Twins

The essence of this technology consists in creating a virtual copy of a real-world asset or product. The following are the four spheres where manufacturers tend to use digital twins the most often:

  • Product development. The company builds a digital representation of a physical product, collects data about it, and uses the results to improve the real product.
  • Design customization. Consumers tend to appreciate customized products. Manufacturers use digital twins to create various versions of a product that can better satisfy customers’ demands.
  • Shop floor performance improvement. Using a digital twin is one of the easiest methods of identifying substandard performance and the likelihood of quality issues.
  • Logistics optimization. Digital twins enable manufacturers to find out in advance which materials they will need to use, order them on time, and automate the replenishment process.

Digital twins allow manufacturers to keep their expenses at a reasonable level when experimenting.

Final Thoughts

Hopefully, you found this article informative, and now you better understand how companies can use AI for manufacturing. AI-based solutions can take care of predictive maintenance, generative design, edge analytics, and raw materials price forecasting. They are crucial for quality assurance, defect detection, process optimization, and inventory management. Plus, businesses can integrate robots and AI-powered digital twins in their workflows to make the manufacturing process safer, quicker, and more efficient.

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