Artificial intelligence

10 Ways to Leverage AI in Manufacturing

Manufacturing AI

AI can help manufacturers cut costs, make better products, and boost revenues. Read this article to find out exactly how manufacturing companies can benefit from AI-powered solutions.

The demand for AI-powered solutions in the manufacturing industry is increasing every year. Robots, machine learning algorithms, smart sensors, and other cutting-edge tools are becoming more powerful. In this article, we’ll have a look at how exactly businesses use manufacturing artificial intelligence, and how this technology helps them achieve their goals and how they benefit from it.


Collaborative robots—or cobots—work side-by-side with human professionals. Here are a few examples of what they can do at a manufacturing facility:

  • Carry out heavy lifting
  • Put parts together
  • Find and collect products in large warehouses
  • Provide an additional set of hands when needed

At the current stage of technological progress, robots are not yet ready to fully replace human professionals. These two types of employees often need to distribute tasks among themselves. For instance, cobots in car factories can lift bulky vehicle parts and hold them in place while human staff members secure them.

Autonomous robots are normally programmed to repeatedly perform only one task. Cobots, on the other hand, can learn multiple tasks. Manufacturers value them for their maneuverability and spatial awareness. Cobots know how to detect and bypass obstacles.

Robotic Process Automation

While cobots optimize the front lines of production, back offices can benefit from robotic process automation. This is what RPA software can do for a manufacturing company:

  • Be in charge of large-volume, repetitive tasks
  • Automate functions so that human professionals don’t have to type in information manually
  • Transfer data across systems
  • Take care of queries, calculations, and record maintenance

Robotic process automation should allow you to save on time and labor as well as reduce the risks of human error.

Digital Twins

A digital twin is a virtual model of a physical product. It obtains information about its physical prototype through the latter’s smart sensors. This technology enables manufacturers to achieve the following goals:

  • Gain insights about the product
  • Monitor the product throughout its lifecycle and receive alerts
  • Understand the inner workings of complex machines more clearly
  • Boost performance

For instance, the manufacturer can attach sensors to an airplane engine to transmit data to the digital twin every time the aircraft takes off or lands. The manufacturing company and the airline will get statistics about the engine’s performance. The airline will be able to use this data to anticipate issues and conduct simulations.

Predictive Maintenance

Manufacturers might wait until their equipment breaks down to fix or replace it—that would require significant expenses. The equipment won’t be producing new items while it’s broken. Plus, breakdowns might cause risks to workers’ safety. At the same time, replacing parts when it’s too early to do it might be just as bad as waiting for the breakdown. Premature replacements incur unnecessary expenses and take a lot of time. AI can predict which machines need maintenance and when it should take place. Such an approach enables businesses to minimize downtime, cut down costs, and enhance safety.

Lights-Out Factories

Such factories require minimal human interaction and rely almost 100% on a robotic workforce. This next-generation technology has not become commonplace yet, but has enormous potential. The term “lights-out” is used literally here: a factory equipped with robotic workers requires no lighting, air conditioning, heating, or other environmental controls. Such an approach allows businesses to save a lot of money. Robotic employees never get tired or lose focus. They can work 24/7 with greater precision and efficiency than their human colleagues. They require maintenance but they never feel sick and make very few errors.

Demand Prediction

Machine learning algorithms can analyze consumers’ purchasing patterns and provide insight to companies. Businesses can understand whether they need to produce more or fewer items with specific characteristics. The items will be ready before shops need them. Such an approach enables companies to prevent deficit and overstocking.

Bottleneck Prevention in Inventory Management

When used for inventory management, AI can carry out the following tasks:

  • Keep track of supplies
  • Notify human employees when it’s time to replenish supplies
  • Identify industry supply chain bottlenecks

It’s a game-changing opportunity for businesses that purchase ingredients with a short shelf life. AI can predict whether these ingredients will be delivered on time or not and inform managers how the delay might impact production.

Optimized Supply Chain Management

Supply chains of large manufacturers might feature millions of orders, purchases, materials, or ingredients to process. If human professionals handle all these tasks, it might take them too much time and effort. Let’s imagine a situation when a manufacturer purchases some parts for its machines from three separate suppliers. One supplier accidentally sends a batch with faulty components. After the machines are already assembled, the manufacturer needs to find out which ones were made with defective parts and recall them from the dealerships. AI can detect these machines much better, faster, and cheaper than even the most skilled human.

Promptly Detected Errors

To look for defects on production lines, factories can rely on visual inspection instruments. Even if the product is small and complex (such as a mobile phone), visual inspection cameras can accurately find flaws in it. Automated error recognition systems work much more promptly and accurately than the human eye. AI can notify human staff so that they fix the flaw before the product hits the market.

Accelerated Product Development

In certain industries, the speed of product development is a crucial factor. For instance, pharmaceutical companies can employ AI to analyze data from experimentation or manufacturing processes. The insights that businesses gain from the data analysis enable them to accelerate the production process, recuse expenses, and streamline replication methods.

Final Thoughts

Hopefully, this article came in handy and now you better understand how manufacturers can use AI. Manufacturing companies can benefit from cobots, robotic process automation, digital twins, predictive maintenance, and lights-out factories. Machine learning algorithms help them to predict demand and inventory management enables them to prevent bottlenecks. AI can enhance supply chain management, detect errors, and accelerate product development. This technology allows manufacturers to optimize business processes, deliver better products, and maximize revenues.

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