10 AI use Cases in Manufacturing

A factory full of robot workers once looked like a scene from a science-fiction film, but today, it is just a real-life scenario that demonstrates the makers’ use of artificial intelligence.

Manufacturers can benefit from AI in many ways.

Here are 10 examples of the use of AI in manufacturing that business leaders should explore.

1. Cobots work with humans

Collaborative robots – also known as cobots – often serve as additional sets of hands along with human workers.

While autonomous robots are programmed to perform a specific task over and over again, cobots are capable of learning different tasks. They can detect and avoid obstacles, and this agility and spatial awareness allow them to work alongside and alongside human workers.

Producers normally put cobots to operate on jobs which require heavy lifting or on factory assembly lines. By way of instance, cobots operating in automotive factories may raise heavy car parts and hold them in position while human employees secure them. Cobots will also be able to find and retrieve things in huge warehouses.

2. RPA tackles tedious tasks

While manufacturing firms use cobots on front lines of manufacturing, robotic procedure automation (RPA) applications is more useful from the rear office. RPA applications is capable of managing high-volume, repetitious jobs, shifting information across systems, questions, calculations and document maintenance.

RPA software automates functions like order processing, which means people do not have to input information manually, and subsequently do not have to spend time looking for entering errors. This manner, RPA has the capability to save in time and labour.

3. Digital twins help boost performance

Businesses can utilize digital twins to better comprehend the internal workings of complex machinery.

An electronic twin is a digital version of a physical thing which receives information regarding its counterpart through the latter smart sensors.

Employing AI and other technology, the electronic twin assists deliver insight regarding the item. Businesses can track an item during its lifecycle, and receive critical alarms, like a demand for review and maintenance.

For example, detectors attached to a plane engine will transmit information to the engine digital twin each time the plane takes off or lands, supplying the airline and maker with crucial details regarding the engine’s functionality. An airline may utilize this information to run simulations and expect problems.

4. Predictive maintenance improves safety, lowers costs

Manufacturing plants, railroads and other heavy equipment users are increasingly turning into AI-based predictive care (PdM) to expect servicing requirements.

If equipment is not kept in a timely fashion, companies risk losing precious money and time. On the 1 hand, they squander resources and money should they perform machine maintenance also premature.

On the flip side hand, waiting too long may get the machine extensive wear and tear. The latter may also introduce workers to security threats.

5. Lights-out factories save money

An AI that is still rare in terms of manufacturing use, but which has some potential, is a “lights-out factory.” A light-out factory is designed to use a fully robotic workforce and run with minimal human interaction, using AI, robots and next-generation technologies.

Manufacturers could potentially save money with light-out factories because robot workers do not have the same requirements as their human counterparts.

For example, a factory full of robotic workers does not require lighting and other environmental controls, such as air conditioning and heating. Manufacturers can economize by adjusting these services.

Robotic employees can work 24/7 without fatigue or illness and have the ability to produce more products than their human counterparts with potentially fewer mistakes.

6. Machine learning algorithms predict demand

AI systems that use machine learning algorithms can detect purchasing patterns in human behavior and give insight to manufacturers.

For example, some machine learning algorithms detect buying patterns that trigger manufacturers to speed up production on a given item. This ability to predict buying behavior helps ensure that manufacturers are producing high demand inventory before the store needs them.

7. Inventory management prevents bottlenecks

Some construction companies rely on AI systems to better manage their inventory needs.

AI systems can keep track of supplies and send alerts when they need to be replenished. Manufacturers may also undertake AI programs to identify industry supply chain bottlenecks.

For example, a pharmaceutical company may use an ingredient that has a short-shelf life. The AI ​​system can predict whether this component will arrive on time or, if it is running late, how the delay will affect production.

8. AI promotes supply chain management

There is a strong AI supply chain management in terms of manufacturing usage. Larger manufacturers typically have supply chains to process millions of orders, purchases, materials, or materials. Handling these processes manually is a significant drain on people’s time and resources and more companies have started to ramp up their supply chain processes with AI.

For example, a car manufacturer may obtain nuts and bolts from two different suppliers. If a supplier accidentally saves a defective batch of nuts and bolts, the car manufacturer needs to know what vehicles were made with those specific nuts and bolts.

An AI system can track which vehicles were built with faulty nuts and bolts, making it easier for manufacturers to recall them from dealerships.

9. AI systems detect errors

Producers may use automatic visual inspection components to hunt for flaws on manufacturing lines.

By way of instance, visual inspection cameras can readily find a flaw in a little, complicated item — for instance, a mobile. The connected AI system may alert human employees of this defect before the thing pops up at the hands of a miserable customer.

10. AI Techniques help accelerate product development

Some makers are turning into AI systems to help in quicker product development, as is true with drug manufacturers.

AI can assess information from experimentation or production procedures. Producers can use insights obtained from the information analysis to decrease the time necessary to produce pharmaceuticals, lower prices and streamline replication procedures.