10 AI use cases in manufacturing

1.      Cobots work with humans

Collaborative robots -- also called cobots -- frequently work alongside human workers, functioning as an extra set of hands.

While autonomous robots are programmed to repeatedly perform one specific task, cobots are capable of learning various tasks. They also can detect and avoid obstacles, and this agility and spatial awareness allows them to work alongside -- and with -- human workers.

Manufacturers typically put cobots to work on tasks that require heavy lifting or on factory assembly lines. For example, cobots working in automotive factories can lift heavy car parts and hold them in place while human workers secure them. Cobots are also able to locate and retrieve items in large warehouses.

2.      RPA tackles tedious tasks

While manufacturing companies use cobots on the front lines of production, robotic process automation (RPA) software is more useful in the back office. RPA software is capable of handling high-volume, repetitious tasks, transferring data across systems, queries, calculations and record maintenance.

RPA software automates functions such as order processing, so that people don't need to enter data manually, and in turn don't need to spend time searching for inputting mistakes. In this way, RPA has the potential to save on time and labor.

3.      Digital twins help boost performance

Companies can use digital twins to better understand the inner workings of complicated machinery.

A digital twin is a virtual model of a physical object that receives information about its physical counterpart through the latter's smart sensors. Using AI and other technologies, the digital twin helps deliver insight about the object. Companies can monitor an object throughout its lifecycle, and get critical alerts, such as a need for inspection and maintenance.

As an example, sensors attached to an airplane engine will transmit data to that engine's digital twin every time the plane takes off or lands, providing the airline and manufacturer with critical information about the engine's performance. An airline can use this information to conduct simulations and anticipate issues.

4.      Predictive maintenance improves safety, lowers costs

Manufacturing plants, railroads and other heavy equipment users are increasingly turning to AI-based Predictive maintenance (PdM) to anticipate servicing needs.

If equipment isn't maintained in a timely manner, companies risk losing valuable time and money. On the one hand, they waste money and resources if they perform machine maintenance too early. On the other, waiting too long can cause the machine extensive wear and tear. The latter can also expose workers to safety hazards.

PdM systems can also help companies predict what replacement parts will be needed and when.

5.      Lights-out factories save money

An AI in manufacturing use case that's still rare, but which has some potential, is the "lights-out factory." Using AI, robots and other next-generation technologies, a lights-out factory is designed to use an entirely robotic workforce and run with minimal human interaction.

Manufacturers can potentially save money with lights-out factories because robotic workers don't have the same needs as their human counterparts. For example, a factory full of robotic workers doesn't require lighting and other environmental controls, such as air conditioning and heating. Manufacturers can economize by adjusting these services.

Robotic workers can operate 24/7 without succumbing to fatigue or illness and have the potential 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 buying patterns in human behavior and give insight to manufacturers.

For example, certain machine learning algorithms detect buying patterns that trigger manufacturers to ramp up production on a given item. This ability to predict buying behavior helps ensure that manufacturers are producing high-demand inventory before the stores need it.

7.      Inventory management prevents bottlenecks

Some manufacturing companies are relying 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 can even program AI to identify industry supply chain bottlenecks.

For example, a pharmaceutical company may use an ingredient that has a short shelf-life. AI systems can predict whether that ingredient will arrive on time or, if it's running late, how the delay will affect production.

8.      AI boosts supply chain management

One strong AI in manufacturing use case is supply chain management. Large manufacturers typically have supply chains with millions of orders, purchases, materials or ingredients to process. Handling these processes manually is a significant drain on people's time and resources and more companies have begun augmenting their supply chain processes with AI.

For example, a car manufacturer may receive nuts and bolts from two separate suppliers. If one supplier accidentally delivers a faulty batch of nuts and bolts, the car manufacturer will need to know which vehicles were made with those specific nuts and bolts. An AI system can help track which vehicles were made with the defective nuts and bolts, making it easier for manufacturers to recall them from the dealerships.

9.      AI systems detect errors

Manufacturers can use automated visual inspection tools to search for defects on production lines. Visual inspection equipment -- such as machine vision cameras -- is able to detect faults more quickly and accurately than the human eye.

For example, visual inspection cameras can easily find a flaw in a small, complex item -- for example, a cellphone. The attached AI system can alert human workers of the flaw before the item winds up in the hands of an unhappy consumer.

10.      AI systems help speed product development

Some manufacturers are turning to AI systems to assist in faster product development, as is the case with drug makers.

AI can analyze data from experimentation or manufacturing processes. Manufacturers can use insights gained from the data analysis to reduce the time it takes to create pharmaceuticals, lower costs and streamline replication methods.