AI is transforming manufacturing in various ways, and here are some key use cases that business leaders should explore and consider implementing

• Cobots work with humans

Autonomous robots are designed to perform a specific task or set of tasks in a predefined manner, often without the ability to adapt to new situations or learn different tasks. On the other hand, collaborative robots, or cobots, are equipped with sensors and advanced programming that allows them to learn and adapt to various tasks. They can also detect obstacles and adjust their movements accordingly, making them safer and more efficient when working alongside humans. This flexibility and spatial awareness enable cobots to collaborate closely with human workers, enhancing productivity and safety in shared workspaces.

• RPA tackles tedious tasks

Robotic Process Automation (RPA) software is designed to automate repetitive, rule-based tasks that involve handling large volumes of data.

• Digital twins help boost performance

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 deeper understanding about the object. Companies can monitor an object throughout its lifecycle and get critical notifications, such as alerts for inspection and maintenance.

• 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.

• Lights-out factories save money

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.

• Machine learning algorithms predict demand

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

• Inventory management prevents bottlenecks

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.

• AI boosts supply chain management

One strong AI use case in manufacturing is supply chain management. Large manufacturers typically have supply chains with millions of orders, purchases, materials or ingredients to process

• 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 in real time, often more quickly and accurately than the human eye.

• AI systems help speed product development

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