AI is driving ‘hyperautomation’ and autonomous factory systems

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Many are familiar with the idea of ​​factory automation, but what about ‘hyperotomation’? And, what about the rise of autonomous factories with their own decision making systems on things like quality control and line speed?

Both concepts, powered by Artificial Intelligence (AI) technologies, are coming to manufacturers soon, and are being closely monitored by many industry observers. They both deal with their confidence as they choose to embark on their play activities.

When it comes to the widespread adoption of these advances, hyperotomation is probably the next big thing, according to Gartner, who seems to have coined the term. But the concept is familiar to many IT manufacturing departments in their companies today, advancing the Industry 4.0 initiative. According to Fabrizio Biscotti, vice president of Gartner Research, this approach allows organizations to automate as many processes as possible with technologies such as robotic process automation, low-code platforms and artificial intelligence.

Technologies are evolving rapidly, and manufacturers who want to stay competitive can no longer avoid marrying them for full factory automation. Other than that, these factories will need to automate their system as little as possible, he said.

This factory automation initiative is possible because AI and machine-learning algorithms that power the AI ​​system are becoming more prevalent and cheaper. At the same time, the Internet of Things and its web of sensors allow these factories to connect processes, collect data and gain critical insights into factory operations, said Scott Kim, senior director analyst at Gartner’s Advanced Manufacturing and Transportation Group.

“Hyperotomation is becoming one thing for manufacturers to increase productivity with optimization,” Kim said. “Disruptions in the supply chain, labor shortages and macroeconomic turmoil could continue throughout 2022 and manufacturers are ready to make aggressive investments to modernize their factories.”

Much about the Industry 4.0 initiative, manufacturers should automate as many technologies and processes as possible or leave the risk behind.

“Hyperotomation has shifted from an alternative to a survival mode,” Biscuit said. “Organizations will need more IT and business process automation as they are forced to accelerate digital transformation plans in the post-Covid-19, digital-first world.”

Gartner expects the market for hyperotomation, such as robotic process automation, low-code platforms and AI, to see double-digit growth by 2022. The firm predicts that by 2024, joint ventures will reduce operational costs by 30%. Hyperotomation technology with redesigned operational processes.

Biscotti added that other types of automation software could be used to automate more specific functions of the company, such as supply chain, enterprise resource planning system and customer-relationship management system.

Beyond Automation to Autonomous

Many manufacturers, as they look to automate as many systems as possible, are also beginning to think about moving beyond automation and into autonomy.

The two concepts seem similar but they are actually quite different.

Automation is a fixed process that runs on its own, like the popular idea of ​​a factory production line. Sure, an automated vision system can monitor the process for selecting defective products, and for sure, the robot can do certain things. But these systems are actually human-powered: they involve one person behind the scenes of their autonomous operations, like the Wizard of Oz. Wizards, in the case of automated systems, are the men behind these systems who are programmed to do them in a limited way, Reynolds said.

The vision system is programmed to detect very specific defects and the robot performs the same task over and over again in exactly the same way.

Autonomous systems, on the other hand, can also learn how to perform tasks on their own and adapt to changes in processes or the environment, according to Jordan Reynolds, global director of data science at management consulting firm Calypso.

A number of Industry 4.0 technologies should come together to operate autonomous systems, including the Internet of Things and AI. The IoT is made up of hundreds, sometimes thousands, of sensors attached to operating equipment and constantly sends information about the surrounding conditions and how the equipment works in real time.

“We now have the ability to enable self-learning, as opposed to the explicit programming of these systems,” Reynolds said. “And they are able to learn how to make products and maintain quality standards on their own.”

Automation would not be possible without AI and machine-learning technology, he added, comparing factory automation to the concept of an autonomous vehicle, which is still on the streets today – albeit in a smaller way – in the form of buses and short-distance transport trucks. The IoT constantly monitors things like road conditions and tire pressure and measures the distance between the vehicle and, say, the person on the bicycle crosses the street in front of the car.

Machine-learning and AI tools allow cars to become smarter over time; Reynolds said that in order to be essentially better at driving based on past experiences, the beginner driver just moves on by just getting out and driving on the road.

The same AI technologies are shifting factories from traditional programmable logic controllers to automate lines at autonomous plants that operate on their own, learning as they go and getting better at what they do over time without human intervention. .

Reynolds said that with AI and autonomous systems, whether self-driving cars or self-optimizing manufacturing processes, the goal is to establish these human-like capabilities – observation, conjecture, judgment and action – in systems that will operate autonomously.

An autonomous production system can bring significant business value. They can eliminate or reuse the need for manual effort, which leads to better planning, scheduling and resource allocation decisions, reduction in resource and raw material inputs, faster production rates, higher quality and yield levels, and greater capital asset efficiency. He added. .

“It’s all a natural progression of the automation market,” Reynolds said. “The ability to independently learn and adapt manufacturing processes is the next logical stage of this development.”

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