AI driving ‘hyperautomation’ and autonomous factory systems

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Many people are familiar with the idea of ​​factory automation, but what about “hyper automation”? And what about the rise of autonomous factories with systems that make their own decisions about things like quality control and line speeds?

Both concepts, based on artificial intelligence (AI) technologies, are coming to manufacturers soon and are being closely watched by many industry watchers. Both are also set to revolutionize how factories work.

Hyperautomation is probably the first big thing when it comes to widespread adoption of these advances, according to Gartner, who seems to have coined the term. But the concept is familiar to many IT manufacturing departments working today to drive Industry 4.0 initiatives within their organizations. According to Fabrizio Biscotti, vice president of research at Gartner, the approach allows companies to automate many of their processes using technologies such as robotic process automation, low-code platforms and artificial intelligence.

Technologies are evolving fast, and manufacturers who want to stay competitive can no longer stop damaging them for full factory automation. In addition, these factories need to automate their systems at least as much as possible, he said.

These factory automation initiatives are possible because AI and the machine learning algorithms that power AI systems are becoming more common and affordable. In addition, the Internet of Things and its web of sensors are enabling these factories to connect processes, collect data and gain important insights into factory performance, said analyst Scott Kim, senior director at Gartner’s Advanced Manufacturing and Transportation Group. Said.

“Hyper-automation is becoming a thing for manufacturers to increase productivity through optimization,” said Kim. “Supply chain disruptions, labor shortages and macroeconomic turmoil may continue into 2022, and manufacturers are expected to make aggressive investments to modernize their factories.”

As is often the case with Industry 4.0 initiatives, manufacturers should automate as many technologies and processes as possible or leave risks behind.

“Hyper-automation has evolved from an option to a survival situation,” Biscotti said. “Enterprises will require more IT and business process automation as they are forced to accelerate digital transformation plans in a post-COVID-19 digital-first world.”

Gartner expects the market for devices that enable hyper-automation, such as robotic process automation, low-code platforms, and AI, to grow at double-digit rates through 2022. The company estimates that by 2024, companies will reduce operational costs by up to 30% through combination. Hyper-automation technologies with redesigned operations.

According to Biscotti, other types of automation software can be used to automate more specific business functions, such as B. supply chains, merchandise management systems and customer relationship management systems.

Beyond automation to autonomous

Many manufacturers, even wanting to automate as many systems as possible, are also striving to go beyond automation into autonomy.

The two concepts may sound similar, but they are actually very different.

Automation is a fixed process that runs automatically, like the popular idea of ​​a factory production line. Certainly, an automated vision system can oversee the process of selecting defective products, and of course a robot can perform specific tasks along the line. But these systems are actually human-powered: they involve a person behind the scenes of their autonomous operation, much like the Wizard of Oz. Reynolds said that in the case of automated systems, magicians are the people behind those systems, which they programmed to that they only work to a limited extent.

The vision system is programmed to recognize very specific errors, and the robot does the same thing over and over again.

Autonomous systems, on the other hand, can learn to function on their own and even adapt to changes in a process or environment, according to Jordan Reynolds, global director of data science at consulting firm Calypso.

Several Industry 4.0 technologies need to come together to operate autonomous systems, including the Internet of Things and AI. IoT consists of hundreds, sometimes thousands, of sensors attached to equipment, constantly sending back real-time information about environmental conditions and how the equipment is functioning.

“We now have the ability to enable self-learning, as opposed to explicitly programming these systems,” Reynolds said. “And they could learn how to make products and maintain that level of quality.”

Automation wouldn’t be possible without AI and machine learning technologies, he compared factory automation to the concept of autonomous vehicles that are still hitting the streets today, albeit on a smaller scale, in the form of buses and commuter trucks. . The IoT continuously monitors things like road conditions and tire pressure, and measures the distance between, for example, a person on a bicycle crossing the road in front of the car.

Machine learning and AI tools make the car smarter over time; Reynolds said, based on past experience, to essentially get better at driving, the way a novice driver gets on the road and drives.

The same AI technologies are shifting factories from traditional programmable logic controllers that automate lines to autonomous plants that work on their own, learn and get better at what they do over time without human intervention. will.

Reynolds said that with AI and autonomous systems, whether self-driving cars or self-optimizing manufacturing processes, the goal is to translate these human-like abilities — to observe, anticipate, make decisions and act — into systems that will act autonomously, Reynolds said .

Autonomous manufacturing systems can deliver significant business value. You can eliminate or rearrange the need for manual effort, resulting in better planning, scheduling, and resource allocation, reduction in resource and raw material use, faster production rates, higher quality and yield levels, and greater capital investment efficiencies. goes. ,

“All of this is a natural progression in the automation market,” said Reynolds. “The ability to independently learn and adapt manufacturing processes is the next logical step in this development.”

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