Many are familiar with the idea of factory automation, but what about ‘hyperautomation’? And, how about the rise of autonomous factories, with systems that make their own decisions about things like quality control and line speed?

Both concepts, driven by artificial intelligence (AI) technologies, are coming soon to manufacturers, and are being closely tracked by many industry watchers. They’re also both expected to revolutionize how factories function.

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

The technologies are evolving rapidly, and manufacturers that want to remain competitive can no longer put off marrying them for full factory automation. Barring that, these factories will at least need to automate their systems as much as is feasibly possible, he said.

These factory automation initiatives are possible because AI and the machine-learning algorithms that power AI systems are becoming more prevalent and affordable. At the same time, the Internet of Things and its web of sensors allows these factories to connect processes, gather data, and gain important insights into factory performance, said Scot Kim, a senior director analyst in Gartner’s advanced manufacturing and transportation Group.

“Hyperautomation is becoming a thing for manufacturers to increase productivity with optimization,” Kim said. “Supply chain disruptions, labor shortage and macroeconomic turmoil happening may continue throughout 2022 and manufacturers are ready to make aggressive investments to modernize their factories.”

Like so much about Industry 4.0 initiatives, manufacturers must automate as many technologies and processes as possible or risk being left behind.

“Hyperautomation has shifted from an option to a condition of survival,” Biscotti said. “Organizations 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 the tools that enable hyperautomation, such as robotic process automation, low-code platforms and AI is expected to see double-digit growth through 2022. The firm predicts that by 2024 organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes.

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

Beyond automation to autonomous
Many manufacturers, even 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 may sound similar but they’re 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 may monitor the process to pick out flawed products and sure, a robot may perform certain jobs all along the line. But these systems are actually human-powered: they include a person behind the curtain of their autonomous operation, rather like the Wizard of Oz. The wizards, in the case of automated systems, are humans behind these systems who have programmed them to perform in a limited way, Reynolds said.

The vision system is programmed to detect very specific flaws and the robot performs the same job in the exact same way, repeatedly.

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

A number of Industry 4.0 technologies must come together to operate autonomous systems, including the Internet of Things and AI. The IoT is made up of hundreds, sometimes thousands, of sensors connected to operating equipment and continually sending back information about surrounding conditions and about how the equipment is functioning in real time.

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

Automation wouldn’t be possible without the AI and machine-learning technologies, he added, likening factory automation to the concept of autonomous vehicles, which even today are hitting the streets — even if in a small way — in the form of buses and short-haul transportation trucks.

The IoT continually monitors things like road conditions and tire pressure and measures the distance between the vehicle and, say,a person on a bicycle crossing the street in front of the car.

The machine-learning and AI tools allow the car to get smarter over time; to essentially get better at driving based on past experiences, in much the same way a beginning driver advances simply by getting out and driving along the road, Reynolds said.

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

With AI and autonomous systems,whether self-driving cars or self-optimizing manufacturing procedures, the goal is to instill these human-like abilities — to observe, infer, decide and act — into the systems that will act autonomously, Reynolds said.

Autonomous manufacturing systems can bring significant business value. They can eliminate or repurpose the need for manual effort, which leads to better planning, scheduling and resource allocation decisions, reductions in resource and raw material inputs, faster rates of production, higher levels of quality and yield and greater capital asset efficiency, he added.

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