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Many 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 powered by artificial intelligence (AI) technologies are coming to manufacturers soon and are being closely followed by many industry watchers. Both are also expected to revolutionize how factories work.
Hyperautomation is perhaps the first 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 familiar to many IT manufacturing departments today tasked with driving Industry 4.0 initiatives within their organizations. According to Gartner Research Vice President Fabrizio Biscotti, the approach enables organizations to automate as many of their processes as possible using technologies such as robotic process automation, low-code platforms and artificial intelligence.
Technologies are evolving at a rapid pace and manufacturers looking to remain competitive can no longer delay marrying them for full factory automation. That being said, those factories need to at least automate their systems 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. At the same time, 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 Scot Kim, senior director analyst in Gartner’s Advanced Manufacturing and Transportation group.
“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 through 2022, and manufacturers are poised to make aggressive investments to modernize their factories.”
As is often the case with Industry 4.0 initiatives, manufacturers need to automate as many technologies and processes as possible or risk being left behind.
“Hyper-automation has gone from being an option to being a requirement for survival,” 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 tools that enable hyper-automation, such as robotic process automation, low-code platforms, and AI, to grow at double-digit rates by 2022. The company predicts that by 2024, enterprises will reduce operational costs by 30% by combining hyper-automation technologies with redesigned operations.
Other types of automation software can be used to automate more specific business tasks, such as: B. the supply chain, the inventory control system and the customer relationship management system, added Biscotti.
Beyond automation to autonomous
Many manufacturers, even as they try to automate as many systems as possible, are starting to think about going beyond automation into autonomy.
The two concepts may sound similar, but they are actually very different.
Automation is a fixed process that happens by itself, like the popular idea of a factory production line. Sure, an automated vision system can monitor the process to select defective products, and sure, a robot can perform specific tasks along the entire line. But these systems are actually human-powered: they involve a person behind the curtain of their autonomous operation, much like the Wizard of Oz. With automated systems, the wizards are people behind those systems, who they have programmed to have limited functionality, said Reynolds.
The vision system is programmed to detect very specific errors, and the robot does the same job in exactly the same way over and over again.
Autonomous systems, on the other hand, can learn 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 management consultancy Kalypso.
In order to operate autonomous systems, a number of Industry 4.0 technologies must come together, including the Internet of Things and AI. The IoT consists of hundreds, sometimes thousands, of sensors connected to assets, continuously sending back real-time information about environmental conditions and how the devices are functioning.
“We now have the ability to enable self-learning, as opposed to explicitly programming these systems,” Reynolds said. “And they can learn how to make products and maintain the level of quality themselves.”
Automation wouldn’t be possible without the AI and machine learning technologies, he added, comparing factory automation to the concept of autonomous vehicles that are already hitting the road today — albeit on a small scale — in the form of bus-haulage trucks . The IoT continuously monitors things like road conditions and tire pressure, and measures the distance between the vehicle and, for example, a person on a bicycle crossing the road in front of the car.
Machine learning and AI tools allow the car to get smarter over time; Essentially getting better at driving based on past experience, much like a beginner makes progress by simply getting out and driving down the road, Reynolds said.
The same AI technologies are shifting factories from the traditional programmable logic controllers that automate lines to autonomous plants that operate on their own while learning 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 processes, the goal is to bring these human-like abilities — to observe, reason, decide and act — to the systems that will act autonomously, Reynolds said.
Autonomous manufacturing systems can deliver significant business value. They can eliminate or repurpose the need for manual effort, resulting in better planning, scheduling and resource allocation decisions, reduction in resource and raw material use, faster production rates, higher quality and yield levels, and greater capital investment efficiencies, he added .
“All of this is a natural progression in the automation market,” said Reynolds. “The ability of manufacturing processes to learn and adapt is the next logical step in this development.”
