What’s the hype in hyperautomation?

July 21, 2021 // By Neil Ballinger, EU Automation
What’s the hype in hyperautomation?
Coined by Gartner to describe one of the biggest automation trends of 2020, the concept of hyperautomation has rapidly spread across the industry. But how does it differ from regular automation, and is it a viable option for manufacturers who don’t wish to revolutionise their entire production or assembly lines? This article explains the fundamentals of hyperautomation.

In its 2019 report "Move Beyond RPA to Deliver Hyperautomation", Gartner pointed out a lack of guidance in how organisations should integrate robotic process automation (RPA) with other tools. The report also highlighted that business managers experience pressure to focus on automating simple routine procedures, but lack a strategy to extend automation processes to the whole factory.

The solution, according to the global research company, is hyperautomation, defined as end-to-end automation that combines complementary technologies such as deep learning, advanced analytics, machine vision, natural language processing, RPA and artificial intelligence to augment business processes.

Automation or hyperautomation?

The main difference between traditional automation and hyperautomation is that the first tends to focus on automating individual tasks — such as deploying a cobot rather than a human worker for repetitive pick and place applications — while the latter aims to optimise the whole production process with a holistic approach. 

Gartner predicted that hyperautomation would be one of the top strategic technology trends from 2020 onwards, but that doesn’t necessarily mean that manufacturers must buy into the hype. To understand whether end-to-end automation can deliver substantial business value, it can be useful to create a roadmap that clearly aligns business goals with the automation tools needed to reach them.

Gartner suggests considering three key objectives — revenue, costs and risks. With these parameters in mind, manufacturers might want to think about which technologies can drive revenue by enhancing customer engagement, increasing output, and automating repetitive tasks. They should then redesign processes to reduce the cost of poor quality and streamline production. Finally, they might need to consider the compliance risks of inefficient processes — for example, feeding parts to a machine manually might be not only inefficient but also risky, and it might therefore violate safety regulations.

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