Hyperautomation: Does it have a role in manufacturing?


Most manufacturing businesses use various automation tools. However, staggered implementation often leads to substantial performance gaps and trouble scaling down the line. On the other hand, hyperautomation is a seamless, manageable strategy. Generally, it is the ideal way to address these issues.

What Is Hyperautomation?

Simply put, hyperautomation is a business strategy for widespread automation and digital transformation. It creates an ecosystem of tools to replace and support human workers. The goal is to make mechanization scalable and more manageable — traits the traditional approach struggles with.

This business strategy always uses a combination of various technologies. While there’s no arbitrary number facilities have to reach, most of them use a handful. In 2021, nearly 60% of businesses used between 4 and 10 tools in their approach.

Hyperautomation typically consists of robotic process automation (RPA), artificial intelligence (AI), machine learning (ML) models, low-code applications, the Internet of Things (IoT), and other software. While it is automation-centric, it often involves manual technologies, as well.

Where plain automation only reduces human involvement in certain tasks, hyperautomation focuses on simultaneously improving as many processes as possible. Additionally, it relies on a combination of software and devices rather than a single tool. In manufacturing, it could address everything from quality control to assembly.

How Does Hyperautomation Work?

The process works by combining multiple technologies. Usually, it’s a collection of basic devices and advanced, modern machinery. Since hyperautomation is a strategy, businesses approach it in different ways. However, many rely on the same core tools because they are proven effective.

RPA is typically the core component of hyperautomation since it is highly efficient. Once manufacturers set scripts, their robots constantly carry out their tasks. Its unique operational advantages have made it one of the fastest-growing technologies in the sector. Its market growth reached 63% in 2018 alone.

ML and AI are the other fundamental tools hyperautomation relies on. They’re one of the most common because they work exceptionally well with RPA. Also, they have massive potential and work well for virtually any application, making them ideal supporting technologies. They can even train on open-source data to lower implementation costs.

Manufacturers can train algorithms for virtually any task, making them impressively versatile. They are quickly becoming some of the most valuable automation tools. Experts project AI will contribute over $15.7 trillion to the global gross domestic product by 2030. Much of the growth — roughly 40% — will come from operational productivity improvements.

Beyond RPA, AI, and ML, manufacturers can use a large ecosystem of tools. Many choose to leverage low-code and no-code applications because they don’t require advanced technical knowledge. IoT devices, management software, and other automation technologies are standard.

What Problem Does Hyperautomation Solve?

Many manufacturers use hyperautomation because they view it as the future industry standard. After all, these technological advancements have shown them they can streamline nearly every operation with minimal downsides. They gain a substantial advantage over their competition if they use this strategy before others in their sector.

Previously, the alternative was step-by-step automation. While that process does improve efficiency, it staggers progress unevenly. Hyperautomation allows for a scalable, standardized ecosystem, enhancing coordination between manufacturing stages and departments.

Most importantly, hyperautomation is purpose-built — a vital feature in a dynamic industry like manufacturing. A one-size-fits-all solution leaves gaps since facility operations can vary depending on what they produce. Overhauling multiple stages at once with a unique strategy makes operations much more seamless.

How Do Manufacturers Benefit?

Hyperautomation’s role in manufacturing can be incredibly beneficial in various ways. It addresses human error, product quality, assembly speed, and performance, among other things.

1. Greater Efficiency

Hyperautomation leads to dramatic efficiency improvements across manufacturing stages. This development should come as no surprise, considering it streamlines multiple critical operations simultaneously. For example, RPA and AI can identify and reduce bottlenecks in the manufacturing process.

More importantly, since human error causes up to 90% of workplace accidents, increasing the amount of automation technology will lead to far fewer interruptions. Unintentional outages, sudden labor shortages, and on-site injuries could become relics of the past.

2. Higher Employee Satisfaction

There is a high likelihood that employee satisfaction will increase after manufacturers leverage hyperautomation. After all, workers will no longer have to spend most of their time on repetitive, tedious tasks. They can instead devote their time to upskilling. As a result, they increase their professional value and gain a competitive edge in the labor market.

3. Lower Operating Costs

Since automation technology reduces the need for human labor, manufacturers reduce their operating costs. RPA and low-code applications can replace most repetitive tasks. ML, IoT, or automated management software can be used for more complex roles or administrative duties.

Further, some hyperautomation tools can reduce future costs. For example, an ML algorithm makes predictive analytics possible, meaning manufacturers can accurately estimate when they will have to service their equipment. Fewer technical failures and unnecessary repairs reduce downtime, improving the production rate and lowering maintenance expenses.

4. Better Quality Control

Automation results in quality control enhancements because manufacturing professionals reduce human error. Improper calibration, for example, is one of the most common reasons for product defects — and hyperautomation can prevent it. For example, IoT sensors can alert manufacturers of excess equipment vibration, and RPA can replace manual assembly.

Manufacturers can even use hyperautomation to replace manual quality control roles. For example, they could deploy AI-integrated cameras to monitor the production line and inspect products. Alternatively, they could use IoT sensors to identify equipment faults and minimize the potential for defects.

5. Enhanced Coordination

Since hyperautomation involves implementing multiple technologies simultaneously, manufacturing businesses often connect them directly or with management software. As a result, they improve coordination. In fact, around 85% of workers believe automation tools enhance their teamwork and make collaboration between departments much more straightforward.

6. More Relevant Analytics

IoT sensors and ML models provide manufacturers with business-specific analytics. For instance, they could collect operational information from their equipment or quality control statistics from the inspection process. They can extract raw data if a manufacturing stage has an automation integration.

Instead of relying on market trends or using outdated physical metrics, manufacturers can automate and digitize their entire analytics process. As a result, they gain access to data-driven insights. Over time, they can build a historical database to optimize their operations.

7. Consistent Productivity

Automation results in performance boosts when it streamlines manual tasks. Organizations’ overall productivity increases by over 5% for every 1% increase in their use of robotics. Since the purpose of hyperautomation is to mechanize as much as possible, this already significant enhancement becomes a dramatic improvement.

Automation technology also supports humans in their new roles, further improving productivity. Since they can rely on their tools when they need to do things like make a report or perform routine maintenance, they can get much more done in a workday than usual.

How Can Manufacturers Implement Hyperautomation?

While there isn’t a one-size-fits-all approach to hyperautomation, manufacturers can follow the typical implementation approach. They can start with a digital twin to determine what tools they need — it simulates their facility and operations so they can accurately identify pain points.

Running multiple simulations and experimenting with different strategies shows manufacturing professionals how their automation technology will work together, giving them the tools to craft a data-driven plan. It also gives them insight into the value hyperautomation will provide.

Once they implement their automation technologies simultaneously, monitoring and consistently auditing operations is the best approach. While a simulation ensures they have the right tools, they can only guarantee real-world success if they ensure everything operates as it should.

The Future of Automation in Manufacturing

Most businesses in the manufacturing sector have already adopted various automation tools, but there is likely plenty of space left for further improvements. Hyperautomation is useful in every manufacturing stage, from project planning to quality control. In all likelihood, it may become the future standard of the industry.