# Hyperautomation! Why and how hyperautomation is useful
Hyper-automation is the automation of business processes by combining multiple systems and tools such as ERP (Enterprise Resource Planning) with AI (Artificial Intelligence) and RPA (Robotic Process Automation). [Hyperautomation](https://www.zionmarketresearch.com/news/global-hyperautomation-market) is a mechanism to automate and utilize by combining. In this post, I will describe the case, market, and technology of hyperautomation.

Purpose and benefits of hyper automation

The global hyper-automation/RPA (robot-based process automation) market is expected to reach $9.2 billion in 2022 and $26.0 billion in 2027, with an average annual growth rate of 23.1%. (Source: MarketsandMarkets)

Major industries are increasingly demanding automation of their business processes to remain competitive. Investing in AI (Artificial Intelligence) and ML (Machine Learning) to identify and optimize business processes that should be automated is a challenge.

Hyperautomation automates virtual tasks, shifts business people to more intelligent tasks, and companies can increase their competitiveness.

Every industry is being updated to keep up with evolving technology. For example, in the construction industry, hyper-automation reduces the risk of accidents for corporate safety considerations. On-site information can be digitized in real time and adjustments can be made. See if field workers are walking in pedestrian lanes and that safety equipment is working properly and worn at all times.

Hyper-automation is the essence of DX (digital transformation) that makes full use of advanced technologies such as RPA (robot automation), ML (machine learning), and AI (artificial intelligence).

Please refer to the post below for DX.

Increased productivity

Hyper-automation dramatically improves productivity by passing data across business processes to automate and optimize them.

Let's look at an example of a make-to-order manufacturing industry.
In the case of make-to-order manufacturing, sales information is the start. From the sales negotiations, you can obtain the forecast of orders, specifications, and desired delivery date information. After the order is decided, the data is transferred to production planning, production control, and shipping, and orders are placed, production instructions, and shipping instructions are automatically carried out. By using 3D CAD, we can reduce the number of drawings and shorten the delivery time.

Elimination of personalization by applying to specialized and advanced work

In the past, it was difficult to automate tasks that required a high degree of expertise or complex processing, but by utilizing cutting-edge technologies such as ML (machine learning) and AI (artificial intelligence), tasks that can be automated. The range is wide.

Even if you had to rely on a specific person with specialized knowledge, you can do it without experience or knowledge. Especially in the IT field, even if you have no programming experience, you can now be involved in planning, design, introduction, operation, and data analysis. Tasks that can be automated can be automated so people can focus on more creative tasks.

Key Points for Introducing Hyper Automation
1. Overall optimal thinking
2. Personnel training
3. Centralized data management

Technologies used in hyper-automation

1. RPA (Robotic Process Automation)
The definition by the Japan RPA Association is, "Robotic Process Automation, commonly known as RPA, is the work that was assumed to be possible only for humans until now, or more advanced work, instead of humans. It is an initiative to substitute or substitute by utilizing cognitive technology including rule engines, AI, machine learning, etc. that can be implemented by

2. AI (Artificial Intelligence)
AI is the artificial reproduction of human intelligence using computers and software, and refers to the field of computer science that studies "intelligence". Like humans, it learns from experience data on its own, adapts to new inputs, and performs tasks flexibly. Deep learning (deep learning) and natural language processing (machine analysis of everyday human language) are often used.

3. Low-Code Platform (LCPA)
Low-code is not development based on coding in a programming language, but application development mainly through visual modeling, which greatly reduces the amount of source code written. The necessary environment that allows people without programming skills to develop is called a low-code platform.

4. iPaaS (Integration Platform as a Service)
Gartner (US research firm) defines iPaaS (Integration Platform as a Service) as “an integration that connects any combination of on-premises and cloud-based processes, services, applications, and data to individual or multiple organizations. A suite of cloud services that enable flow development, execution, and governance." In other words, iPaaS is a cloud service that integrates multiple business systems in a cloud environment.

5. Task Mining
Task mining is to analyze the log data of computer operations (application launch, screen launch, file open, copy & paste, etc.) in business, to identify issues and problems at the task level (inefficient work, bottlenecks, etc.) , repetitive tasks, etc.).

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