[-AI-3.5]Is QC circles activity necessary in modern manufacturing?

Abstract

現代製造業において、AI技術の進歩によりプロセスがより効率的に自動化されています。過去の生産ラインではQCサークルが非常に有用でしたが、現代製造業がAIベースになるにつれ、QCサークルの重要性が問われるようになっています。ここでは、QCサークルとAIの関係、QCサークルの課題とその適用不可能性の理由、そしてAIによる品質管理の可能性について考えてみたいと思います。

Table of contents

• Introduction
• The Connection between Modern Manufacturing and AI
• Issues with QC Circles
• Reasons for Inapplicability of QC Circles
• Possibilities of Quality Management through AI
• Conclusion

-- CAUTION

[Text]This blog is purely generated by Cohesive.ai

Introduction

In modern manufacturing, the advancement of AI technology has automated processes to be more efficient. QC circles have been very helpful in past production lines. However, as modern manufacturing becomes AI-based, the importance of QC circles is being questioned. This time, we would like to consider the connection between QC circles and AI, the challenges of QC circles and their reasons for not being applicable, and the possibility of quality management by AI.

The Link between Modern Manufacturing and AI

First of all, the use of AI in modern manufacturing industry is advancing. AI contributes greatly to quality control, automation of manufacturing processes, and improvement of quality control. However, QC circles are still active in many manufacturing industries, and their necessity is being questioned. In this article, we will consider the necessity of QC circles in modern manufacturing industry.

Quality control and QC circles in AI

AI is also very effective in quality control. The reason for this is that AI can efficiently process a large amount of data and can detect product defects early. QC circles have been a very useful technique in quality control, but with the advent of AI, defects can be detected with higher accuracy compared to quality control by employees.

Automation of manufacturing processes by AI

AI also helps automate manufacturing processes. Many tasks in the manufacturing process can be automated. Although QC circles have been used to improve the quality of the manufacturing process, human quality control such as QC circles is not necessary in automated manufacturing processes.

Contribution of AI to quality control

AI contributes more to quality control than employees. AI can perform fast and accurate quality control using manufacturing robots and quality control systems. Although QC circles can also be used, AI-based quality control is more effective and more accurate than QC circles.

The necessity of QC circles in modern manufacturing industry is being replaced by AI. Although there are cases where QC circles cannot be applied due to labor shortages, the scale and complexity of manufacturing processes, automation and improvement of quality control can be achieved by utilising AI, hence ensuring the quality of manufacturing without relying on QC circles.

QC Circle’s Challenges

Yes, QC circles are a very important quality management method in the Japanese manufacturing industry. QC circles can improve quality and increase productivity by focusing on quality improvement among employees. However, with the recent advances in AI technology, the challenges of QC circles have been highlighted.

Let’s consider the definition and activities of QC circles. QC circles are activities in which employees voluntarily organize and discuss issues related to quality management. However, the weakness of QC circles is that those with less experience may contribute less to improving quality through their discussions. In addition, time and effort are required for data collection and analysis, which are necessary for quality management.

On the other hand, AI technology can make a significant contribution to quality management. For example, it can process large amounts of data, enabling more efficient quality management. Furthermore, automatic judgement by AI enables higher-precision quality management, reducing employee burden.

However, QC circles are not entirely unnecessary with AI technology. There is always room for improvement in quality management. We must not forget that there are areas where human sensibility is necessary, as AI cannot handle all the fine details.

In other words, QC circles are needed in modern manufacturing, but as quality management evolves with AI technology, the traditional role of QC circles is expected to change. Effective combination of QC circles and AI technology is required to achieve more advanced quality management.

Reasons for Non-Application of QC Circle

In modern manufacturing, there are several reasons why QC circles may not function effectively. Among them, the most representative are scale and complexity that cannot be addressed by humans and shortage of labour.

In modern manufacturing, dealing with large and complex data is almost always necessary, and processing it manually is extremely difficult. As there are many tasks that cannot be handled by humans, the activities of QC circles are reaching their limits.

In addition, labour shortages have become a serious problem in modern manufacturing. QC circle activities require a lot of personnel to handle them as part of their daily duties. However, in the current situation of labour shortages, it is difficult for employees to engage in such activities.

To address these challenges, AI technology needs to be utilised. With the progress of AI technology, large-scale data processing can be performed efficiently, and improvement in precision through automatic judgment can also be expected. In addition, labour burdens can be reduced, making the introduction of AI technology essential in manufacturing.

Whether QC circle activities are necessary or not should be determined based on the flow of the times and technological advances in the future. However, it is undeniable that QC circle activities have reached their limits due to the progress of AI technology.

The possibility of quality control by AI

In modern manufacturing, AI-based quality control has high demand. As a result, AI can overcome many of the challenges faced by QC circles. First, AI can efficiently process large amounts of data. It can automatically analyse data and output results almost instantly. This automation simplifies the production process and reduces the workload for employees. Second, AI can increase accuracy through automatic judgement. By accurately detecting minor defects, AI can increase the reliability of inspection work. Finally, AI-based quality control can significantly reduce the burden on employees. Traditional QC circle methods required a lot of time and human resources. However, AI has improved the autonomy of the production process and reduced the opportunity for employees to make judgments. AI is not an ideal replacement for QC circles but can be used to solve some of the challenges surrounding them.

Conclusion¶

In modern manufacturing, the role of QC circles has been changed to production methods using AI. The conventional approach of QC circles has proven to have limitations when dealing with large amounts of data. However, the potential for quality management using AI is infinite and can handle large-scale and complex tasks that humans cannot handle. Therefore, QC circles are destined to be replaced by AI.