#Japan #AI Automation #Ehime #SCIEN #Triangle Ehime

SCIEN's Groundbreaking Project in Quality Management Automation

Introduction

In a significant development for Japan's manufacturing sector, SCIEN, a startup emerging from the University of Tokyo's Matsuo Lab, has been accepted into the 'Triangle Ehime 2.0' initiative. This program, which is spearheaded by Ehime Prefecture, aims to promote co-creation and solve local industrial challenges through digital technologies. SCIEN's project focuses on automating and standardizing quality management tasks using cutting-edge AI technologies.

Overview of the Project

Project Name: AI-Powered Automation for Quality Control in Manufacturing

The key aim of this project is to tackle the '2035 problem' afflicting the manufacturing industry in Ehime, which relates to the aging workforce and the looming crisis of skill inheritance. With an aging workforce where over 35% of employees are older than 55, the challenges are significant. Project partners include Shinwa Co., Ltd., known for non-woven fabric manufacturing, and Takayoshi Industrial Co., Ltd., specializing in precision machining. The project will implement five distinct AI functionalities:

1. AI Image Inspection: Automating visual inspections performed by skilled operators to enhance accuracy while reducing labor hours.
2. Quality Prediction AI: Optimizing manufacturing conditions to minimize trial runs and material waste.
3. Document Generation AI: Streamlining the creation of necessary quality management documents for ISO certification.
4. Skill Mapping AI: Supporting strategic placement of workers and planned skill inheritance for future needs.
5. Knowledge Management AI: Compiling scattered expertise in quality control into an accessible organizational knowledge resource.

Background and Challenges

Manufacturing in Ehime is at a critical junction, with projections indicating that by 2035, up to 80% of the workforce will reach retirement age. Currently, a staggering 85% of quality control practices depend on manual inspections, leading to a notably low ISO certification rate of only 30%. These inefficiencies result in annual losses amounting to ¥1 billion due to quality defects and ¥5 billion in missed business opportunities. Thus, the urgency for digital transformation and technological intervention cannot be overstated.

Specific Initiatives

Within this broad initiative, SCIEN will focus on implementing the aforementioned AI functionalities in collaboration with local manufacturers. The aim is to drastically improve the efficiency of quality management processes. This will include:

• - Enhancing Inspection Accuracy: By utilizing AI for image inspections, manufacturers can expect improved precision and reduced error rates in quality assurance.
• - Increasing Product Quality: Predictive analytics will help mitigate quality issues before they arise, fostering a culture of continuous improvement.
• - Expediting ISO Certification: Automating document preparation will streamline the certification process, enabling companies to achieve compliance faster.
• - Facilitating Skill Development: The skill mapping tool will aid in identifying the right fit for tasks, ensuring knowledge is passed on effectively within organizations.
• - Building a Comprehensive Knowledge Base: A centralized system will help preserve best practices in quality management, ensuring that knowledge is transferred and accessible.

Expected Outcomes

Over the next three years, the project aims to achieve several critical outcomes:

• - Significant Efficiency Improvements in Quality Management: Streamlining operations will reduce time and resources spent on traditional methods.
• - Reduction of Waste Losses and Improvement of Quality Standards: Greater quality will minimize defects and enhance overall production yields.
• - Increase in ISO Certified Companies: More manufacturers achieving certification will lead to an expanded market presence.
• - Creation of New Job Opportunities: Promoting youth engagement in manufacturing as skills transfer becomes more structured.
• - Strengthening the Overall Industrial Competitiveness: Extending the successful outcomes of this project throughout other manufacturing firms in the region.

About Triangle Ehime 2.0

The 'Triangle Ehime 2.0' initiative, launched in fiscal year 2024, is a continuation of the prefecture's commitment to tackling local challenges through digital solutions. It seeks to implement advanced digital technologies across various industries within the region to bolster economic vitality and foster a skilled workforce adept in digital tool utilization. The incorporation of statewide co-creation hubs has seen the selection of five pioneering projects aimed at leveraging AI and digital transformation, underscoring a collective push towards enhancing regional competitive capabilities.

Comments from SCIEN's CEO

Sora Tabata, the CEO of SCIEN, expressed her gratitude for the opportunity to participate in such a transformative project. “Being accepted into 'Triangle Ehime 2.0' is a significant honor. The challenges faced by the manufacturing industry in Ehime regarding quality management and skill inheritance are shared across local manufacturing sectors nationwide. Our aim is to develop an AI system that is not only innovative but also practically applicable, addressing the real-world issues faced by workplaces,” she stated.

Conclusion

SCIEN’s efforts, in collaboration with local partners, represent a significant advancement in leveraging AI to solve pressing issues within Japan’s manufacturing sector. Through these initiatives, the company hopes not only to influence Ehime but also to create a scalable model of innovation that benefits the wider manufacturing landscape across Japan.

About SCIEN

SCIEN is dedicated to enriching lives through science by creating value that is genuinely needed in society, moving beyond mere technological provision. Centered on manufacturing environments, it develops unique inspection systems and automation solutions, focusing on a phased implementation process that extends beyond the PoC stage. With a 'problem-driven' approach, SCIEN aims to contribute to the digital transformation and value creation for enterprises in the contemporary industrial realm.