Manufacturing DX Solutions 製造業DXソリューション
Realizing Digital Transformation in Manufacturing
We solve supply chain challenges across procurement, production, logistics, sales, and after-sales service
— leveraging AsiaQuest's extensive expertise and proven track record in AI, IoT, digital twins, system development, and cloud infrastructure.
Solution
ソリューション概要
AsiaQuest's Manufacturing DX
AsiaQuest's Manufacturing DX
Digital Transformation (DX) is a critical initiative for improving productivity and strengthening competitiveness in the manufacturing industry. By utilizing digital technologies such as IoT, AI, and cloud, we collect and analyze data from manufacturing equipment and production lines to achieve visualization of production processes, advanced quality management, and optimized production planning.
Service
- Performance Monitoring
- Equipment Maintenance System
- Drawing Knowledge Platform
- Search System for Similar Parts Defects
Service サービス
Factory Equipment Inspection & Maintenance in Local Environments
Factory Equipment Inspection & Maintenance in Local Environments
Solving the challenges of veteran dependency, paper-based operations, and reporting — all at once in a single workflow
Automatically read gauges using a smartphone camera & local LLM, and digitize inspection records. The collected data is securely aggregated through a two-layer approach — closed network and cloud — enabling trend analysis, anomaly prediction, and automated report generation.
- Just snap a photo to digitize gauges — ready to deploy on-site immediately
- AI Agent automatically generates analysis and reports, reducing reporting workload
Example Challenge
- Looking to streamline factory equipment inspection and maintenance operations with AI.
Equipment Maintenance System
Equipment Maintenance System
Detect early signs of abnormalities with AI and receive recommended inspection tasks
The AI agent reads the system diagram information of registered equipment and, together with abnormality alerts, supports the identification of inspection points and root cause analysis. By learning user behavior to classify alerts, it also helps reduce monitoring fatigue.
- Promote early detection of equipment faults to minimize the risk of unplanned shutdowns
- AI traces equipment systems such as piping to suggest recommended inspection tasks
Example Challenge
- Detecting anomalies early to eliminate unexpected equipment downtime.
Drawing Knowledge Platform
Drawing Knowledge Platform
Improving operational efficiency and quality by leveraging past drawing data
A platform that enables similarity search of 2D drawings by both text and shape.
By integrating into design review workflows, a system is built where data naturally accumulates over time.
AI corrects variations in notation and structuring, keeping internal data naturally organized.
- Eliminates the effort of recreating drawings from scratch when past ones can't be found
- Automatically detects risks by cross-referencing drawings with known past defects
Example Challenge
- Drawing knowledge is hard to reuse because the data is not well organized.
Performance Monitoring
Performance Monitoring
Driving improvement activities based on reliable evidence
Motion capture & AI identify processes and automate the collection of performance data on the manufacturing floor.
Collected data can be used to analyze and address bottlenecks, as well as serve as evidence for production planning.
- Automatically identifies waste, inefficiency, and inconsistency
- Smoothly measures the effectiveness of improvement initiatives through comparison with past data
Example Challenge
- Yield improvement efforts are underway but results have been hard to achieve.
Search System for Similar Parts Defects
Search System for Similar Parts Defects
AI inherits and cross-searches design knowledge
By matching design parts information (such as E-BOM) with defect databases (market and quality information) using AI, a system is built that enables cross-referential search of past similar defects — even without a part number. This prevents oversights during design reviews and addresses the risk of knowledge loss due to veteran retirements through systematic processes.
- Cross-search for similar defects without part numbers
- Proactively prevent oversights during design reviews
Example Challenge
- There is a risk of knowledge loss when veteran employees retire.
Case
製造DXソリューション（事例）
Meidensha Corporation
"Walking alongside from the concept stage, building together" — Realizing Meidensha's innovation through "hands-on" development support ~
In building a smart security service for Meidensha Corporation, AsiaQuest did not simply develop to spec — we partnered from the concept stage to jointly define requirements and co-create the system.
Sumitomo Wiring Systems, Ltd.
Development of a beacon-based access management system
