Wuhan Automotive Factory
Intelligent Manufacturing Management System
Contact UsBusiness Background
As a leading player in China’s automotive industry, the automaker runs a large, highly automated digital plant in Wuhan, Hubei.
With rising automation and expanding production scale, it faces critical challenges from explosive data growth: diverse data types, limited real-time data sharing, and complex equipment condition management. A refined control and management solution is urgently needed to enable real-time production risk monitoring, timely scheduling and maintenance, and orderly production planning, ensuring safe and efficient operations.
Business Challenges
Facing massive industrial sensors and terminal nodes, traditional architectures have become a huge bottleneck restricting the computing power and storage for intelligent manufacturing.
Diverse Data Formats
The automotive industry generates structured, semi-structured and unstructured data. The automaker’s plant needs a data management system capable of processing diverse data formats to enable unified data storage, management and analysis.
Severe Data Silos
The plant relies on multiple independent business systems, resulting in severe data silos. Data across systems remains isolated, and full integration of IoT, production, sales and after-sales data is un-achieved, affecting decision-making and operational efficiency
Unsatisfactory Real-Time Performance
The plant demands real-time data processing. For instance, real-time road condition analysis, anomaly detection and critical fault early warning during vehicle operation all require data collection, analysis and response within milliseconds.
KaiwuDB Solution
Client Benefits
Improved Ingestion and Storage Efficiency
The system enables efficient collection, storage and analysis of multi-source data from 200+ devices and 30,000+ metrics across four workshops.
Simplified Databases Architecture
Data in InfluxDB and MySQL were seamlessly migrated to KaiwuDB. Data throughput and query efficiency improved by 3–8x.
AI-assisted Decision Making
The AI algorithm was applied to the PHM system, achieving 81% risk prediction accuracy and reducing unexpected failures by 90%–100%.