Are you using your manufacturing data
or only collecting it?
QualityLine AI solution automatically integrates, interprets and analyses
ANY type of manufacturing data and from any factory worldwide
or only collecting it?
ANY type of manufacturing data and from any factory worldwide
QualityLine’s AI Automated Data Integration Technology Maximizes Quality in Manufacturing
Achieving the full potential of manufacturing data to improve efficiency through effective business intelligence (BI) solutions is essential in today’s competitive market. Manufacturing analytics have become a critical necessity for end-to-end process monitoring and quality control.
Data on manufacturing available to us in a meaningful way?
Achieving a comprehensive overview of manufacturing data is challenging due to the myriad of data formats and structures from different machines, equipment, and systems. Data harmonization, which involves combining data from various sources, is a crucial aspect of any BI analytics implementation.
The Challenge of Existing Data Integration Solutions
Current manufacturing data integration solutions are often expensive and time-consuming, with a significant portion of the process done manually.
Team members and experienced software programmers are required to perform repetitive tasks, such as exporting numerous data sources into a unified format to enable the BI analytics.
AI-Powered Automated Integration for Improved Quality Control
BI analytics and data analysis are most effective when all types of data processes are unified into one database. Continuous collection and analysis of data from every phase, including SMT repairs, manual tests, and automated tests, are crucial for tracking production progress and quality levels. This comprehensive approach ensures the maintenance of product quality and the protection of the brand.
QualityLine has developed a technology that automatically harmonizes and interprets any type of manufacturing data without requiring API or hardware installation.
This technology remotely and automatically integrates, interprets, and analyzes data from any global production source, including testing stations, machines, sensors, ERP, and MES systems. This enhances industrial monitoring and ensures the required quality levels.
Benefits of AI Technology in Data Integration
AI technology can integrate with all types of data source
from any factory worldwide, allowing manufacturers to monitor the full manufacturing process and improve quality, even when parts of a product are produced by third parties. The combination of AI and machine learning technology analyzes all captured data through advanced algorithms to create an interactive manufacturing analytics dashboard. This system includes prediction and automatic anomaly detection alerts, pinpointing the exact source of manufacturing problems and suggesting solutions to improve quality and efficiency.
Improve Data Integration and Quality Control
The first step in improving manufacturing quality data
is to integrate and unify all global data sources into one harmonized database, including data from third-party vendors. When manufacturing errors occur, a comprehensive understanding facilitated by full data availability enables quick root cause analysis and accurate corrections. This process supports lean manufacturing and continuous improvement, boosting efficiency and quality levels.
“Keeping our existing data output, but significantly shortening
product manufacturing time and gaining full end-to-end
transparency over the whole production cycle.
Our production capacity improved by 22% within the first
year of use”
Marcel Bondar
Director of Quality, Telematics & Wireless M2M, Molex
Continuous Data Collection
QualityLine data integration system will continuously and automatically collect
and harmonize all accumulated data, so you will get the best insights
at your analytics dashboard.
TECH SPECS
TECH SPECS
WHY IS IT IMPORTANT?
Data integration involves combining data residing in different sources
and providing users with a unified view of them.
This process becomes significant in a variety of situations, which include both
from engineering and testing to repair of faulty products.
