System Architecture
Data Flow
Input: Scanners (barcodes/QR codes), sensors, and manual entry.
Processing: SQL database and automation middleware (e.g., Python, Java, .NET).
Output: Reports, alerts, and integration with ERP/MES systems.
Integration
IoT Devices: Real-time data logging from sensors and machines.
Dashboards: Visualize data using tools like Power BI or Tableau.
APIs: Facilitate communication between systems.
Advantages of SQL-Based Traceability
Scalability
Handles large datasets as production grows.
Data Integrity
Ensures reliable relationships between products, materials, and processes.
Custom Queries
Flexible analysis and reporting for different traceability needs.
Regulatory Compliance
Facilitates documentation for audits and certifications.
Cost-Effective
Leverages open-source or commercial relational database systems.
Future Trends
Blockchain for Traceability
Immutable records for transparent supply chains.
AI and Machine Learning
Predictive analytics for defects and production optimization.
Cloud Integration
Real-time access and data synchronization across multiple sites.
Edge Computing
Faster decision-making with localized processing.
SQL databases are ideal for traceability due to their ability to handle structured data and complex relationships. The system's architecture typically includes tables for product, process, batch, and event data.
