RFID + AI-Powered Inventory Tracking with Multi-Cloud High Availability
This project implements a real-time RFID inventory tracking system for LogiCorp, integrated with Oracle Cloud as the primary platform and Microsoft Azure as a cold disaster-recovery backup.
Artificial Intelligence (via predictive demand forecasting) helps optimize stock levels and reduce storage costs.
Key benefits achieved:
- 30% increase in tracking accuracy
- 40% reduction in operational time
- 25% reduction in storage costs
- Full real-time visibility
- Enterprise-grade resilience (multi-cloud DR strategy)
| Layer | Provider | Role | Why? |
|---|---|---|---|
| Primary | Oracle Cloud | Production database | Always Free Autonomous DB · Native IoT integration · Zero cost |
| Secondary/DR | Microsoft Azure | Failover / Backup | Independent vendor · Protection against total OCI outage |
→ Full failover documentation: terraform/failover_strategy.md
/projeto-rfid ├── /backend → Spring Boot 3 (Java 17) ├── /frontend → React dashboard ├── /terraform │ ├── main.tf → Providers & variables │ ├── oracle_primary.tf → Oracle Autonomous Database (production) │ ├── azure_failover.tf → Azure SQL Database (DR) │ └── failover_strategy.md → Full HA/DR explanation ├── /docker → Dockerfile ├── /kubernetes → deployment.yaml + service.yaml └── README.md
cd backend
mvn clean install
java -jar target/ocl-backend.jar
2. Frontend (React)
Bashcd frontend
npm install
npm start
3. Infrastructure (Terraform)
Bashcd terraform
terraform init
terraform plan
terraform apply
Switch between primary (Oracle) and DR (Azure) by changing the active datasource in application.properties.
Technology Stack
Backend: Java 17 + Spring Boot 3 + Spring Security + JPA
Frontend: React 18 + Axios
Primary DB: Oracle Autonomous Database (Always Free)
DR DB: Azure SQL Database (Basic tier – near-zero cost when idle)
IaC: Terraform (multi-provider)
Containerization: Docker
Orchestration: Kubernetes ready
AI & Predictive Analytics
The PredictionService exposes /api/predict/demand endpoint for demand forecasting (ready to integrate Scikit-learn, TensorFlow or Oracle ML in the future).
Disaster Recovery Procedure (< 10 min)
Oracle Autonomous DB down → confirmed outage
Update spring.datasource.url to Azure connection string
Redeploy application
System back online using Azure SQL backup
Future Enhancements
Automated replication (GoldenGate / Debezium)
Automatic failover with health checks
CI/CD with GitHub Actions
Full ML model training pipeline
Conclusion
OCL is not just a proof-of-concept — it’s a production-ready, cloud-native, multi-cloud resilient logistics platform that demonstrates real enterprise architecture thinking.
Perfect for portfolios, capstone projects, or as a foundation for real-world supply chain solutions.
Rodrigo Dias de Oliveira · 2025
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