A Large European Cargo Handling Company Enabled Real-Time ULD Tracking Using AWS-Based IoT and Analytics Platform

Challenges

The client aimed to build a robust AWS-based Data Lake solution for real-time tracking and predictive insights. Key challenges included:

• Lack of real-time tracking for Unit Load Devices (ULDs).
• Inability to accurately forecast SLA adherence for cargo handling operations.
• No automated alerting mechanism for temperature deviations or delays.
• Unplanned ULD maintenance due to lack of predictive insights.

Solution

Coforge implemented an end-to-end IoT and analytics architecture leveraging AWS Cloud services and Power BI for real-time insights:

• Deployed IoT Devices integrated with AWS IoT Core, S3 Lambda, SNS, Athena, Glacier, and Snowflake DB for seamless data ingestion and transformation.
• Developed an AWS-based Data Lake to ingest, store, and process high-frequency telemetry and operational data.
• Enabled real-time dashboards and analytics using Power BI, supporting quick decision-making and SLA forecasting.
• Implemented event-driven architecture with automated alerts for delays and temperature deviations.
• Designed predictive maintenance workflows using historical data and operational trends.
• Supported automated firmware updates for continuous performance optimization.

The Impact

• Real-time ULD and vehicle tracking with enhanced operational transparency.
• Accurate forecasting of ULD SLA adherence and delay prediction.
• Automated alerting for temperature deviations and service delays.
• Enabled predictive maintenance to minimize unplanned downtime.
• Streamlined asset management with automatic firmware updates.