Limited Visibility in Airport Baggage Operations
The client is a global aviation technology provider delivering IT and data solutions that support airlines and airports in modernizing critical operational workflows. Their solutions power key functions across flight operations, passenger processing, and baggage management.
As baggage operations expanded across high-traffic airport environments, the organization faced growing challenges in processing and standardizing large volumes of operational data in real time.
- High-volume and complex baggage event processing: Each bag generated multiple tracking events throughout its journey, creating millions of daily events that required low-latency, high-throughput processing capabilities.
- Fragmented and non-standardized operational data: Airports and ground-handling systems delivered data in inconsistent formats, making integration difficult and limiting scalability across operations.
- Outdated operational infrastructure: Existing SQL Server and Power BI systems supported static reporting but could not handle the real-time analytics and automation required for modern baggage operations.
To address these challenges, “Bag Radar” emerged as one of the most notable solutions, evolving from an internal pilot into a strategic operational intelligence platform supporting baggage logistics and real-time airport operations.
Developing a Real-Time Baggage Intelligence Platform
Addressing these challenges required more than traditional baggage tracking systems. A platform capable of processing live operational data, predicting disruptions, and providing real-time operational intelligence was essential.
Our team engaged as a strategic engineering partner to design and implement a scalable AI-powered baggage tracking and prediction platform.
1. Building a Real-Time Baggage Tracking System
The platform continuously tracks baggage movement across operational workflows using real-time operational and tracking data from airport systems.
Continuous visibility into baggage flows enables operators to identify delays faster and improve coordination across baggage handling operations.
2. Developing Predictive Analytics for Bottleneck Detection
Machine learning models were implemented to identify patterns associated with baggage delays, congestion, and potential mishandling incidents before disruptions escalated.
Predictive operational insights allow teams to proactively reroute resources and reduce the risk of cascading operational issues.
3. Integrating Real-Time Operational Data Sources
The solution aggregates live operational data from multiple airport systems into a centralized intelligence platform. Real-time integration ensures predictions and operational insights remain aligned with current airport conditions.
Unified operational visibility improves responsiveness and enables faster operational decision-making.
4. Delivering Operational Intelligence Through Real-Time Dashboards
Interactive dashboards were developed to provide airport teams with live operational insights into baggage movement, bottlenecks, and predicted risks.
Accessible decision intelligence enables operators to monitor baggage operations proactively and respond more effectively to disruptions.
“With more than 12 million baggage events processed daily, batch processing was too slow to support real-time operational decisions. Our team implemented a streaming architecture with continuous ML inference on Databricks, enabling faster and more scalable processing across baggage operations.”
Paweł Żak
Tech Lead & Senior Data Engineer – Addepto
Modernizing ASR Workflows for Better-Organized Trip Execution
The partnership transformed manual ASR workflows into a faster, more intelligent, and operationally efficient planning process. Rather than replacing aviation operators, the AI-powered platform was designed to support their work by accelerating information retrieval, reducing repetitive manual effort, and minimizing the risk of documentation errors.
By reducing administrative overhead, operational teams can focus more on higher-value planning and coordination tasks that require human expertise and decision-making.
Before
- Legacy SQL-based data silos
- Delayed and static operational reporting
- Disconnected baggage data sources
- Reactive handling of baggage issues
After
- Unified Databricks-based data platform
- Real-time operational dashboards and analytics
- Standardized and reusable data pipelines
- Predictive and proactive baggage loss prevention
Standardized and centralized operational data further enabled the organization to build reusable data assets that now support additional analytics and machine learning initiatives across aviation operations.
As part of KMS Technology, Addepto continues to help aviation organizations modernize operational workflows through real-time intelligence, scalable data platforms, and AI-powered decision support systems.
Ready to leverage real-time AI intelligence? Contact us today!