AI-Driven Ground Handling Intelligence Solution for a Leading Logistics Provider
A leading logistics provider engaged Aristek to implement AI-based flight delay prediction in ground handling. Early analysis revealed the need for a structured, reliable data foundation before modeling.
Logistics
Ongoing collaborationKey achievements
Key achievements
| 200+ | engineered time-series features per turnaround |
| 100M+ | event records consolidated into an enterprise-grade AI-ready dataset |
| >95% | event-to-flight mapping accuracy |
Challenge
As airport operations grow more complex and time-sensitive, ground handling providers face mounting pressure to reduce delays, improve SLA compliance, and optimize resources – all while maintaining strict safety standards.
The client’s objective was clear: move from reactive incident management to AI-powered delay prediction.
However, several critical challenges surfaced during early discovery.
Fragmented data ecosystem
Operational data was spread across flight schedules, turnaround logs, baggage systems, telematics, crew rosters, safety platforms, ERP tools, and external feeds. These systems operated independently and were not designed for unified analytics, limiting cross-domain visibility and making root-cause analysis of delays difficult.
Limited data readiness
Although the goal was AI-driven delay prediction, the main constraint was data quality. Inconsistent timestamps, missing identifiers, duplicate records, and unstructured labels required normalization, entity mapping, and feature preparation before modeling could begin.
Reactive operational management
Supervisors relied on fragmented dashboards and manual coordination. Monitoring focused on isolated processes rather than end-to-end operations. Delays were typically addressed after deviations occurred, leaving limited room for proactive intervention or predictive decision-making.
Limited cross-domain transparency
Operations, safety, workforce allocation, and financial performance were tracked separately. Leadership lacked a unified view connecting operational KPIs, SLA compliance, resource utilization, and the financial impact of delays, resulting in slower and partially informed decisions.
Scalability limitations
The client aimed to expand AI capabilities across additional operational domains and airports. Without a unified architecture, each new initiative required separate integrations, increasing complexity and reducing efficiency instead of enabling scalable growth.
Project scope
The project followed a structured, four-phase delivery approach to ensure technical stability and measurable validation.
Discovery & data mapping
We began by inventorying all relevant data sources, assessing data quality and schema consistency, and mapping operational processes to business KPIs.
This phase included defining the delay prediction use case (e.g., identifying flights at risk of turnaround delays based on historical operations, weather, and resource data).
Outcome: Clear understanding of workflows, mapped datasets, defined KPIs, and identified data gaps.
Domain data modeling
We designed a unified Ground Handling Data Model linking core entities such as:
- Flight
- OperationEvent
- EquipmentTelemetry
- EmployeeShift
- CargoShipment / ULD
This model created a consistent operational timeline across systems, enabling accurate feature engineering for delay prediction and future optimization use cases.
Outcome: A structured domain model ready to support ML pipelines.
Data ingestion & consolidation
We connected operational systems, IoT streams, and external feeds into a scalable cloud architecture using a layered data design:
Bronze (landing layer): Raw ingested batch and streaming data stored with source metadata and timestamps.
Silver (curated layer): Cleaned, normalized, and structured datasets.
Gold (analytics layer): Aggregated, joined datasets ready for ML training and analysis.
Where required, a feature store was introduced to support reproducible and real-time inference.
This separation ensured reproducibility, governance, and decoupling of ingestion from modeling. As a result, we got a consolidated, staged raw-to-curated repository ready for ML development.
Proof of Concept (PoC)
With validated and structured data in place, we implemented a pilot ML pipeline for delay probability prediction and Turnaround Time (TAT) forecasting.
The model was evaluated using time-based validation to reflect real operational conditions. We validated technical feasibility and defined a production-ready architecture for scaling.
In the end, the PoC demonstrated strong predictive reliability and identified key contributors to delays, such as:
- Resource contention density
- Late inbound aircraft
- Shift transition overlaps
- Weather variability
- Equipment allocation saturation
How it works
After data consolidation and ML deployment, the pilot AI operates as follows:
Operational events are ingested and standardized in near real-time.
Feature pipelines transform raw events into predictive signals.
The ML model generates delay probabilities and forecasts.
Supervisors take action, and feedback is logged for retraining.
Team
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Project Manager x1
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Data Architect x1
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Data Engineers x2
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Data Scientist x1
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ML Engineer x1
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BI Developer x1
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DevOps Engineer x1
Tools & technologies
Key takeaways
Key takeaways
The project highlighted that AI success depends on data readiness and thoughtful architectural design. A structured data foundation proved more critical than the model itself.
Through disciplined data assessment, domain modeling, and pipeline architecture design, we established a production-ready framework built for scalability. What began as a pilot resulted in a reliable, governed, and extensible data backbone.
The collaboration continues with:
 | Real-time streaming ingestion for continuous prediction refresh |
| Automated drift detection and controlled retraining |
| Expansion to additional operational datasets |
| Production hardening of monitoring and governance controls |