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.
Aviation logistics
Ongoing collaboration
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 distributed across multiple independent systems, including flight schedules, turnaround logs, baggage platforms, telematics, crew rosters, safety systems, ERP tools, and external feeds.
These systems were not designed to operate within a unified analytical architecture, limiting visibility and cross-domain analysis.
Lack of data readiness
Although the objective was AI-driven delay prediction, the primary constraint proved to be data readiness. Timestamp inconsistencies, missing identifiers, duplicated records, and the absence of structured labels required normalization, entity mapping, and feature engineering before modeling could begin.
Inconsistent operational management
Supervisors relied on fragmented dashboards and manual coordination. Delays were typically addressed only after deviation occurred, leaving limited room for proactive intervention.
Limited transparency across domains
Operations, safety, workforce allocation, and financial performance were monitored separately. Leadership lacked a unified view connecting operational performance, resource utilization, SLA compliance, cost per flight, and the financial impact of delays.
Without this integration, optimization decisions were slow and often based on partial information.
Scalability limitations
The client intended to scale AI capabilities across additional operational domains and potentially multiple airport environments. However, without a unified architecture, every new feature risked increasing complexity rather than efficiency.
Solution
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:
1. Operational events are ingested and standardized in near real-time.
2. Feature pipelines transform raw events into predictive signals.
3. The ML model generates delay probabilities and forecasts.
4. 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
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 initiative has moved from feasibility validation to production planning, supported by a structured data backbone designed for long-term AI expansion.