Logistics Intelligence Case Study | Operations Automation
Back to Case StudiesLogistics Intelligence Hub
Real-time operational intelligence for shipment tracking, delay detection, and carrier performance optimization.
This Operations AI automation case study explores Logistics Intelligence Hub. Tags: Operations, AI Implementation, Reporting, Integration, Make.com, Retool, AI Automation, Workflow Automation.
2-6 hours earlier
Delay Detection
-20%
SLA Breaches
Full
Operational Visibility
Overview
A logistics provider needed a real-time operational intelligence system to track shipments, detect delays, analyze carrier performance, and optimize routing. The system enabled delay detection 2-6 hours earlier than before.
Challenges
Fragmented carrier APIs and formats
Manual delay detection
No predictive analytics
Operators lacked real-time visibility
Complex multi-carrier environment
What We Delivered
Streaming data pipeline with Kafka event ingestion
Real-time ETL into Snowflake with standardized schema
Predictive routing engine with ML delay prediction
Custom control tower application with live shipment map
Carrier performance metrics dashboard
Automated Slack/Teams notifications with escalation workflows
Tech Stack
Kafka, Snowflake, FastAPI, Python ML models, Next.js, AWS Lambda, Airflow
Tags
Results
2-6 hours earlier
Delay Detection
-20%
SLA Breaches
Full
Operational Visibility
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