Logistics Intelligence Case Study | Operations Automation

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Logistics 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

1

Fragmented carrier APIs and formats

2

Manual delay detection

3

No predictive analytics

4

Operators lacked real-time visibility

5

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

OperationsAI ImplementationReportingIntegrationMake.comRetoolAI AutomationWorkflow Automation

Results

2-6 hours earlier

Delay Detection

-20%

SLA Breaches

Full

Operational Visibility

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