join now

Logistics – Shipment Tracking & Customer Notification Automation

Architecture Level 1

image

Automation High-Level Overview

Architecture Level 2

image

Automation Detailed Components

Architecture Level 3

image

Automation Technical Deep Dive

Shipment Tracking & Customer Notification Automation

1. Executive Summary

The Shipment Tracking & Customer Notification Automation system is a real-time, multi-carrier tracking platform that continuously monitors FedEx, UPS, and DHL shipments via polling + webhooks, instantly detects delays/exceptions, updates internal dashboards, and sends proactive alerts to logistics teams (Slack) and customers (email/SMS). Built with Python, FastAPI, Celery/Redis, and Slack bots, it cuts manual tracking effort by 80 %, delivers notifications in <10 seconds, handles 10,000+ active shipments/day, and was delivered in 10 weeks (1 Sep – 19 Nov 2025).

2. Architecture Overview

Hybrid event-driven + scheduled pipeline:

Registry: New shipments are stored in PostgreSQL; Celery beat schedules polls every 5 min or receives carrier webhook pushes.

Processing: FastAPI processes standardized status parsing and exception detection logic.

Alerting: Detected issues trigger Slack bots for teams and SendGrid/Twilio for customers.

Infrastructure: Containerized via Docker, deployed on AWS ECS/Fargate with automatic retries and dead-letter queues.

3. Technology Stack

  • Backend: Python 3.11 + FastAPI
  • Task Queue: Celery + Redis + RabbitMQ
  • Database: PostgreSQL (Shipment registry & history)
  • APIs: FedEx, UPS, DHL (Webhooks & Polling)
  • Messaging: Slack Bolt SDK, SendGrid (Email), Twilio (SMS)
  • Cloud Infra: Docker, AWS ECS/Fargate, CloudWatch
  • Observability: Prometheus & Grafana

4. Automation Model and Features

Real-time Sync: Webhook-first approach with fallback polling ensures no missed updates.

Detection Engine: Configurable rules for late ETAs, exception codes, and stuck shipments.

  • Notifications: Immediate Slack cards to #logistics-alerts with "Resolve" buttons and automated customer templates.
  • Resilience: Auto-retry logic and circuit breakers per carrier for 99.4% detection accuracy.

    • 5. Data Processing

    Shipment records trigger Celery tasks that fetch raw carrier payloads. An adapter layer normalizes data to an internal schema, comparing it against SLA rules and previous states. Changes enrich messages for notification workers while logging full history. The system is rate-limit aware with masked PII for security.

    6. Project Timeline (10 Weeks)

    Timeline: September 1 – November 19, 2025

    • Weeks 1–2: Discovery and carrier API sandbox access.
    • Weeks 3–4: System design, schema definition, and detection rules.
    • Weeks 5–8: Core development of adapters, FastAPI, and Slack bot.
    • Week 9: UAT with 500 simulated and real test shipments.
    • Week 10: Production rollout, monitoring setup, and handover.

    7. Testing & Deployment

    Testing: Unit + integration tests (pytest), contract testing against carrier sandboxes, and chaos testing for API failures. Achieved 99.4% accuracy during UAT.

    Deployment: Blue-green rollout on AWS ECS via GitHub Actions. Rollback capability in <3 minutes by switching task definitions.

    8. Monitoring & Maintenance

    Full observability via Grafana (latency, queue depth, delivery success) and PagerDuty integration for on-call rotations. Maintenance includes monthly carrier performance reviews and a one-click redeploy pipeline. Current uptime: 99.99%.

    9. Roles & Responsibilities

    Methodology: Agile with mandatory code reviews and daily stand-ups.

    • 🚀 Project Manager: Timeline, stakeholder sync, risk & budget.
    • ⚙️ Backend Developers (3): FastAPI, Celery, carrier adapters, Slack bot.
    • 🛠️ DevOps Engineer: Docker, AWS infra, CI/CD, monitoring.
    • 🧪 QA Tester: Test scenarios, regression suite, accuracy validation.