Intelligent Supply Chain
AI-assisted logistics and inventory optimization platform.
Overview
Intelligent Supply Chain is a cloud-native, event-driven microservices platform built to simulate how modern distributed supply chain systems operate internally. The project focuses on distributed systems, event-driven architecture, Kubernetes orchestration, AWS EKS deployment, observability, and production-style infrastructure. It was first deployed locally using Minikube for Kubernetes testing and debugging, and later deployed on AWS EKS. :contentReference[oaicite:1]{index=1}
Problem Statement
Supply chain platforms need to coordinate users, orders, inventory, payments, shipments, and returns across multiple business functions. As the system grows, a monolithic backend becomes harder to scale, monitor, and evolve. This project explores how those responsibilities can be separated into independently deployable microservices that communicate through events, while remaining observable and deployable in a cloud-native environment. :contentReference[oaicite:2]{index=2}
Features
- Cloud-native microservice architecture
- API Gateway for centralized routing
- Service discovery with Eureka
- Event-driven communication with Kafka
- PostgreSQL database per service
- Kubernetes deployment
- Local Minikube support
- AWS EKS deployment
- Prometheus monitoring
- Grafana dashboards
- Zipkin distributed tracing
- Helm-based packaging
Core Services
The system is composed of independently deployable services, each responsible for a single business capability.
API Gateway
Central entry point for all external client requests.
Discovery Server
Provides Eureka-based service discovery.
User Service
Manages users and user-related data.
Order Service
Creates and manages orders.
Inventory Service
Handles product inventory.
Payment Service
Processes payments.
Shipment Service
Manages shipment workflows.
Return Service
Handles product returns.
Technology Stack
Technologies, frameworks, infrastructure, and tooling used throughout the development of this project.
Backend
Java 21 • Spring Boot • Spring Cloud • Spring Data JPA • Spring Cloud Gateway • Eureka Discovery Server
Database
PostgreSQL
Messaging
Apache Kafka
Infrastructure
Docker • Kubernetes • Minikube • AWS EKS • Helm
Developer Tools
Prometheus • Grafana • Zipkin • Git • Docker Compose • kubectl
Engineering Decisions
Architectural choices made to improve scalability, maintainability, reliability, and long-term evolution of the system.
Microservice Architecture
The platform is split into independent services so user, order, inventory, payment, shipment, and return concerns can evolve separately.
Event-Driven Workflow
Kafka is used to coordinate asynchronous workflows across services, reducing coupling and making the system closer to a real distributed backend.
Service Discovery
Eureka is used so services can find each other dynamically instead of relying on hardcoded addresses.
Kubernetes Deployment
The project was designed to run locally on Minikube and later on AWS EKS, which makes it useful for cloud-native deployment practice.
Observability First
Prometheus, Grafana, and Zipkin were included so the platform can be monitored and traced across service boundaries.
Helm Packaging
Helm charts were used to make deployment and configuration management more repeatable across environments.
Challenges
The most significant engineering challenges encountered while building the system.
Splitting supply chain workflows into clear service boundaries.
Coordinating request flow across multiple services.
Managing asynchronous events with Kafka.
Deploying and debugging the system on Kubernetes.
Keeping the platform observable during local and cloud deployment.
What I Learned
Key technical concepts and engineering practices gained during the implementation.
How cloud-native distributed systems are structured.
How Kafka supports event-driven workflows.
How Kubernetes changes deployment and debugging.
How observability tools help trace service interactions.
How different services can be modeled around business capabilities.
Future Improvements
Potential enhancements that could improve scalability, reliability, and maintainability.
Add CI/CD automation.
Improve scaling experiments.
Add advanced analytics for supply chain operations.
Introduce stronger security hardening.
Explore predictive inventory intelligence.