🚀 IBM Sterling OMS Next Gen
Complete Interactive Cheat Sheet
📋 Overview & Architecture
🎯 What is IBM Sterling OMS Next Gen?
A cloud-native order management system built on microservices architecture with containerized deployment using Docker and Kubernetes. Features API-first approach with GraphQL and REST APIs, event-driven architecture with real-time processing capabilities.
🏗️ Key Architectural Components
| Component | Description | Key Features |
|---|---|---|
| Order Hub | Central order processing engine | Multi-channel capture, orchestration, routing |
| Inventory Hub | Real-time inventory management | ATP calculations, allocation, tracking |
| Fulfillment Hub | Order fulfillment orchestration | Wave planning, pick optimization, shipping |
| Customer Hub | Customer data management | Profile management, preferences, history |
| Partner Hub | Supplier/vendor integration | EDI, API integration, collaboration |
| Analytics Hub | Real-time analytics and reporting | KPI dashboards, trends, insights |
⚡ Core Features
📦 Order Management
- Multi-channel order capture
- Order orchestration and routing
- Split shipment handling
- Backorder management
- Returns and exchanges
📊 Inventory Management
- Real-time inventory visibility
- ATP calculations
- Safety stock management
- Multi-location tracking
- Demand forecasting
🚚 Fulfillment Operations
- Wave planning optimization
- Pick path optimization
- Shipment consolidation
- Carrier integration
- Track and trace
🏗️ Technical Architecture
🔧 Technology Stack
| Layer | Technology | Purpose |
|---|---|---|
| Runtime | Java 11+, Node.js | Application execution environment |
| Containers | Docker, Kubernetes | Containerization and orchestration |
| Databases | PostgreSQL, MongoDB, Redis | Data persistence and caching |
| Message Queue | Apache Kafka, RabbitMQ | Event streaming and messaging |
| API Gateway | Kong, Istio | API management and routing |
| Monitoring | Prometheus, Grafana, ELK | Observability and logging |
🏗️ Microservices Architecture
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Order Service │ │Inventory Service│ │Fulfillment Svc │
│ │ │ │ │ │
│ • Create Orders │ │ • Track Stock │ │ • Wave Planning │
│ • Update Status │ │ • ATP Check │ │ • Pick Optimize │
│ • Process Mods │ │ • Allocation │ │ • Ship Orders │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
└───────────────────────┼───────────────────────┘
│
┌─────────────────┐
│ Event Bus │
│ (Kafka/MQ) │
│ │
│ • Order Events │
│ • Inventory Evt │
│ • Ship Events │
└─────────────────┘
🔌 API Reference
📡 GraphQL Endpoints
# Order Queries
query getOrder($orderId: ID!) {
order(id: $orderId) {
id
status
orderNumber
items {
sku
quantity
price
status
}
customer {
id
email
name
}
shipping {
address
method
trackingNumber
}
}
}
# Create Order Mutation
mutation createOrder($input: OrderInput!) {
createOrder(input: $input) {
id
status
orderNumber
estimatedDelivery
}
}
# Update Inventory
mutation updateInventory($sku: String!, $quantity: Int!, $location: String!) {
updateInventory(sku: $sku, quantity: $quantity, location: $location) {
sku
availableQuantity
reservedQuantity
location
}
}
🌐 REST API Endpoints
| Method | Endpoint | Description | Example |
|---|---|---|---|
| GET | /api/v1/orders/{orderId} | Retrieve order details | GET /api/v1/orders/12345 |
| POST | /api/v1/orders | Create new order | POST /api/v1/orders |
| PUT | /api/v1/orders/{orderId} | Update order | PUT /api/v1/orders/12345 |
| GET | /api/v1/inventory/{sku} | Get inventory levels | GET /api/v1/inventory/ABC123 |
| POST | /api/v1/fulfillment/shipments | Create shipment | POST /api/v1/fulfillment/shipments |
# Create Order Example
POST /api/v1/orders
Content-Type: application/json
Authorization: Bearer {token}
{
"customerId": "CUST-12345",
"orderType": "SALES",
"channel": "WEB",
"items": [
{
"sku": "ABC123",
"quantity": 2,
"unitPrice": 29.99
},
{
"sku": "XYZ789",
"quantity": 1,
"unitPrice": 49.99
}
],
"shipping": {
"method": "STANDARD",
"address": {
"street": "123 Main St",
"city": "New York",
"state": "NY",
"zip": "10001"
}
}
}
⚙️ Configuration & Setup
🔧 Environment Configuration
# application.yml
server:
port: 8080
servlet:
context-path: /oms
spring:
application:
name: sterling-oms-nextgen
datasource:
url: jdbc:postgresql://localhost:5432/omsdb
username: ${DB_USER:omsuser}
password: ${DB_PASSWORD:password}
hikari:
maximum-pool-size: 20
minimum-idle: 5
jpa:
hibernate:
ddl-auto: validate
show-sql: false
kafka:
bootstrap-servers: ${KAFKA_BROKERS:localhost:9092}
consumer:
group-id: oms-consumer-group
topics:
order-events: order.events
inventory-events: inventory.events
fulfillment-events: fulfillment.events
redis:
host: ${REDIS_HOST:localhost}
port: ${REDIS_PORT:6379}
timeout: 2000ms
management:
endpoints:
web:
exposure:
include: health,metrics,info,prometheus
endpoint:
health:
show-details: always
🐳 Docker Configuration
| Component | Image | Port | Environment Variables |
|---|---|---|---|
| OMS Service | sterling-oms:latest | 8080 | DB_HOST, KAFKA_BROKERS, REDIS_HOST |
| PostgreSQL | postgres:13 | 5432 | POSTGRES_DB, POSTGRES_USER, POSTGRES_PASSWORD |
| Kafka | confluentinc/cp-kafka | 9092 | KAFKA_ZOOKEEPER_CONNECT, KAFKA_ADVERTISED_LISTENERS |
| Redis | redis:6-alpine | 6379 | REDIS_PASSWORD (optional) |
# docker-compose.yml
version: '3.8'
services:
oms-service:
image: sterling-oms-nextgen:latest
ports:
- "8080:8080"
environment:
- DB_HOST=postgres
- DB_USER=omsuser
- DB_PASSWORD=omspass
- KAFKA_BROKERS=kafka:9092
- REDIS_HOST=redis
depends_on:
- postgres
- kafka
- redis
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
interval: 30s
timeout: 10s
retries: 3
postgres:
image: postgres:13
environment:
- POSTGRES_DB=omsdb
- POSTGRES_USER=omsuser
- POSTGRES_PASSWORD=omspass
volumes:
- postgres_data:/var/lib/postgresql/data
ports:
- "5432:5432"
kafka:
image: confluentinc/cp-kafka:latest
environment:
- KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181
- KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://kafka:9092
- KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1
depends_on:
- zookeeper
ports:
- "9092:9092"
volumes:
postgres_data:
🔄 Key Business Processes
📋 Order Lifecycle
| Stage | Activities | Systems Involved | Key Metrics |
|---|---|---|---|
| 1. Order Capture | Validation, Pricing, Tax Calculation | Order Hub, Customer Hub | Order Creation Time, Validation Success Rate |
| 2. Inventory Check | ATP Check, Allocation, Reservation | Inventory Hub | ATP Response Time, Allocation Success Rate |
| 3. Payment Processing | Authorization, Fraud Check, Settlement | Payment Gateway, Risk Engine | Authorization Rate, Fraud Detection Rate |
| 4. Fulfillment | Wave Planning, Pick, Pack, Ship | Fulfillment Hub, WMS Integration | Pick Accuracy, Ship Time, Fill Rate |
| 5. Delivery | Track, Deliver, Confirm, Complete | Carrier Integration, Customer Portal | On-Time Delivery, Customer Satisfaction |
🎯 Process Optimization Tips
- Parallel Processing: Run inventory check and payment authorization simultaneously
- Early Allocation: Reserve inventory immediately after order validation
- Smart Routing: Use ML algorithms for optimal fulfillment location selection
- Exception Handling: Implement automated fallback processes for common failures
🔗 Integration Patterns
⚡ Event-Driven Integration
{
"eventId": "evt_001",
"eventType": "ORDER_CREATED",
"timestamp": "2024-01-15T10:30:00Z",
"source": "order-service",
"data": {
"orderId": "ORD-12345",
"customerId": "CUST-67890",
"orderNumber": "WEB-2024-001",
"status": "CREATED",
"totalAmount": 149.97,
"currency": "USD",
"items": [
{
"lineId": "line_001",
"sku": "ITEM-001",
"quantity": 2,
"unitPrice": 49.99,
"totalPrice": 99.98
}
],
"shipping": {
"method": "STANDARD",
"estimatedDelivery": "2024-01-20"
}
}
}
🔗 Integration Endpoints
| Integration Type | Protocol | Use Case | Example |
|---|---|---|---|
| Webhooks | HTTP POST | Real-time notifications | Order status updates |
| REST APIs | HTTP/HTTPS | Synchronous operations | Create/Update orders |
| GraphQL | HTTP/HTTPS | Flexible data queries | Customer portal data |
| Message Queue | Kafka/AMQP | Asynchronous processing | Inventory updates |
| EDI | X12/EDIFACT | B2B transactions | Supplier integration |
📊 Performance & Monitoring
📈 Key Performance Metrics
| Category | Metric | Target | Alert Threshold |
|---|---|---|---|
| Performance | API Response Time (95th percentile) | < 500ms | > 1000ms |
| Throughput | Orders Processed per Hour | > 10,000 | < 5,000 |
| Reliability | System Uptime | 99.9% | < 99.5% |
| Accuracy | Order Processing Error Rate | < 0.1% | > 0.5% |
| Inventory | Inventory Accuracy | > 99.5% | < 98% |
| Fulfillment | Order Fill Rate | > 95% | < 90% |
🔍 Health Check Endpoints
# System Health
GET /actuator/health
Response: {
"status": "UP",
"components": {
"db": {"status": "UP"},
"kafka": {"status": "UP"},
"redis": {"status": "UP"}
}
}
# Application Metrics
GET /actuator/metrics
Response: {
"names": [
"jvm.memory.used",
"http.server.requests",
"kafka.consumer.lag"
]
}
# Custom Business Metrics
GET /actuator/prometheus
Response:
# HELP orders_processed_total Total number of orders processed
# TYPE orders_processed_total counter
orders_processed_total{status="completed"} 12345.0
orders_processed_total{status="failed"} 23.0
📊 Monitoring Dashboard Setup
🎯 Essential Dashboards
- Business KPIs: Order volume, revenue, conversion rates
- System Health: CPU, memory, disk usage, network I/O
- Application Metrics: Response times, error rates, throughput
- Infrastructure: Container health, database connections, message queue lag
- Custom Alerts: Business rule violations, SLA breaches
✅ Best Practices
👨💻 Development
- Use domain-driven design principles
- Implement circuit breaker patterns
- Follow 12-factor app methodology
- Use feature toggles for safe deployments
- Comprehensive testing (unit, integration, e2e)
🚀 Operations
- Automated monitoring and alerting
- Blue-green deployments
- Container orchestration (Kubernetes)
- Disaster recovery procedures
- Regular performance testing
🔒 Security
- OAuth 2.0/JWT authentication
- API rate limiting
- Audit logging
- Regular security scanning
- Data encryption at rest and in transit
🏗️ Architecture Best Practices
| Pattern | Description | Implementation | Benefits |
|---|---|---|---|
| Circuit Breaker | Prevent cascading failures | Hystrix, Resilience4j | System resilience, graceful degradation |
| Event Sourcing | Store events instead of current state | Event store, replay capability | Audit trail, time travel debugging |
| CQRS | Separate read/write models | Command/Query segregation | Optimized performance, scalability |
| Saga Pattern | Distributed transaction management | Choreography/Orchestration | Data consistency, error recovery |
🔧 Troubleshooting Guide
❗ Common Issues & Solutions
| Issue | Symptoms | Possible Causes | Solutions |
|---|---|---|---|
| Order Processing Delays | High processing times, queue backlog | Database contention, inventory service down | Scale services, optimize queries, check dependencies |
| API Timeouts | HTTP 504 errors, slow responses | Network latency, resource constraints | Increase timeouts, scale horizontally, optimize code |
| Inventory Discrepancies | Stock showing incorrect levels | Race conditions, sync issues | Implement proper locking, audit sync processes |
| Message Queue Lag | Events not processed timely | Consumer overload, topic misconfiguration | Increase partitions, scale consumers |
🔍 Diagnostic Commands
# Check Kubernetes pods status
kubectl get pods -n oms-namespace
kubectl describe pod oms-service-xxx
# View application logs
kubectl logs -f deployment/oms-service --tail=100
kubectl logs -f deployment/oms-service --previous
# Check resource usage
kubectl top pods -n oms-namespace
kubectl top nodes
# Database connectivity test
kubectl exec -it postgres-pod -- psql -U omsuser -d omsdb -c "SELECT 1;"
# Kafka consumer lag check
kubectl exec -it kafka-pod -- kafka-consumer-groups.sh \
--bootstrap-server localhost:9092 \
--describe --group oms-consumer-group
# Redis connectivity test
kubectl exec -it redis-pod -- redis-cli ping
# Port forwarding for local debugging
kubectl port-forward svc/oms-service 8080:8080
kubectl port-forward svc/kafka 9092:9092
⚠️ Emergency Response Procedures
- Immediate Assessment: Check system dashboards and alerts
- Isolate Impact: Determine affected services and customers
- Implement Workaround: Route traffic to healthy instances
- Root Cause Analysis: Investigate logs and metrics
- Communication: Update stakeholders and customers
- Post-Incident Review: Document lessons learned
📦 Migration Considerations
🔄 Migration Strategy
| Phase | Activities | Duration | Risk Level |
|---|---|---|---|
| Assessment | Current state analysis, gap identification | 2-4 weeks | Low |
| Planning | Migration roadmap, resource allocation | 2-3 weeks | Low |
| Development | Data mapping, integration development | 8-12 weeks | Medium |
| Testing | Unit, integration, performance testing | 4-6 weeks | Medium |
| Pilot | Limited production deployment | 2-4 weeks | High |
| Full Rollout | Complete system migration | 1-2 weeks | High |
✅ Go-Live Checklist
📋 Pre-Go-Live Validation
| ✅ | Item | Owner | Status |
|---|---|---|---|
| ☐ | Environment provisioning complete | Infrastructure Team | Pending |
| ☐ | Data migration validated | Data Team | Pending |
| ☐ | Integration testing passed | QA Team | Pending |
| ☐ | Performance testing completed | Performance Team | Pending |
| ☐ | Security scanning clear | Security Team | Pending |
| ☐ | Monitoring configured | DevOps Team | Pending |
| ☐ | Rollback plan tested | Release Team | Pending |
📚 Resources & References
📖 Documentation
- IBM Sterling OMS Next Gen Official Docs
- API Reference Guide
- Deployment Guide
- Integration Patterns Guide
- Troubleshooting Manual
🌐 Community & Support
- IBM Sterling Community Forums
- Stack Overflow: ibm-sterling-oms
- GitHub: Sample implementations
- IBM Support Portal
- Developer Slack Channels
🎓 Training Resources
- IBM Sterling OMS Certification
- Online Learning Modules
- Hands-on Labs
- Partner Training Programs
- Webinar Series
🔗 Quick Reference Links
| Resource | Description | URL Pattern |
|---|---|---|
| API Documentation | Interactive API explorer | https://your-oms-instance/swagger-ui |
| Health Dashboard | System health monitoring | https://your-oms-instance/actuator/health |
| Metrics Endpoint | Prometheus metrics | https://your-oms-instance/actuator/prometheus |
| GraphQL Playground | GraphQL query interface | https://your-oms-instance/graphql |
🚀 Quick Start Commands
# Clone starter template
git clone https://github.com/ibm-sterling/oms-nextgen-starter.git
# Run with Docker Compose
docker-compose up -d
# Check system health
curl http://localhost:8080/actuator/health
# Create sample order
curl -X POST http://localhost:8080/api/v1/orders \
-H "Content-Type: application/json" \
-d '{"customerId":"12345","items":[{"sku":"ABC123","quantity":1}]}'
# Monitor logs
docker-compose logs -f oms-service
