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Inventory Management System Design ​

1. Business Requirements ​

Functional Requirements ​

  • User registration and authentication (warehouse staff, managers, admins)
  • Product catalog management (add, update, remove items)
  • Real-time inventory tracking (stock levels, locations)
  • Stock in/out operations (receiving, shipping, transfers)
  • Alerts for low stock, overstock, and expiry
  • Mobile-ready UI for on-the-go access
  • Generate inventory reports and trends (e.g., fast-moving items)
  • Audit logs for all inventory changes
  • Multi-location/warehouse support
  • Role-based access control

Non-Functional Requirements ​

  • 99.9% availability (max ~8.76 hours downtime/year)
  • Scalability to support multiple warehouses and thousands of SKUs
  • Fast response times (<300ms for most requests)
  • Secure data storage and access control
  • Backup and disaster recovery
  • Audit logging and monitoring
  • Mobile responsiveness

Out of Scope ​

  • Integration with external ERP or accounting systems
  • Automated procurement or supplier management
  • Payment processing

2. Estimation & Back-of-the-Envelope Calculations ​

  • Warehouses: 10
  • Users: 500 (staff, managers, admins)
  • SKUs: 100,000
  • Daily transactions: ~10,000 (stock in/out, queries, alerts)
  • Peak concurrent users: ~200
  • Data size:
    • Product catalog: 100,000 × 1 KB ≈ 100 MB
    • Inventory records: 1M × 0.5 KB ≈ 500 MB
    • User data: 500 × 2 KB ≈ 1 MB
    • Audit logs: 10M × 0.2 KB ≈ 2 GB
    • Total DB size: ~3 GB (excluding logs, backups)
  • Availability:
    • 99.9% = 8.76 hours/year downtime max
    • Use managed DB, multi-AZ deployment, health checks, auto-scaling

3. High Level Design (Mermaid Diagrams) ​

Component Diagram ​

mermaid
flowchart LR
  User[User (Web/Mobile)]
  LB[Load Balancer]
  App[Application Server]
  DB[(Database)]
  Cache[Cache (Redis)]
  Analytics[Analytics Engine]
  Alert[Alert Service]

  User --> LB --> App
  App --> DB
  App --> Cache
  App --> Alert
  App --> Analytics
  Analytics --> DB

Data Flow Diagram ​

mermaid
sequenceDiagram
  participant U as User
  participant A as App Server
  participant D as Database
  participant C as Cache
  participant L as Alert Service

  U->>A: Stock Out Operation
  A->>C: Check Stock Level
  C-->>A: Hit/Miss
  A->>D: Update Inventory Record
  D-->>A: Success/Fail
  A->>L: Trigger Alert if Low Stock
  A-->>U: Response

Key Design Decisions ​

  • Database: Relational DB (e.g., PostgreSQL) for transactional integrity and strong consistency
  • Cache: Redis for fast lookups (stock levels, sessions)
  • Analytics: Batch or streaming (e.g., Kafka + Spark, or managed cloud analytics)
  • Deployment: Cloud-based, multi-AZ, managed services for high availability
  • Alerting: Email/SMS/push via third-party service (e.g., Twilio, Firebase)

4. Conceptual Design ​

Entities ​

  • User: id, name, email, role, warehouse_id, status
  • Warehouse: id, name, location
  • Product: id, name, sku, description, category, unit, status
  • Inventory: id, product_id, warehouse_id, quantity, last_updated
  • StockTransaction: id, product_id, warehouse_id, type (in/out/transfer), quantity, timestamp, user_id
  • Alert: id, product_id, warehouse_id, type (low/overstock/expiry), message, created_at, status

Key Flows ​

  • Stock In/Out:
    1. User initiates stock operation
    2. App checks cache for stock level, falls back to DB
    3. Updates Inventory and creates StockTransaction
    4. Triggers alert if thresholds are crossed
  • Alerts:
    • System monitors inventory and triggers alerts for low/overstock/expiry
  • Analytics:
    • Periodic jobs aggregate inventory and transaction data for trends (e.g., fast-moving items)

Security ​

  • Role-based access control (RBAC)
  • Input validation, rate limiting
  • Encrypted connections (HTTPS)
  • Regular backups and audit logs

5. Bottlenecks and Refinement ​

Potential Bottlenecks ​

  • Database contention (high read/write during peak):
    • Use read replicas, caching, and DB connection pooling
  • Alert delivery:
    • Use async queues for alert notifications
  • Analytics workload:
    • Offload to separate analytics engine, run during off-peak
  • Single region failure:
    • Deploy across multiple availability zones/regions

Refinement ​

  • Monitor system metrics and auto-scale app servers
  • Regularly test failover and backup restores
  • Optimize queries and indexes for frequent operations
  • Consider sharding if SKU/warehouse volume grows significantly

This design provides a scalable, highly available, and mobile-ready inventory management system with real-time alerts and robust operational practices.