Enterprise Inventory Management Platform

Pain Points

• Stockouts discovered only when orders could not be fulfilled
• Overstocking tied up working capital in slow-moving goods
• Reporting took days because data was scattered across spreadsheets
• No alerts — teams found out about low stock by walking the warehouse
• No forecasting — purchasing decisions were entirely manual

Project Goals

• Provide real-time inventory visibility across all locations
• Automate low-stock and threshold alerts via Telegram
• Implement AI-assisted demand forecasting
• Generate reports in seconds, not days
• Ensure alert delivery reliability even during failures

Requirements Gathering

Interviewed warehouse managers, procurement leads, and finance teams. Discovered that the biggest pain point was not tracking itself but the lack of timely alerts and the inability to forecast demand. This shifted the architecture toward event-driven design.

Architecture Decisions

• Chose event-driven architecture with RabbitMQ for reliable event delivery
• Used BullMQ for background job processing (reports, forecasting, alerts)
• Redis for caching inventory reads — dashboards are read-heavy
• Telegram Bot for alerts because it required no additional app for users
• AI forecasting as a separate service consuming inventory events

Database Schema

API Design

• RESTful inventory endpoints with optimistic concurrency control
• WebSocket channel for real-time dashboard updates
• Event publisher for stock changes consumed by alert and forecast services
• BullMQ job API for long-running report generation

Authentication Strategy

JWT authentication with role-based access for warehouse, procurement, and admin users. API keys for service-to-service communication between the forecast service and core platform.

Technology Selection

• NestJS — modular architecture with clean event handling
• RabbitMQ — battle-tested message broker with delivery guarantees
• BullMQ — Redis-based job queue for background processing
• Redis — caching and job queue backend
• Telegram Bot API — instant alerts without building a mobile app

Trade-offs

• RabbitMQ over Redis Pub/Sub: added infrastructure but guaranteed delivery
• Telegram over email/push: less customizable but instant and zero-friction
• Optimistic locking over pessimistic: better throughput for mostly-read inventory

Performance Optimizations

• Redis caching reduced dashboard load time by 60%
• BullMQ background jobs reduced report processing time by 80%
• Event-driven architecture decoupled alerts from core transactions
• WebSocket updates batched to avoid flooding the dashboard

Security Considerations

• JWT authentication with role-based access control
• Service-to-service API keys for internal communication
• Input validation on all stock transaction endpoints
• Audit logging for all inventory adjustments

Scalability Planning

RabbitMQ and BullMQ allow horizontal scaling of consumers. Redis can be clustered for cache and job queue scaling. The forecast service scales independently. Read replicas can serve dashboard queries.

Deployment Strategy

Docker Compose for development. Production deployed with CI/CD, separate containers for API, workers, and forecast service. Health checks and automatic restarts for queue consumers.

Lessons Learned

• Event-driven architecture is worth the complexity when reliability matters
• Telegram bots are a surprisingly effective B2B notification channel
• AI forecasting needs guardrails — never auto-order without human review
• Optimistic locking beats pessimistic for read-heavy inventory systems

Future Improvements

• Multi-warehouse transfer optimization with route planning
• Supplier integration for automated purchase orders
• Mobile app for warehouse barcode scanning
• Advanced ML forecasting with seasonality and market trends