Industrial Production Data System
Full-stack production data system for industrial manufacturing. Tracks multi-stage SOP workflows, quality control inspections, assembly management, and team operations across factory departments with role-based access.
🔒 Private / client work — the live site and code are under NDA. Happy to walk through the architecture and my role on a call.
Project Overview
A comprehensive Django-based production management system built for an industrial manufacturer. It serves as the central hub for tracking the entire manufacturing lifecycle — from raw workpieces through 7 sequential SOP stages, quality inspections, and final assembly.
Key Features
- Multi-stage SOP workflow engine tracking workpieces across 7+ production phases
- Quality control system with visual inspections, surface treatment checks, and NCR tracking
- Assembly management with photo documentation stored in S3
- Role-based dashboards for QC managers, production planners, and department staff
- Shift and team management across multiple factory departments
- CSV/Excel import from legacy systems with batch processing
- PDF and Excel report generation with ReportLab and OpenPyXL
- REST API for external integrations and data access
- Full i18n support with django-rosetta
- Sentry error tracking and health check monitoring
Architecture
The application runs on Django 4.2 with PostgreSQL (AWS RDS in production) and uses MinIO/S3 for document and photo storage across multiple buckets. Deployed on AWS Elastic Beanstalk with Docker containers, automated migrations, and Apache reverse proxy. The codebase enforces strict typing with MyPy and automated formatting via pre-commit hooks.