Hotel Kitchen Supply & Production Management

Tech stack (concise)

• Django & Django REST Framework (multi-tenant design)
• PostgreSQL for relational data
• Redis (streams) and WebSockets for real-time dashboards
• Celery for background jobs (batch orders, stock reconciliation)
• VPS / Nginx + Gunicorn deployment; SSL hardening and database provisioning

Project summary

Built a full-featured portal for a hotel group (three branches) comprising separate administration and kitchen-staff interfaces. The system converts raw inventory inputs into meal outputs using a dynamic, rule-driven recipe model and automates production calculations based on guest check-in/check-out events.

Core capabilities

• Admin & Staff Portals: distinct UIs for management (menu & recipe configuration, user management) and kitchen staff (ingredient entry, order processing).
• Dynamic recipe builder: compose each meal from components and quantities (e.g., cheese, eggs, milk in grams; beverages and condiments). Recipes are fully configurable and versioned.
• Automatic meal generation: the system translates inventory levels into available meal counts and assigns meals to guests by name, enforcing configurable constraints (such as max meals per guest per day).
• Lifecycle automation: real-time recalculation of available meals on guest check-in / check-out events to keep production and inventory aligned with occupancy.
• Operational dashboards: staff-facing panels for current orders, inventory alerts and shift handovers; admin dashboards for reporting and procurement planning.

Deployment & operations

Deployed on a VPS with a public IP. Server stack includes Nginx as reverse proxy, Gunicorn for application workers, and a dedicated PostgreSQL instance. SSL was configured and hardened; backup and basic monitoring were set up. Background tasks run via Celery with Redis as broker. Access to admin and staff panels is secured and role-restricted.

Operational benefits & impact

• Reduced manual effort in production planning by converting raw ingredient data directly into meal counts and ordered outputs.
• Improved procurement accuracy with consolidated supplier orders based on projected consumption.
• Faster kitchen workflows through clear staff dashboards and automated order lists tied to guest reservations.

Implementation notes

I developed the backend in Django, the frontend in Vanilla JavaScript for lightweight staff UIs, and provisioned the Postgres database and VPS environment. The entire project was completed by me in approximately one and a half months.