as a manufacturer at Palladium Dynamics, we have implemented a robust system to manage our mezzanine floor production, inventory, and sales data using Python and relational databases. Here’s an approach that has worked well for us:

1. Database Design:

   • We use PostgreSQL to maintain structured data for inventory, BOM (Bill of Materials), production orders, and sales.

   • Core tables include:

     • Materials (raw materials, components)

     • Inventory (current stock levels, locations)

     • ProductionOrders (linked to BOM and inventory)

     • SalesOrders (linked to finished products)

     • MaintenanceRecords (for installed mezzanine floors)

   • Relationships ensure real-time traceability from raw materials → production → sales.

2. Real-time Inventory Updates:

   • Python scripts using SQLAlchemy interact with the database to automatically update inventory when production or sales orders are processed.

   • For larger operations, a message queue (like RabbitMQ or Kafka) can be used to sync inventory changes in real time across multiple systems.

3. Python Frameworks & Tools:

   • Pandas: For data analysis and reporting.

   • SQLAlchemy / Django ORM: For smooth database interactions.

   • Plotly / Matplotlib: For production and sales dashboards.

   • FastAPI / Flask: To build internal APIs for real-time tracking.

4. Best Practices:

   • Maintain separate tables for raw vs finished inventory.

   • Use foreign keys and constraints to prevent inconsistencies.

   • Implement versioning for BOMs to track changes in mezzanine floor designs.

   • Automate alerts for low stock or delayed production orders.

Using this approach, Palladium Dynamics has been able to streamline production, optimize inventory, and improve order tracking for our mezzanine floor systems.