Environmental Pneumatics Inventory Tracking & Reporting App

A Python/PostgreSQL inventory application I independently designed, built, and used in a real manufacturing workflow while working as a CNC laser operator at Environmental Pneumatics in Oakland, Tennessee.

Why I Built It

Sheet-metal availability directly affected laser-production work. I wanted a faster, more structured way to track what material was on hand rather than relying only on manual observation and informal records.

I built this system to track sheet metal by attributes such as material type, gauge/thickness, dimensions, storage location, and quantity.

The longer-term idea was broader shop integration. My employment ended before that full rollout was completed, so I do not describe this as a shop-wide production deployment. However, I did personally use the database and application in my laser-operating work to track the material available to me.

What It Does

• Create, read, update, and delete inventory records
• Store and query structured inventory data in PostgreSQL
• Track sheet metal by type, gauge/thickness, dimensions, location, and quantity
• Validate inventory inputs
• Generate and print barcode labels
• Filter and sort stored material
• Export CSV/XLSX data for reporting
• Export data for ProNest-related workflows
• Back up and restore inventory data
• Maintain indexed fields for frequently queried inventory attributes

Technology

• Python — application and workflow logic
• PostgreSQL / SQL — structured inventory storage and querying
• Tkinter — desktop user interface
• ReportLab / Pillow — barcode and printable-output workflows
• CSV / XLSX — data exchange and reporting
• ProNest-related exports — support for the CNC laser workflow

Why It Matters

This project connects my manufacturing background directly to software development.

I wasn't building an abstract portfolio exercise. I was operating CNC laser equipment, seeing an inventory problem in my own work, and building software to make that workflow more structured and useful.

It demonstrates:

• translating an operational problem into software requirements
• relational data modeling
• SQL query design
• inventory data validation
• CRUD application development
• reporting/export workflows
• barcode tooling
• backup/recovery thinking
• iterative development informed by real use

Representative Data Model

The inventory system tracks fields such as:

• barcode
• shelf/storage location
• thickness/gauge
• metal type
• dimensions
• quantity
• sheet length and width
• date
• usable scrap status

Frequently searched attributes are indexed to support practical lookup workflows.

Project Structure

EP_Inventory_Management/
├── db/
│   ├── config.py
│   ├── connection.py
│   └── queries.py
├── services/
│   ├── inventory_service.py
│   ├── export_service.py
│   ├── backup_service.py
│   └── barcode_service.py
├── utils/
│   └── formatting.py
├── inventory_import.py
├── Inventory_Management_Fixed.py
└── requirements.txt

Running Locally

Requirements:

• Python 3.11+
• PostgreSQL 13+
• dependencies in requirements.txt

Create a local PostgreSQL database, configure the local connection, apply the inventory schema, install the Python dependencies, and run:

python Inventory_Management_Fixed.py

Do not commit local database credentials.

Potential Next Steps

• add inventory trend and reorder analytics
• add production-consumption history
• add dashboard reporting
• formalize roles/authentication
• further automate ProNest/material workflows

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Built by Zachary Maness as an independently developed manufacturing/inventory tool used during his work at Environmental Pneumatics.