🛡️ Process Capability, Cpk Analyzer

A statistical tool modeled to measure process capability and estimate production yield.

Key Features

• Numbered Data Entry: A sidebar tracker providing live "Point #" feedback (up to 60 points) to help users stay organized during manual key-in.
• Statistical Metrics: Calculates Mean, Standard Deviation (ϭ), Cp, and Cpk.
• PPM Analysis: Provides a detailed breakdown of "Defects per Million" (PPM) occurring above the USL and below the LSL.
• Automated Audit Reports: Generates a professional PDF containing:
  o Project summary and spec limits.
  o Individuals Control Chart and Capability Histogram.
  o A full Measurement Data Log for audit transparency.

---

🛠️ Installation & Setup

1. Requirements Ensure you have Python 3.8+ installed. Create a requirements.txt file with the following: Plaintext streamlit easyocr numpy pandas matplotlib scipy reportlab Pillow

2. Local Deployment Bash Clone the repository git clone https://github.com/your-repo-link

Install dependencies pip install -r requirements.txt

Run the Export QC App streamlit run export_qc.py

Run the Cpk Analyzer streamlit run cpk_analyzer.py

3. System Dependencies (Streamlit Cloud) If deploying to a cloud environment, create a packages.txt file to support the OCR engine: Plaintext libzbar0

---

📈 Quality Standards The Cpk Analyzer follows standard automotive quality benchmarks:

• Cpk < 1.00: Process is not capable.
• 1.00 ≤ Cpk < 1.33: Process is marginally capable.
• Cpk ≥ 1.33: Process is capable (Standard requirement).