Manufacturing Statistical Analysis Tool

A comprehensive Python tool for manufacturing data analysis using Statistical Process Control (SPC) techniques. Designed for engineers with entry-level statistical knowledge, providing Minitab-like functionality.

Features

• Process Capability Analysis: Cp, Cpk, Cpu, Cpl, Pp, Ppk, Cpm, Cpkm calculations
• Distribution Fitting: Normal and Weibull distribution analysis
• Statistical Tests: Mann-Whitney U, Kolmogorov-Smirnov tests
• Multivariate Analysis: PCA, bivariate distribution plots
• Visualization: Multi-vari charts, box plots, normal probability plots
• Tolerance Intervals: Statistical tolerance calculation

Quick Start

1. Installation

pip install pandas numpy matplotlib seaborn scipy scikit-learn plotly

2. Launch Jupyter Notebook

jupyter notebook Statistical_Analysis.ipynb

Or use the provided batch file:

run_jupyter.bat

3. Basic Usage

# Load your data
pc = read_csv_data('data/sample_manufacturing_data.csv', 'Temperature')

# Set specification limits
pc.set_specification_limits(usl=26.0, lsl=24.0, target=25.0)

# Calculate capability
capability_results = pc.calculate_capability()
print(capability_results)

Project Structure

Statistical_Analysis/
├── Statistical_Analysis.ipynb    # Main analysis notebook
├── data/                         # Sample data files
│   ├── sample_manufacturing_data.csv
│   ├── basedata.txt
│   └── basedata_deviation.txt
├── utilities/                    # Helper scripts
│   ├── createrandomdata.py      # Generate test data
│   └── data_deviation.py        # Create data variations
├── examples/                     # Example outputs
│   └── basedata.png             # Sample analysis plot
├── run_jupyter.bat              # Windows batch file to start Jupyter
└── export.bat                   # Export notebook to HTML

Sample Data

The data/ directory contains sample manufacturing data:

• sample_manufacturing_data.csv: Multi-variable manufacturing dataset
• basedata.txt: Simple baseline data for testing
• generated_data.csv: Generated test data

Utilities

Generate Test Data

python utilities/createrandomdata.py

Create Data Variations

python utilities/data_deviation.py -h

Key Analysis Methods

Process Capability

# Calculate Cp, Cpk, Pp, Ppk
capability = pc.calculate_capability()

Distribution Analysis

# Fit normal and Weibull distributions
pc.fit_distributions()

Statistical Tests

# Mann-Whitney U test
result = pc.mann_whitney_test(group1, group2)

# Kolmogorov-Smirnov test
result = pc.ks_test(data, distribution)

Visualization

# Multi-vari chart
pc.create_multivari_chart()

# Normal probability plot
pc.normal_probability_plot()

Requirements

• Python 3.8+
• pandas
• numpy
• matplotlib
• seaborn
• scipy
• scikit-learn
• plotly

Requirements

• Python 3.8+
• pandas
• numpy
• matplotlib
• seaborn
• scipy
• scikit-learn
• plotly

Version History

See CHANGELOG.md for detailed version history and release notes.

License

MIT License - See LICENSE file for details.

Support

For questions or issues, please refer to the examples in the notebook or create an issue in the repository.