process_improvement.py

process_improvement.py is a Python library that performs calculations and generates figures that facilitate an understanding of variation in business and manufacturing processes. The primary tool of this library is the process behavior chart (control chart) for individual values and a moving range called the XmR chart. Other functions include:

  • Capability histogram

  • Process capability index calculations

  • Combo chart

  • Limit chart

  • Limit chart network analysis

  • Network analysis

  • Taguchi loss function

  • Economic loss due to poor quality calculation

  • XmR chart

  • XmR chart comparison

To learn more about these functions visit the GitHub repository: GitHub.com/jimlehner/process-improvement.

Figure 1. XmR chart of electrode overhang.

Process_improvement.py on PyPi.org

Table of contents

process_improvement.py on GitHub
Capability histogram.png
Combo Chart.png
Limit Chart Network Analysis.png
Limit Chart.png
Network Analysis.png
Taguchi Loss Function.png
XmR Chart.png
XmR Comparison.png

Installation

The library is hosted on PyPi.org, thus it can be installed via pip using the command:

pip install process-improvement

Alternatively, the library can be installed from GitHub using the command:

pip install git+https://github.com/jimlehner/process_improvement.py

Additional information

Parts of an XmR chart: Invented by Dr. Walter Shewhart in the mid-1920s at Bell Telephone Laboratories, PBCs are composed of two charts: the X-chart and the mR-chart. Where the X-chart bounds the variation associated with individual values the mR-chart bounds the value-to-value variation. This is made possible through the calculation of a trio of limits known as process limits. The upper process limit (UPL) and lower process limit (LPL) are used on the X-chart. The upper range limit (URL) is used on the mR-chart.

  • Two types of variation: Inherent in the characterizations of predictable and unpredictable is the type of variation influencing process behavior. A predictable process is influenced by only routine causes of variation. An unpredictable process is influenced by both routine causes of variation and assignable causes of variation.

  • Improvement:

    • Predictable: To improve a predictable process routine causes of variation must be identified, understood, and mitigated. This requires fundamental changes to the process must be made. These include, but are not limited to, changes to raw materials, adjustment to system settings, redesign of stations, redesign of software, calibration of measurement systems.

    • Unpredictable: To improve an unpredictable process assignable causes of variation must be identified, understood, and eliminated. To begin this process, an investigation into values that fall outside the process limits on the PBC must be performed.

  • For those unfamiliar with process behavior charts (control charts) that are interested in learning more click here.

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