manufacturing 1.5.0

Six-Sigma based analysis of manufacturing data

Purpose

To provide analysis tools and metrics useful in manufacturing environments.

Go to the documentation.

Project Maturity

Plots and project are reasonably mature at this point. Calculations have been refined and are in-line with commonly accepted standards.

A major v2.0 update is coming to control charts and will be available in manufacturing.alt_vis module. For instance, instead of using from manufacturing import x_mr_chart, you would use from manufacturing.alt_vis import x_mr_chart. The new API should allow for a greater degree of flexibility with recalculation points and the ability to relabel the axes. Additionally, alternative axis labels will be able to be supplied. These changes will eventually become "the way", but are to be considered experimental until the v2.0 update.

Installation

To install from pypi:

$>pip install manufacturing

Building

This package uses uv to manage the workflow.

$>git clone <this repository>
$>cd manufacturing
$manufacturing/>uv build

Testing

Tests will take a while to run - it is generating several hundred plots in the background.

$>uv run pytest

Usage

Cpk Visualization

The most useful feature of the manufacturing package is the visualization of Cpk. As hinted previously, the ppk_plot() function is the primary method for display of Cpk visual information. First, get your data into a list, numpy.array, or pandas.Series; then supply that data, along with the lower_control_limit and upper_control_limit into the ppk_plot() function.

manufacturing.ppk_plot(data, lower_specification_limit=-2, upper_specification_limit=2)

In this example, it appears that the manufacturing processes are not up to the task of making consistent product within the specified limits.

Zone Control Visualization

Another useful feature is the zone control visualization.

manufacturing.control_chart(data)

There are X-MR charts, Xbar-R charts, and Xbar-S charts available as well. If you call the control_chart() function, the appropriate sample size will be selected and data grouped as the dataset requires. However, if you wish to call a specific type of control chart, use

• x_mr_chart
• xbar_r_chart
• xbar_s_chart
• p_chart

Contributions

Contributions are welcome!

RoadMap

Items marked out were added most recently.

• ...
• Add use github actions for deployment
• Transition to poetry for releases
• Add I-MR Chart (see examples/imr_chart.py)
• Add Xbar-R Chart (subgroups between 2 and 10)
• Add Xbar-S Chart (subgroups of 11 or more)
• Update documentation to reflect recent API changes
• Add p chart
• Add np chart
• Add u chart
• Add c chart
• Add automated testing (partially implemented)

Gallery