Six-Sigma based analysis of manufacturing data
Project description
Purpose
To provide analysis tools and metrics useful in manufacturing environments.
I am slowly generating the documentation and, as that is maturing, I will begin to move information
from this readme.md
into that location. If you don't find something here, head over to the
documentation.
Project Maturity
Every effort is being made to ensure that the results are accurate, but the user is ultimately responsible for any resulting analysis.
The API should not be considered stable until v1.0 or greater. Until then, breaking changes may be released as different API options are explored.
During the v0.X.X
versioning, I am using the package in my own analyses in order to find any bugs. Once
I am reasonably satisfied that the package is feature complete, usable, and bug-free, I will break out
the v1.X.X
releases.
Installation
To install from pypi
:
pip install manufacturing
To install from source download and install using poetry:
poetry install
Usage
Visualizations with Jupyter Notebooks
Visualizations work approximately as expected within a jupyter notebook.
data = np.random.normal(0, 1, size=30) # generate some data
manufacturing.ppk_plot(data, lower_specification_limit=-2, upper_specification_limit=2)
There is a sample jupyter notebook in the examples directory.
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
RoadMap
Items marked out were added most recently.
- ...
Add use github actions for deploymentTransition topoetry
for releasesAddI-MR Chart
(seeexamples/imr_chart.py
)AddXbar-R Chart
(subgroups between 2 and 10)AddXbar-S Chart
(subgroups of 11 or more)- Back with testing
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