Six-Sigma based analysis of manufacturing data
Project description
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 shortly 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
To install from source download and install using poetry:
$>poetry install
Building
$>poetry update
$>poetry build
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 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)Update documentation to reflect recent API changesAddp chart
- Add
np chart
- Add
u chart
- Add
c chart
- Add automated testing (partially implemented)
Gallery
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file manufacturing-1.4.1.tar.gz
.
File metadata
- Download URL: manufacturing-1.4.1.tar.gz
- Upload date:
- Size: 22.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 38a7e01a3f0f0427a428dd7b5de8650e5937907e5da13cacd632f94e7002f4f9 |
|
MD5 | dfc382246528096b4ab8818e9a0d02f5 |
|
BLAKE2b-256 | 39404ad8658fc3825c9efa97c38f56e4b169494e51ecd5669674d50d74f56645 |
File details
Details for the file manufacturing-1.4.1-py3-none-any.whl
.
File metadata
- Download URL: manufacturing-1.4.1-py3-none-any.whl
- Upload date:
- Size: 25.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ab94b3def753f1fc059824f85ffd175a23329ecac39d279ac45b4bc2086052ce |
|
MD5 | 83625d55134c2c8aa864046113acd7ab |
|
BLAKE2b-256 | c87423e3d3f639733c069e72be67ef1975bce72a9d13e43b861701ae264eb712 |