Creating Quality Control Dashboard using Python for Manufacturing Issues
summary
A Python pipeline collects operator drop‑off and lab check data via Tkinter forms, cleans it with NumPy/Pandas, builds reports with Matplotlib and XlsxWriter, and sends automated emails via SMTP; interactive dashboards run with Plotly + Voila using sunburst filters and a custom pivot‑table widget published on PyPI and GitHub. The system tracks checks per control plan, measures reaction time to out‑of‑spec parts, logs fixes, and replaces repetitive reporting—dashboard used in weekly meetings, demo hiccup aside, automation is operational.
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chapters (17)
- 0:00Introduction
- 0:41Disclaimer
- 1:03Simplification
- 1:30Outline
- 2:18Why Open Source
- 3:38Basic Design
- 8:03Pivisorg
- 9:28Bouquet and Plotly
- 10:52voila
- 11:35Quality Check Flow
- 13:23Lower Level Objectives
- 14:33Collecting Data
- 15:51Generating Daily Reports
- 16:41Interactive Dashboard
- 18:09Dashboard Overview
- 21:08Python Packages
- 21:41Pros
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