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Build interactive violin plot for Pareto Analysis of your Supply Chain data using Flask and D3.js

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Pareto Analysis of Cosmetics Sales with Violin Plot using Flask + D3.js 💄

Build Interactive Violin chart to understand the distribution of your e-commerce sales

An Italian economist named Vilfredo Pareto developed 1906 a mathematical formula to describe the distribution of wealth in Italy. He discovered that 80% of the wealth belonged to 20% of the population.

Pareto

A few decades later this rule has been generalized to many other applications including Supply Chain and Logistics Management. This principle called the “Pareto Principle”, “the 80–20 rule” or “The Law of Trivial Many and Critical Few” can be translated for Logistics Practitioners

  • 80% of your company revenue is made from 20% of your reference
  • 80% of your volume is picked in 20% of your picking locations
  • 80% of your replenishment volume will be performed on 20% of your picking locations

Medium Article

In this Medium Article, we will explore how to apply this Pareto Principle using a actual Logistic operational example.

This is an image

Objective

This repository code can be used to build a visualization tool to apply the principles presented in this article.

Code

This repository code should be used in the first step of analysis to duplicate the example found and render it using Python Flask.

  1. Copy Github repository in your local folder and create a local python environment
  2. Download libraries listed in requirements.txt
  pip3 install -r requirements.txt
  1. Launch app.py
py app.py

Next Steps

Communicate these visuals to your operations and marketing team to understand what drives your sales.

About me 🤓

Senior Supply Chain Engineer with an international experience working on Logistics and Transportation operations.
Have a look at my portfolio: Data Science for Supply Chain Portfolio
Data Science for Warehousing📦, Transportation 🚚 and Demand Forecasting 📈

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Build interactive violin plot for Pareto Analysis of your Supply Chain data using Flask and D3.js

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