Product Segmentation for Retail with Python 📈
A statistical methodology to segment your products based on turnover and demand variability
Product segmentation is the process of grouping products with similar characteristics and serving the same market. It is usually related to marketing (Sales Categories) or manufacturing (Production Processes). However, as a Logistics Manager, you rarely care about the product itself when managing goods flows, except for the dangerous and oversized products.
Your attention is primarily focused on the distribution of sales volumes (fast/slow movers), demand variability, and delivery lead time.
You want to put effort into managing products that have:
- The highest contribution to your total turnover: ABC Analysis
- The most unstable demand: Demand Variability
Article
In this Article, we will introduce simple statistical tools to combine ABC Analysis and Demand Variability to perform product segmentation.
Problem Statement
You are the Operational Director of a local Distribution Center (DC) that delivers 10 Hypermarkets.
In your scope, you have the responsibility of
- Preparation and delivery of replenishment orders from stores
- Demand Planning and Inventory Management
Question
What impacts your logistic performance?
Data set
This analysis will be based on the M5 Forecasting dataset of Walmart stores' sales records (Link.
Code
In this repository, you will find all the code used to explain the concepts presented in the article.
About me 🤓
Senior Supply Chain Engineer with international experience working on Logistics and Transportation operations.
Please have a look at my portfolio: Data Science for Supply Chain Portfolio
For consulting or advising on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting
Data Science for Warehousing📦, Transportation 🚚 and Demand Forecasting 📈