Find the optimal locations of your manufacturing facilities to meet your customers’ demand and reduce production costs

In this Article, we will present a simple methodology using Linear Programming for Supply Chain Optimisation, considering

• Fixed production costs of your facilities ($/Month)
• Variable production costs per unit produced ($/Unit)
• Shipping costs ($)
• Customer’s demand (Units)

Click on the image below to access a full tutorial to understand the concept behind this solution

As the Head of Supply Chain Management of an international manufacturing company, you want to redefine the Supply Chain Network for the next 5 years, taking into account the recent increase in shipping costs and demand forecasts.

In this repository, you will find all the code used to explain the concepts presented in the article.

• Supply Chain Optimization.ipynb - Jupyter notebook with step-by-step analysis
• supply_chain_optimization.py - Standalone Python script

This project uses uv for dependency management.

# Install dependencies
uv sync

# Run the Python script
uv run python supply_chain_optimization.py

# Or launch Jupyter notebook
uv run jupyter notebook

• pandas
• pulp
• openpyxl
• jupyter

Senior Supply Chain and Data Science consultant with international experience working on Logistics and Transportation operations.
For consulting or advising on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting.
For more case studies, check my Personal Website.