This project, implemented in a Jupyter Notebook (.ipynb), provides Python classes for two common supply chain techniques:
- Moving Average Forecasting: A simple time-series forecasting method implemented from scratch using NumPy.
- Transportation Linear Programming (LP): An optimization model to find the minimum cost shipment plan between supply sources and demand destinations, using the PuLP library.
This notebook demonstrates the application of these quantitative methods in a scalable and reusable format, including example usage for both techniques.
To run this notebook, you need a Jupyter environment and the following Python libraries:
- Python 3.x
- Jupyter Notebook or Jupyter Lab
- PuLP library
- NumPy library
You can install the required Python libraries using pip (the notebook also includes a cell to install them):
pip install jupyterlab pulp numpy(Or pip install notebook pulp numpy, if you prefer the classic Notebook interface)
How to Run:-
Clone or Download: Clone this repository or download the supply_chain_project.ipynb file.
Start Jupyter: Open your terminal or command prompt, navigate to the directory where you saved the file, and start Jupyter Lab (jupyter lab) or Jupyter Notebook (jupyter notebook).
Open Notebook: Your browser should open the Jupyter interface. Navigate to and open the supply_chain_project.ipynb file.
Run Cells: You can run the cells
The script includes example usage for both the MovingAverageForecaster and TransportationOptimizer classes