This project performs Exploratory Data Analysis (EDA) on a sample supply chain dataset to uncover insights about bottlenecks, supplier performance, shipment delays, and cost trends. The analysis helps in optimizing inventory and improving overall supply chain efficiency.
- Understand key supply chain metrics and their distributions
- Identify delays, bottlenecks, and patterns in shipments
- Derive actionable insights for better decision-making
- Sample supply chain dataset containing:
- Orders, Shipments, Suppliers
- Costs, Delivery timelines, Inventory levels
- Cleaned and preprocessed for analysis
- Python
- Pandas — Data manipulation & cleaning
- NumPy — Numerical computations
- Matplotlib & Seaborn — Data visualization
- Jupyter Notebook — Interactive development
- Data cleaning & preprocessing
- Visualizations for:
- Order distribution
- Shipment delays
- Supplier performance
- Cost trends
- Actionable insights to reduce delays and optimize inventory
Summary:
This EDA highlights top-selling products, customer segments, supplier performance, and cost-efficiency insights.
This EDA provided actionable insights into supplier performance, shipment delays, cost optimization, and revenue trends. The analysis can help organizations improve demand forecasting and reduce logistics inefficiencies.
git clone https://github.com/dubeypt/supply-chain-eda.git
cd supply-chain-eda
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
pip install -r requirements.txt
jupyter labAuthor: Aditya Dubey
Data Science | Analytics | AI And ML Enthusiast