Analyzing 100 product records to uncover supplier risks, shipping inefficiencies, cost anomalies, and demand patterns across a multi-supplier procurement network.
This project performs an end-to-end data analysis of a supply chain dataset covering 100 SKUs across 3 product categories (skincare, haircare, cosmetics) and 5 suppliers. The goal is to extract actionable business insights that help procurement and operations teams make smarter, data-driven decisions.
A plain-English insights report (PDF) is also included, written for non-technical stakeholders such as business managers and executives.
supply-chain-analysis/
│
├── supply_chain_data.csv # Raw dataset
├── supply_chain_analysis.py # Main analysis script
├── supply_chain_report.pdf # Non-technical insights report
│
├── charts/
│ ├── 01_revenue_profitability.png
│ ├── 02_supplier_performance.png
│ ├── 03_shipping_logistics.png
│ ├── 04_inventory_stock.png
│ ├── 05_quality_inspection.png
│ ├── 06_supplier_risk.png
│ ├── 07_demand_forecasting.png
│ ├── 08_anomaly_detection.png
│ ├── 09_supplier_segmentation.png
│ └── 10_correlation_heatmap.png
│
└── README.md
| Feature | Description |
|---|---|
| Product type | Category: skincare, haircare, cosmetics |
| SKU | Unique product identifier |
| Price | Selling price per unit |
| Supplier name | One of 5 suppliers |
| Defect rates | % of defective units per SKU |
| Lead times | Days from order to delivery |
| Manufacturing costs | Cost to produce each SKU |
| Shipping costs | Cost to ship each SKU |
| Revenue generated | Total revenue per SKU |
| Inspection results | Pass / Fail / Pending |
| Transportation modes | Road, Air, Rail |
| # | Finding |
|---|---|
| 1 | Skincare is the most profitable product category |
| 2 | Supplier 1 generates the highest revenue |
| 3 | Supplier 5 has the highest defect rate |
| 4 | Supplier 4 carries the highest composite risk score 🔴 |
| 5 | Carrier B is the most cost-efficient shipping option |
| 6 | 5 cost anomalies detected — require manual review |
| Tool | Purpose |
|---|---|
Python |
Core programming language |
Pandas |
Data manipulation and feature engineering |
NumPy |
Numerical computations |
Matplotlib |
Chart creation |
Seaborn |
Statistical visualizations |
Scikit-learn |
Machine learning — clustering, regression, anomaly detection |
1. Clone the repository
git clone https://github.com/Evthag/supply-chain-analysis.git
cd supply-chain-analysis2. Install dependencies
pip install pandas numpy matplotlib seaborn scikit-learn3. Run the analysis
python supply_chain_analysis.pyAll 10 charts will be saved as PNG files in your working directory and a full summary will print to the terminal.
Found this project useful or have suggestions? Feel free to open an issue or reach out via email.