Supply chain management plays a crucial role in ensuring efficient production, inventory management, logistics, and customer satisfaction. This project performs an Exploratory Data Analysis (EDA) on a supply chain dataset to uncover valuable business insights related to sales, revenue, inventory, shipping, manufacturing costs, and product quality.
The project demonstrates how Python can be used to analyze operational data and support data-driven decision-making in supply chain management.
- Analyze product sales and revenue trends.
- Evaluate inventory and stock levels.
- Compare shipping costs across different carriers.
- Analyze transportation costs by transportation mode.
- Evaluate supplier performance.
- Study manufacturing costs and lead time.
- Identify product categories with higher defect rates.
The dataset contains information related to various aspects of the supply chain, including:
- Product Type
- SKU
- Price
- Availability
- Number of Products Sold
- Revenue Generated
- Stock Levels
- Order Quantities
- Shipping Costs
- Shipping Carriers
- Transportation Modes
- Supplier Name
- Manufacturing Costs
- Lead Time
- Inspection Results
- Defect Rates
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Plotly
- jupyter
- Data Loading
- Exploratory Data Analysis (EDA)
- Data Cleaning
- Correlation Analysis
- Revenue Analysis
- Inventory Analysis
- Shipping Cost Analysis
- Transportation Cost Analysis
- Manufacturing Cost Analysis
- Supplier Performance Analysis
- Defect Rate Analysis
- Conclusion
- Scatter Plot (Price vs Revenue)
- Sales Distribution by Product Type
- Defect Rate Distribution
- Transportation Cost Distribution
- Revenue by Shipping Carrier
- Revenue Generated by SKU
- Stock Levels by SKU
- Order Quantity by SKU
- Shipping Costs by Shipping Carrier
- Lead Time & Manufacturing Cost Analysis
- Revenue generation varies significantly across different product types.
- Product pricing influences revenue generation.
- Sales distribution differs among product categories.
- Shipping costs vary across shipping carriers.
- Transportation modes contribute differently to overall logistics costs.
- Manufacturing cost and lead time vary by product type.
- Some product categories have higher defect rates, indicating opportunities for quality improvement.
- Inventory and order quantity analysis help identify stock management patterns.
This project can be extended by:
- Developing demand forecasting models using Machine Learning.
- Predicting inventory shortages.
- Optimizing transportation routes.
git clone https://github.com/vineetvyas7/Supply-Chain-Analysis.gitcd Supply-Chain-Analysispip install -r requirements.txtjupyter notebookOpen the notebook and run all cells.
Supply-Chain-Analysis/
│
├── Supply_Chain_Analysis.ipynb
├── supply_chain.csv
├── README.md
├── requirements.txt
├── .gitignore
└── images/
This project demonstrates how data analysis can improve supply chain decision-making by uncovering trends in sales, inventory, logistics, manufacturing, and product quality. The insights generated from this analysis can help businesses optimize operations, reduce costs, improve supplier performance, and enhance overall supply chain efficiency.
Vineet Vyas