This project is an end-to-end data analytics and simulation dashboard designed to monitor global delivery health, identify high-risk suppliers, and simulate the financial impact of logistics disruptions. Built using Python and Streamlit, this tool transforms 180,000+ rows of raw shipping data into an interactive, enterprise-grade application.
- Data Engineering & Analysis: Python (Pandas, NumPy)
- Interactive Visualizations: Plotly Express
- Front-End Framework: Streamlit
- Environment: Local Virtual Environment (
.venv)
- Dynamic KPI Tracking: Calculates the Shipping Gap (Real vs. Scheduled days) and Late Delivery Risk percentages across all global regions.
- Stress Test Simulator: A localized disruption engine that allows managers to artificially inject delayed shipping days into a specific region and watch the downstream impact on operational failure rates in real-time.
- Geographic Risk Mapping: Interactive heatmaps pinpointing specific order cities with the highest concentration of delayed shipments.
- Supplier Watchlist: An automated, threshold-based table identifying specific product categories that fall below acceptable delivery standards, allowing for targeted contract renegotiations.
- Clone the repository:
git clone [https://github.com/YourUsername/Supply_Chain_Analytics.git](https://github.com/YourUsername/Supply_Chain_Analytics.git) cd Supply_Chain_Analytics