Design and develop an interactive data visualization system to monitor and analyze supply chain performance metrics such as inventory levels, order fulfillment rates, supplier efficiency, logistics costs, and delivery timelines. The system should enable managers to identify bottlenecks, optimize operations, and improve overall supply chain efficiency.
Collect or simulate supply chain datasets including suppliers, inventory, orders, shipments, delivery dates, costs, and warehouse data.
Perform data cleaning, transformation, and normalization using Python (Pandas) or R.
Design a relational database schema representing suppliers, products, warehouses, customers, and logistics partners.
Create ER diagrams and document database relationships.
Implement ETL processes to load transactional data into a data warehouse (star or snowflake schema).
Develop backend APIs using Flask/Django/Node.js to fetch aggregated KPIs.
Build interactive dashboards using Power BI, Tableau, or Plotly/D3.js.
Implement filters by supplier, region, product category, and time period.
Add drill-down capability from global supply chain overview to individual shipment details.
Integrate predictive visualization for demand forecasting or delay risk analysis.
Optimize dashboard responsiveness and performance for large datasets.
Conduct testing with simulated real-world scenarios.
Prepare comprehensive documentation including system architecture, KPI definitions, visualization methods, and business insights derived from the dashboard.
