Project Nexus: Supply Chain Analytics & Financial Impact Modeling Project Overview As an Economics student at the University of Minnesota, I developed Project Nexus to bridge the gap between raw data and strategic business decisions. This project simulates a complex retail supply chain, using Python and SQL to identify operational inefficiencies that directly impact the bottom line.

Technical Toolkit Language: Python 3 (Pandas, NumPy, SQLite3)

Database: Relational SQL (Window Functions, CTEs, Complex Joins)

Key Skills: Data Cleaning, Variance Analysis, Financial Modeling, Inventory Smoothing

High-Level Business Insights The $432K Revenue Leak: Identified that Global_Vendor_5 was responsible for over $432,000 in lost revenue due to stockout events.

Reliability Benchmarking: Ranked suppliers by delivery cadence; Global_Vendor_4 was the most stable partner (3.49 variance).

Inventory Smoothing: Engineered a 7-Day Rolling Average model for high-value SKUs to optimize reorder points and minimize capital tie-up.