RetailCo Supply Chain Dashboard

Project Objective

To identify and monitor the key factors affecting supplier performance and lead-time reliability across a camper-van manufacturing supply chain. The goal was to design a single Power BI dashboard that executives could use to track late deliveries, supplier cost efficiency, and parts availability in real time.

Outcome

A fully interactive Power BI dashboard connected to a synthetic SQL data warehouse that visualizes supplier spend, delivery lead-times, and performance KPIs. It includes dynamic filters by supplier, category, and region — allowing managers to pinpoint bottlenecks and assess supplier reliability within seconds.

Tools Used

SQL, Power BI, Python, ETL Pipelines, PostgreSQL, Excel

Data Source

Data originated from simulated ERP exports covering: Purchase Orders and Supplier Master data from Macola ERP Warehouse Inventory levels and Bill of Materials tables Daily receipts and lead-time logs Data was extracted and transformed using Python (Pandas) scripts, cleaned for consistency, and loaded into PostgreSQL on Neon. Power BI was then connected via a direct SQL query for visualization.

Analysis & Insights

The analysis focused on supplier reliability and procurement efficiency:

Created KPIs for average lead-time, on-time delivery %, and supplier cost per part.

Used DAX measures to calculate rolling averages and conditional formatting to highlight outliers.

Designed a scatter plot comparing lead-time vs spend to identify high-impact suppliers.

Introduced slicers for date, supplier, and part category — enabling interactive exploration.

Key Findings:

30% of total spend was concentrated in 5 suppliers with high variance in delivery times.

Several low-cost suppliers exhibited long and inconsistent lead-times, indicating potential risk to production schedules.

Centralizing the data reduced manual spreadsheet reporting by over 10 hours per month.

Key Findings

This project deepened my understanding of ETL pipeline design and data modeling in Power BI. I refined DAX skills for cumulative measures, improved the star schema linking purchase orders and suppliers, and learned how to automate refreshes through the Power BI service. In future updates, I plan to integrate a live API for automated supplier updates and incorporate anomaly detection on delivery times.

Results & Impact

Delivered a unified dashboard consolidating supplier data from multiple ERP tables. Simplified stakeholder reporting through Power BI’s scheduled refresh and role-based access. Demonstrated how a manufacturer could reduce production delays by improving supplier visibility. Built the foundation for adding predictive analytics (lead-time forecasting) in future iterations.