SQL · PYTHON · DATA PIPELINES

Supply Chain Intelligence Dashboard

Designed and implemented end-to-end data pipelines that transformed raw supplier data into actionable insights on pricing, design components, and supply chain risk — enabling executive decision-making with confidence.

RoleData Analyst · IronPlate Solutions
TimelineMay 2025 – Sept 2025
StackPython · Excel · SQL

📌 The Problem

IronPlate Solutions needed visibility into their supply chain: which suppliers were price-competitive, where design component costs were drifting, and what the data actually said. The existing process was fragmented — data lived in disconnected spreadsheets with no consistent structure or reporting cadence.

The ask was clear: build a system that generates reliable, repeatable insights without relying on a dedicated database team.

🏗️ What I Built

  • Designed a relational spreadsheet architecture with normalized lookup tables, foreign key-style references, and validation rules — structured to enable a future migration to a formal SQL database with minimal rework
  • Built Python scripts (using pandas and openpyxl) to automate data ingestion, cleaning, and formatting — cutting manual entry time by ~70%
  • Created dynamic pivot-based dashboards that allowed stakeholders to slice supplier data by category, region, and time period
  • Delivered a weekly executive summary report generated automatically each Monday morning

📊 Outcomes

~70%
Reduction in manual data entry time
12%
Pricing variance identified across supplier tiers
Weekly
Automated executive report cadence established

🛠️ Technologies Used

Python (pandas)Excel / openpyxl SQL-style Data ModelingVLOOKUP / Pivot Tables Data Pipeline DesignExecutive Reporting

💡 Key Learnings

  • The most impactful analytical work is often translating complexity into simplicity — executives don't need every number, they need the right three
  • Building with future migration in mind (relational principles in Excel) saved the company from having to rebuild from scratch
  • Automating reporting is not just a time-saver — it builds trust in the data because the process is consistent and auditable