Procurement Analytics · Python · Streamlit

Supplier Performance & Spend Intelligence Dashboard

A practical procurement analytics application that turns messy supplier spend files into spend insights, supplier categorization, rationalization opportunities, directional savings estimates, and procurement action recommendations.

PythonStreamlitPandasPlotlyOpenPyXLPytestProcurement AnalyticsData Quality

Problem

Supplier spend data is rarely clean. Real files often contain inconsistent supplier names, multiple Excel tabs, varied column names, missing categories, multiple currencies, transaction-level rows, contract gaps, and extra ERP fields. Most dashboards assume clean input, but procurement analysis usually starts with data preparation and validation.

Solution

I built a Streamlit app that maps common spend file columns, checks data readiness, classifies suppliers into procurement categories, parses dates, aggregates supplier/category spend, identifies rationalization opportunities, estimates savings, and generates procurement next-action recommendations.

Core Features

CSV and multi-sheet Excel upload

Automatic column alias mapping for real-world spend files

Extra, context, and unmapped column reporting

Rules-based supplier and spend categorization

Classification confidence scoring and review flags

Invoice date parsing with year, quarter, and month fields

Supplier/category aggregation for transaction-level files

Spend dashboard with supplier, category, region, business unit, and trend views

Supplier rationalization opportunity engine

Directional savings estimate ranges

Grouped procurement recommendation initiatives

Downloadable CSV and Excel outputs

What It Demonstrates

This project combines procurement domain expertise with hands-on analytics engineering. It shows how I think about messy business data, product workflow design, spend analytics, category strategy, supplier rationalization, savings estimation, and executive-facing insight generation.