This project demonstrates my ability to turn disconnected sales and inventory data into actionable business intelligence. Designed as a simulated case for Volvo Trucks, the dashboard enables dynamic filtering, KPI tracking, and automated insights — all hosted via a browser-based interface built in Streamlit. It reflects the kind of work I aim to deliver as an Insight Analyst: clear, functional, and decision-ready.
Project Context
Use Case: Simulated insight solution for Volvo Trucks (2017–2018 sales data)
Role: Reporting Analyst (self-directed simulation)
Challenge: Inconsistent data across Excel files, lack of visual sales tracking, no unified KPI structure
Goal: Build a lightweight browser-based tool that informs both sales performance and inventory risk
Regional and annual filters (e.g. model, year, category)
KPI-driven insights with built-in logic for high-risk inventory
Visual comparison of sales volume vs stock levels
Auto-generated written insights, tailored to selected filters
Accessible and interactive front-end built entirely with Python + Streamlit
Insight 