Design
Optimizing Inventory for a Toronto D2C Cosmetics Brand
Overview
What this challenge is about.
Your task is to design a multidimensional data model (star schema) for inventory management, create an ETL pipeline to load sample data (provided as CSV files), and develop an OLAP cube to analyze stock turnover, forecast demand using simple moving averages, and identify slow-moving items. Success means delivering a functional dashboard (using any BI tool like Power BI or Tableau) that shows at least three key metrics: stock-out risk, turnover rate, and ABC classification. Constraints: use only publicly available or simulated data; no real customer data.
The Brief
What you'll do, and what you'll demonstrate.
Design and implement a BI system to reduce stock-outs by 20% and decrease excess inventory by 15% within three months.
Earning criteria — what you'll demonstrate
- Apply dimensional modeling (star schema) to a business scenario
- Design and implement an ETL process for data integration
- Use OLAP operations (drill-down, slice, dice) to analyze inventory data
- Create a BI dashboard that supports decision-making
- Evaluate inventory performance using key metrics
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career mappings coming soon.