COGS & SKU Profitability Engine
Designed a cost calculation system that supported thousands of standard SKUs and hundreds of thousands of dynamically generated custom SKUs, enabling SKU-level profitability tracking and better pricing decisions.
- Python
- PostgreSQL
- Supabase
- SQL
- Next.js
- TypeScript
- pandas
Overview
I designed and built a COGS and SKU profitability engine to calculate product costs across a complex ecommerce catalog. The system supported both standard SKUs and dynamically generated custom SKUs, making it possible to estimate profitability at a much more granular level.
This project was built to improve pricing decisions, margin visibility, and reporting accuracy.
Problem
The business had a large product catalog with many size, material, and shape combinations. Standard SKU-level cost tracking was not enough because many products could be dynamically generated from dimensions and product rules.
Without a reliable way to calculate product-level cost, it was difficult to accurately measure profitability, compare channels, or make confident pricing decisions.
Solution
I created logic to calculate costs based on SKU structure, product size, shape, material, and adjustment codes.
The system included:
- SKU parsing logic
- Size code interpretation
- Width and length calculations
- Shape-based rules
- Adjustment code handling
- Cost lookup tables
- Support for standard and custom SKUs
- Integration with reporting and profitability workflows
The result was a reusable engine that could support profitability analysis across a much larger SKU universe than a manually maintained table would allow.
Impact
This project enabled more accurate SKU-level profitability tracking and helped improve visibility into product margins. It also supported better pricing decisions by connecting product structure, cost, and channel performance.
Instead of treating cost as a static field, the system made cost calculation dynamic and scalable.
What I Learned
This project taught me how important clean business logic is when building data tools. A strong profitability system is not just about storing data. It requires understanding how products are structured, how costs are created, and how those rules need to scale over time.