Monthly FP&A flash in SQL. Actual vs budget vs forecast, price/volume, channel margin, and OpEx by cost center.
Fictional company: Northline Consumer Products. No employer data.
The question this answers: March revenue missed budget by about $6k. Was that price, volume, or mix, and did OpEx make it worse?
| Item | Result |
|---|---|
| Revenue | $623k |
| vs budget | −$6.4k (−1.0%) |
| Gross margin | 57.2% |
| OpEx vs budget | +$3.9k |
| Contribution after OpEx | $185k |
Channel split for March: DTC beat budget by $6.0k on hydration price and volume. Wholesale missed by $11.1k, almost all snacks volume. Marketing is the YTD OpEx problem (+9% vs budget, media).
pip install -r requirements.txt
python load_and_run.pyThat loads data/ into SQLite and writes one CSV per query under output/.
| File | Use |
|---|---|
sql/01_monthly_variance.sql |
Revenue and margin vs budget and forecast |
sql/02_price_volume.sql |
Price and volume variance by SKU, latest actual month |
sql/03_channel_margin.sql |
Channel revenue and gross margin |
sql/04_opex_by_cost_center.sql |
YTD OpEx vs budget |
sql/05_executive_flash.sql |
One-row close flash |
Stack: SQL (SQLite) · pandas. Same grain an FP&A analyst would pull from a warehouse.
