Aurora Distribution: where the margin lives, and where it leaks
Profitability analysis: margin cut by product, category, channel and customer, plus a consolidated profit and loss. Period 2024-2025. Fictional company on synthetic data: your own analysis uses your own products and customers.
The business turned over $2,501,412 in the period and made $172,581 in operating profit, which is 6.9% of what it sold. Gross margin is 27.8%, but 2 products are sold at a loss and drain $2,989 of margin that could have stayed in the bank. Revenue grew 11.5% from 2024 to 2025.
Profit and loss
| Line | Amount | % of revenue |
|---|---|---|
| Revenue | $2,501,412 | 100.0% |
| (-) Sales taxes | -$144,240 | -5.8% |
| (=) Net revenue | $2,357,172 | 94.2% |
| (-) Cost of the goods sold | -$1,805,475 | -72.2% |
| (=) Gross profit | $695,938 | 27.8% |
| (-) Sales commission | -$64,974 | -2.6% |
| (=) Contribution margin | $486,724 | 19.5% |
| (-) Fixed costs | -$314,142 | -12.6% |
| (=) Operating profit | $172,581 | 6.9% |
For every 100 you sell, about 6.90 is left once every cost is paid. That puts the operating margin in the low band.
Where the margin lives, and where it leaks
By category
| Category | Revenue | Margin % | % of revenue |
|---|---|---|---|
| Dry goods | $871,102 | 26.8% | 34.8% |
| Drinks | $851,286 | 28.2% | 34.0% |
| Dairy | $401,917 | 33.0% | 16.1% |
| Cleaning | $377,108 | 24.0% | 15.1% |
Dairy is what holds the business up, at 33.0% margin. Cleaning is the weakest, at 24.0%.
The products losing money
2 products are sold for less than they cost. The full detail is in produtos_margem_negativa.csv.
| Product | Category | Revenue | Margin % | Margin |
|---|---|---|---|---|
| Washing-up liquid 500ml | Cleaning | $21,346 | -4.9% | -$1,039 |
| Disinfectant 2L | Cleaning | $67,801 | -2.9% | -$1,950 |
What to do: reprice them or stop selling them. Getting them merely to break even puts $2,989 of margin back. Before you drop any of them, check whether one is a loss leader that brings the rest of the order with it, by cross-referencing margem_por_cliente.csv.
How concentrated the revenue is (and why that is a risk)
The three largest customers account for 37.6% of revenue, and Fairprice Market alone is 14.1%. At that level of concentration, losing one large customer takes the month's cash with it.
| Customer | Revenue | % of revenue | % running total |
|---|---|---|---|
| Fairprice Market | $351,653 | 14.1% | 14.1% |
| Supermore Group | $298,858 | 11.9% | 26.0% |
| Green Valley Wholesale | $289,359 | 11.6% | 37.6% |
| Southern Distribution | $209,283 | 8.4% | 46.0% |
| Central Emporium | $165,888 | 6.6% | 52.6% |
| Sabor & Co. Restaurant | $136,171 | 5.4% | 58.0% |
| Thrift Supermarket | $126,983 | 5.1% | 63.1% |
| Mirante Hotel | $116,939 | 4.7% | 67.8% |
| Bella Massa Canteen | $88,419 | 3.5% | 71.3% |
| Good Party Catering | $84,259 | 3.4% | 74.7% |
Seasonality (when cash gets tight)
The peak is December, averaging $127,854 a month; the trough is February, at $77,231. Planning purchases and working capital around the trough is what keeps it from hurting.
What to do, in order
- This month: decide whether to reprice or drop the 2 loss-making products (
produtos_margem_negativa.csv). - Next buying cycle: renegotiate cost on the low-margin categories, starting with the one that brings in the most revenue.
- Risk: put a retention plan behind the top three customers, aiming to bring their combined share below 33%.
- Cash: set aside extra working capital ahead of the February trough.
Limitations (read these before you act)
- Cost here is the direct cost of the goods as recorded on the sale. Fixed overheads are not spread across individual products; the profit and loss consolidates them at the level of the business.
- Whether an expense counts as fixed or variable follows the
tipocolumn in the expenses file. - Seasonality averages two years (2024-2025). A one-off event can distort a single month.
Appendix: the files that came with this report
margem_por_produto.csv,margem_por_categoria.csv,margem_por_canal.csv,margem_por_cliente.csv: margin by each axis.produtos_margem_negativa.csv: the loss-making subset.cliente_concentracao_top10.csv: top 10 customers with running share.dre_consolidada.csv,dre_mensal.csv,dre_anual.csv: the profit and loss.sazonalidade_mes.csv: average revenue by month of the year.