Defined the FBS and FBO strategies and acceptable risk, approved price writes, and reconciled results against orders and settlements.
[Ozon · Automation · Analytics]
manager case · human decision
Ozon pricing and unit economics for FBO and FBS
The tool helps control pricing across a large Ozon catalogue: it reconciles Ozon and supplier data, calculates a minimum acceptable FBS price, and supports a separate FBO clearance flow. Daniel reviews the calculation, saves the previous price, and verifies the result through the API after each write.
role and evidence
human decisionPricing control with human verification
The 21 July audit, 21 August test run, and verified 6 August operation are separate from the synthetic demo.
Every write requires a calculation without changes, a 1–3 SKU test, operator approval, and saved source prices. No causal profit claim is made.
interactive demo · synthetic data
// simplified model, not the operator tool interface
Pricing parameters
Price corridor: protection from extremes
{{ avgMargin }} %
avg calculated margin
{{ clampedCount }}
products hit the corridor
Product
Cost
Fee
Price
Margin
Status
{{ r.name }}
{{ r.cost }} ₽
{{ r.comm }} %
{{ r.price }} ₽
{{ r.marginPct }} %
{{ r.status }}
Calculated margin distributionsynthetic data · −40…+45% scale
{{ r.name }}
{{ r.marginPct }}%
The demo illustrates price-corridor mechanics with 8 fictional products. The working FBS model also uses acquiring, tiered commission, standard and higher-cost logistics scenarios, and a verified supplier cost. Scheduled monitors read data automatically; the operator confirms every price write.
decision and verification loop
From fresh data to a verified price
The automation prepares a decision and its evidence, while the operator retains write authority. Any mismatch moves the SKU into a safe path.
Collect current inputs
Ozon prices and orders are matched with supplier cost, commissions and logistics.
Model FBS and FBO separately
FBS gets a minimum-price calculation under standard and higher costs; FBO uses a separate clearance flow with current price and stock checks.
Show the calculation before writing
The proposed price, margin, reason and every constrained SKU are visible before any change.
Change a small group and verify
The operator starts with 1–3 SKUs, then uses a bounded batch sized for the selected flow. Source prices are saved first, and the result is read back from the Ozon API.
Continue the wave
The observed price matches the calculation.
Human review
Incomplete data or doubtful economics never reach the write step.
Stop and revert
A network error or mismatch halts the process.
01 · problem
One operator manages a large Ozon catalogue. Supplier costs, commissions, logistics, stock, and supplier availability change independently. The task is to calculate safe prices, keep FBS and FBO goals separate, and prevent a bulk operation from becoming an uncontrolled risk.
02 · solution
Python tools read prices, stock, orders, and financial operations from the Ozon Seller API and supplier costs from the Sima-land API. The FBS forecast calculates a target price from commission, acquiring, logistics, fulfilment, and cost. Historical profit calculations use financial operations and completed sales. They remain estimates when current rather than historical cost is used, or when account-level expenses cannot be allocated reliably by SKU.
The output is an inspectable file-based plan in CSV, JSON, or XLSX. There is no unattended price-writing mode.
03 · change safety
For FBS, the tool protects a minimum price and never proposes a reduction. FBO uses a separate liquidation flow: no more than a 20% reduction, and only when FBO stock exists while FBS stock is zero. This prevents a warehouse-clearance decision from weakening active FBS economics.
current data → calculation without writing → human review → confirmation → saved source prices → 1–3 SKU test → small batch → API reread → quarantine check
The FBS safeguard rereads price, supplier cost, market bounds, and economics under higher costs. The FBO flow rereads price and stock, limits each step to 20%, and runs only when FBO>0 and FBS=0. The operator starts with 1–3 SKUs, then uses a bounded batch. Source prices are saved before writing; afterwards the API price is reread and any quarantine growth is checked.
04 · verified outcome
7,473 SKUsin the verified 21 July 2026 audit
3 SKUspassed the automated checks
2,932 SKUsrouted to manual review
19 FBO SKUsreached target prices in the verified 6 August operation
All 126 automated tests pass as of 21 August 2026. No isolated causal before-and-after financial study has been run, so this case does not attribute an unverified profit increase to the tool.
05 · current boundaries
This is a working tool under operator control, not an autonomous service: there is no unified working database or central scheduler yet. Taxes are outside the model, some profit attribution is approximate, and scheduled monitors need stronger network resilience. Legacy one-off scripts are outside the current guarded flow. Price writes and quarantine release remain human decisions.
role of AI tools
AI tools helped analyze data and write code and tests; Daniel retained the decisions about external changes and verified their results.
Python
httpx
pydantic-settings
pytest
Ozon Seller API
Sima-land API
launchd
CSV · JSON · XLSX