Design inventory and stock control — availability tracking, optimistic locking to prevent overselling, reservations, multi-location, backorders, and restock forecasting. Includes a safety-stock/reorder-point calculator. Triggers: designing inventory, fixing overselling, forecasting restock.
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Stock is where data integrity meets revenue: oversell and you lose trust, over-reserve and you lose sales. Design availability that is correct under concurrency and forecast restocks from real demand — platform-agnostic.
Gather: inventory model (single vs multi-location), sales velocity and lead times, fulfillment style (own stock, dropship, backorder), and goal (design the model, forecast restock, or audit for oversell risk).
| Mode | Trigger | Output |
|---|---|---|
| Design | design | Availability model + concurrency strategy |
| Forecast | forecast | Safety stock + reorder points (via script) |
| Audit | audit | Oversell-risk and accuracy review |
Track three numbers per SKU per location, never one:
| Quantity | Meaning |
|---|---|
| on_hand | Physically present |
| reserved | Allocated to unfulfilled orders/checkouts |
| available | on_hand − reserved — the only number a shopper sees |
Rule: Reserve at checkout start with a TTL; release on timeout/abandonment. Decrement on_hand at fulfillment, not at order placement.
The classic race: two shoppers buy the last unit. Defenses, in order of preference:
UPDATE ... WHERE available >= qty and check rows-affected.Always treat "reserve stock" as a transaction that can FAIL and surface that to checkout cleanly. Multi-location adds an allocation step (which location fulfills) before the decrement.
For backorder rules, multi-location allocation strategies, and reservation TTL tuning, see the patterns above and hand off to order-lifecycle for fulfillment state.
Use the script to turn a sales CSV into reorder points:
python skills/inventory-stock/scripts/restock_forecast.py # demo data
python skills/inventory-stock/scripts/restock_forecast.py sales.csv --lead-time 14 --service-level 0.95
It computes average daily demand, demand variability, safety stock (z × σ_demand × √lead_time), and reorder point (demand × lead_time + safety_stock). CSV columns: sku,date,units_sold.
| Request | Output |
|---|---|
| Design | Availability model + chosen concurrency strategy |
| Forecast | Per-SKU reorder point + safety stock table |
| Audit | Oversell-risk findings: PASS / FAIL per check |
An inventory design PASSES when shoppers only see available, reservations expire, decrements are atomic/versioned, and forecasts drive reorder points instead of guesswork.
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