In this guide
A low-stock alert tells you how many units remain. It does not tell you whether those units will survive until usable replenishment arrives, how uncertain demand is, or whether the next purchase is worth the cash and obsolescence risk.
This guide turns Shopify data into a transparent reorder model. Use it with the inventory management pillar, then replace assumptions with actual demand and supplier performance.
Fast summary
- Reorder point, safety stock, and order quantity are separate.
- Measure lead time through quality release, not dispatch.
- Use in-stock demand and scenario ranges.
- Set buffers by SKU risk and uncertainty.
- Feed actual demand and supplier performance back into the model.
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Separate the trigger, buffer, and purchase amount
The reorder point triggers review or purchase. Safety stock absorbs uncertainty. Order quantity is how much to buy. Mixing them creates duplicate buffers or purchases that ignore case packs, storage, and cash.
Work at variant and location level. Use available stock for immediate supply, add only dependable incoming units, and subtract uncovered demand. An unconfirmed supplier promise is not firm incoming stock.
| Term | Question | Input |
|---|---|---|
| Lead-time demand | What sells before stock is usable? | Daily demand multiplied by total lead time |
| Safety stock | What uncertainty should be absorbed? | Demand and lead-time variability |
| Reorder point | When should action begin? | Lead-time demand plus safety stock |
| Order quantity | How much should be bought? | Target less inventory position, adjusted for constraints |
Swipe horizontally to compare every column.
The trigger and purchase amount are related but separate.
Build a baseline from in-stock demand and total lead time
A useful baseline is reorder point = average daily unit demand multiplied by total replenishment lead time, plus safety stock. Lead time runs from purchase approval through production, handling, transit, customs, receiving, inspection, and release to available.
Calculate demand from representative in-stock days. A calendar average can understate demand when the item was unavailable. Separate ordinary demand from launches, wholesale orders, and promotions, then model base, high, and low scenarios.
Note
Example: 4 units per day, 18 days of total lead time, and 24 units of safety stock produces a baseline reorder point of 96 units. This is a planning example, not a universal target.
Set safety stock from uncertainty and consequences
A simple starting buffer is average daily demand multiplied by chosen buffer days. Increase it for variable lead times, volatile demand, no substitute, high contribution, or supplier weakness. Constrain it for short shelf life, trend risk, easy substitution, expensive storage, or limited cash.
Do not assign every SKU the same service target. Shopify's ABC analysis is revenue-based; add margin, strategic role, supplier concentration, and obsolescence risk before choosing a buffer.
| Signal | Action | Reason |
|---|---|---|
| Variable supplier lead time | Increase buffer | Arrival is less dependable |
| Campaign approaching | Run a separate scenario | Demand can depart from baseline |
| Easy substitute | Possibly reduce | Demand can shift |
| Short shelf life | Constrain | Excess can cost more than stockout |
| High contribution, no substitute | Increase | Unavailable days are expensive |
Swipe horizontally to compare every column.
Operate a weekly buy sheet
Record available, committed, unavailable, dependable incoming, average and scenario demand, lead time, buffer, reorder point, inventory position, MOQ, case pack, landed cost, open purchase order, and owner. Rank by days until risk.
Review recommendations against cash, storage, campaigns, aged stock, supplier capacity, and freight. Document overrides. After receipt, compare forecast with actual in-stock demand and promised lead time with usable lead time, then update one input when evidence repeats.
Plan new products, seasonality, and promotions without false precision
New products have no stable velocity, so do not disguise an assumption as a forecast. Build a range from qualified audience size, comparable products, conversion scenarios, launch duration, and replenishment flexibility. Commit inventory in stages when supplier economics permit, and define the evidence required before a second order.
Seasonal products need a demand curve, not a higher annual average. Mark the selling window, final safe reorder date, supplier closure dates, inbound capacity, and markdown deadline. For promotions, model incremental units rather than applying the discount percentage to ordinary demand. Include cannibalization from related variants and the possibility that creative or traffic underperforms.
Connect stock decisions to the product validation guide and use preorders only when the product, payment timing, ship window, cancellation rights, and capacity support the promise. Inventory is a capital decision, so the downside case deserves the same attention as the upside case.
| Scenario | Planning method | Stop or review signal |
|---|---|---|
| New product | Demand range and staged buy | Weak qualified interest or conversion |
| Seasonal item | Weekly curve and final reorder date | Inbound arrival misses selling window |
| Promotion | Incremental demand scenario | Margin, traffic, or capacity guardrail |
| Viral spike | Capacity cap and frequent review | Supplier or fulfillment rejection |
| Preorder | Explicit demand plus promise ledger | Delay or refund capacity threshold |
Swipe horizontally to compare every column.
Uncertain demand needs ranges, gates, and exit rules rather than a single confident number.
Frequently asked questions
A transparent baseline is average daily unit demand multiplied by total replenishment lead time, plus safety stock. Refine it for variability, dependable incoming supply, open demand, campaigns, and SKU risk.