A reorder point tells you the stock level at which to place the next purchase order. For a Shopify brand that uses a 3PL, the formula is the same one every inventory guide shows, but the lead time is longer than the supplier quote: inventory is not sellable until the warehouse has received it and Shopify shows it as Available. Leave those days out and the reorder point is too low every cycle.
The reorder point formula is: reorder point = (average daily unit sales × lead time in days) + safety stock. For a brand using a 3PL, lead time runs from the day you place the PO to the day units show as Available in Shopify: supplier production, freight, the dock appointment and receiving. Safety stock = Z × √(lead time × σ demand² + average demand² × σ lead time²).
What is the reorder point formula?
Reorder point = (average daily unit sales × lead time in days) + safety stock. The first term covers what you expect to sell while the order is on its way; safety stock covers the variation around that expectation.
Run it per variant, not per product. A hoodie in five sizes is five reorder points, because each size sells at a different rate and runs out on its own. Use units, not orders: a SKU that sells 120 units a day may appear on 100 orders if some customers buy two.
The term most brands get wrong is lead time. Many guides, and Google's AI Overview for this query as of October 2026, define it as the days your supplier takes to deliver. Peter King's widely cited APICS article defines the performance cycle more broadly: deciding what to order, communicating the order, manufacturing, delivery and storage, plus any time until the next review. For a brand using a 3PL, that cycle ends when the warehouse has received the units and they are sellable.
What counts as lead time when a 3PL receives your inventory?
Lead time is every day between placing the PO and the units showing as Available in Shopify. That includes four stages, and two of them happen at the 3PL.
| Stage | Example days | Where the number comes from |
|---|---|---|
| Supplier production and pack-out | 25 | Supplier quote, checked against the last three POs |
| Freight to the warehouse (ocean or air, port, drayage, domestic transit) | 30 | Forwarder bookings and actual arrival dates |
| Dock appointment wait at the 3PL | 2 | Date the freight was ready to deliver vs the appointment date |
| Receiving, count and putaway until Available in Shopify | 1 | Delivery date vs the date Available increased in Shopify |
| Total lead time | 58 | Sum of the four stages |
The 3PL stages look small, but they scale with velocity. At 120 units a day, each extra warehouse day is 120 units of demand the reorder point has to cover. A brand that leaves the three warehouse days out of this example sets its reorder point 360 units too low, about a third of the safety stock calculated below.
If you review stock and place POs on a fixed schedule, such as every Monday, add the review period to lead time when you size safety stock. King notes that the cycle includes any review period, because stock can drop below the reorder point the day after you checked.
How do you calculate safety stock?
Safety stock = Z × √(L × σd² + d² × σL²), where Z sets the service level, L is average lead time in days, σd is the standard deviation of daily demand, d is average daily demand and σL is the standard deviation of lead time in days. This is the combined formula from King's article and applies when demand and lead time vary independently.
Two simpler versions are special cases. If only demand varies, safety stock = Z × σd × √L. If only lead time varies, safety stock = Z × σL × d. Most brands that import goods have both kinds of variation, and lead time variation usually dominates, as the example shows.
Z comes from the cycle service level you want, which is the share of replenishment cycles that end without a stockout. The values below are from King's table. In Google Sheets or Excel, =NORM.S.INV(0.95) returns 1.645.
| Cycle service level | Z | Expected cycles with a stockout |
|---|---|---|
| 90% | 1.28 | 1 in 10 |
| 95% | 1.65 | 1 in 20 |
| 98% | 2.05 | 1 in 50 |
| 99% | 2.33 | 1 in 100 |
Service level is a business choice per SKU. Hero SKUs that drive first orders and subscriptions justify 98 or 99 percent; slow tail variants rarely need more than 90 percent, and overstocking them creates the aged stock covered in the dead stock guide.
What does a worked example look like at 12,000 orders a month?
Take a brand shipping 12,000 orders a month whose hero SKU sells an average of 120 units a day. The figures are illustrative, not data from any brand or provider.
- Average daily demand (d): 120 units, from 90 days of daily sales.
- Standard deviation of daily demand (σd): 36 units.
- Average lead time (L): 58 days, from the stage table above.
- Standard deviation of lead time (σL): 5 days, from the last eight POs.
- Target service level: 95 percent, so Z = 1.65.
- Lead time demand: 120 × 58 = 6,960 units.
- Safety stock: 1.65 × √(58 × 36² + 120² × 5²) = 1.65 × √(75,168 + 360,000) = 1.65 × 659.7 = 1,088 units.
- Reorder point: 6,960 + 1,088 = 8,048 units. When available plus incoming stock for this SKU falls to 8,048, place the next PO.
Notice where the safety stock comes from. Demand variation alone would need 1.65 × 36 × √58 = 452 units. The five-day swing in lead time is what pushes it to 1,088. For most importing brands, shortening and steadying lead time, including the days at the warehouse, lowers safety stock more than a better demand forecast does.
The same method at three brand sizes. In each case the hero SKU sells 0.3 units per order, and the standard deviation of daily demand is 30 percent of the average:
| Orders per month | Hero SKU units per day | σ daily demand | Lead time demand (58 days) | Safety stock (95%) | Reorder point |
|---|---|---|---|---|---|
| 5,000 | 50 | 15 | 2,900 | 454 | 3,354 |
| 12,000 | 120 | 36 | 6,960 | 1,088 | 8,048 |
| 25,000 | 250 | 75 | 14,500 | 2,268 | 16,768 |
How much do 3PL receiving days change the reorder point?
Every day of dock wait or receiving adds one day of sales to the reorder point. At the 12,000-order example, that is 120 units per day.
| Warehouse days (dock wait plus receiving) | Total lead time | Lead time demand | Change vs 1 warehouse day |
|---|---|---|---|
| 1 day | 56 days | 6,720 units | Baseline |
| 3 days | 58 days | 6,960 units | +240 units |
| 7 days (receiving backlog in peak) | 62 days | 7,440 units | +720 units |
Variation in warehouse days matters as much as the average. A 3PL that receives in one day in March and in seven days in November adds lead time variation exactly when demand is highest. Plan November inbounds around the warehouse's peak receiving schedule; the peak season guide works backward from Black Friday.
Ask any 3PL for its receiving record in writing: the delivery timestamp and the time each PO was made available to sell, by month, including peak. Use those actual numbers for L and σL instead of a promised figure.
Why does the max-minus-average shortcut overstate safety stock?
The shortcut, safety stock = (maximum daily sales × maximum lead time) − (average daily sales × average lead time), plans for the worst day of demand and the worst lead time happening together. That rarely happens, so it buys far more buffer than the statistical formula.
In the 12,000-order example, suppose the best sales day was 190 units and the slowest PO took 70 days. The shortcut gives 190 × 70 − 6,960 = 6,340 units of safety stock, against 1,088 from the formula. At an illustrative landed cost of $12 per unit, the extra 5,252 units tie up about $63,000 in cash and add storage fees every month. The shortcut is a reasonable start for a new SKU with little history; replace it once you have 60 to 90 days of daily sales.
Where do you find each input in Shopify and from your 3PL?
Shopify's built-in reports give you demand; the 3PL gives you the warehouse part of lead time; your supplier and forwarder records give you the rest.
| Input | Where to get it | What to watch |
|---|---|---|
| Average daily demand per variant | Shopify Analytics > Reports > Inventory sold daily by product (quantity sold per day by variant) | Quantity sold does not reflect returns or manual adjustments |
| Standard deviation of daily demand | Export daily units sold by variant for 60 to 90 days, then =STDEV.S() in a sheet | Remove stockout days and planned promotions, or demand looks lower and steadier than it is |
| Current stock to compare with the reorder point | Shopify inventory states: Available plus Incoming for the 3PL location, plus open POs not yet entered | From October 1, 2026, Shopify inventory reports use On hand, which includes Committed and Unavailable units |
| Days of cover | Shopify Analytics > Reports > Inventory remaining per product | Based on the last 28 days of sales, so it lags a trend change |
| Supplier and freight days | PO dates, supplier ship dates and forwarder arrival dates for the last six to ten POs | Use actual dates, not quoted ones, and record the spread for σL |
| Dock wait and receiving days | The 3PL's receiving records: delivery date and the date units became sellable | Ask for peak-season numbers separately |
| Receiving shortages and damage | The 3PL's receiving variance report for each PO | Short or damaged units never become Available, so a PO adds less than its paperwork says |
Compare the reorder point with stock that can still be sold. Committed units are already promised to orders, and Unavailable units (damaged, quality hold) cannot ship, so neither should count. If you hold back a separate buffer to prevent overselling, keep it out of the comparison too; the overselling guide explains how sync timing and buffers interact.
How often should a Shopify brand recalculate reorder points?
Recalculate monthly for fast movers and before every seasonal change in demand. Velocity, lead time and its variation all drift, and a reorder point set on summer sales is too low for November.
- Monthly: refresh average daily demand and its standard deviation for A-grade SKUs (Shopify's ABC product analysis report grades variants by revenue over the last 28 days).
- Each PO: log the actual date of every stage, so lead time and its spread update with real data.
- Before a launch or promotion: plan event stock separately from the reorder point. The formula covers normal demand; a drop needs its own buy, as in the product launch guide.
- After a stockout: find which input was wrong (demand, supplier, freight or warehouse days) and fix that input, not just the buffer.
Where does Orderfy fit?
Orderfy is a tech-enabled, modern 3PL for high-volume Shopify and Amazon brands, with its warehouse in the center of the US (Dallas-Fort Worth), so ground shipping reaches both coasts quickly without paying for air. For reorder planning, the parts Orderfy controls are the warehouse days in your lead time and the accuracy of the stock numbers you plan from.
- Same-day receiving: inbound inventory is received the same day, which keeps the warehouse share of lead time short and steady.
- Real-time Shopify sync: Orderfy's Shopify app is natively integrated, and inventory syncs to Shopify in real time, so Available reflects what is on the shelf.
- Orderfy WMS: the proprietary inventory software we built in-house for Shopify and Amazon sellers, included for clients. It gives full visibility into inventory and products and tracks every order at every stage, so you manage your whole inventory in one place. Many 3PLs license their WMS from a software vendor. Orderfy built its own.
- Accuracy: Orderfy tracks a 99.9% order accuracy rate in Orderfy WMS.
- Support: dedicated staff who know your brand, photo records of shipments, and direct communication channels for 24/7 access to the warehouse team.
Orderfy is not the right fit for brands that need warehouses on both coasts for one-day ground delivery, or for very low-volume stores. If you need specific inventory planning reports from your 3PL, ask during the quote and see what Orderfy WMS shows before you sign.
Frequently asked questions
The reorder point (ROP) tells you when to order; economic order quantity (EOQ) tells you how much. EOQ = √(2 × annual demand × cost per order ÷ annual holding cost per unit). Most brands set quantity by supplier minimums, case packs and cash, then use EOQ as a check.
A common method is to order enough to cover demand until the next PO arrives: average daily demand × days between orders, rounded to the supplier's case pack or minimum. Subtract any stock already on order. The reorder point decides timing; this decides size.
Enough to hit the service level you choose for each SKU, which the safety stock formula converts into units. In the 12,000-order example, 95 percent needs 1,088 units for the hero SKU. Fixed rules such as "two weeks of stock" ignore how much demand and lead time actually vary.
It is the combined formula from Peter King's 2011 APICS article: safety stock = Z × √(lead time ÷ demand period × σ demand² + (σ lead time × average demand)²). With daily demand data, the lead time ratio is simply lead time in days. It accounts for demand and lead time variation together when they are independent.
With daily demand in column B and the inputs in named cells: =NORM.S.INV(0.95)*SQRT(LeadTime*STDEV.S(B:B)^2+AVERAGE(B:B)^2*LeadTimeSD^2). Replace 0.95 with your service level. The same formula works in Google Sheets.
Shopify's built-in reports give the inputs: units sold per day by variant, days of inventory remaining and ABC grades. The reorder point itself is usually calculated in a spreadsheet or an inventory planning app that reads Shopify data.
Yes. Lead time ends when units can be sold, which for a 3PL means received, put away and showing as Available in Shopify. Use the warehouse's actual receiving times, including peak season, rather than the supplier's delivery date alone.
Get a quote from Orderfy
If you want short, steady warehouse days in your lead time and live inventory you can plan from, get a quote and tell us your SKU count, monthly orders and inbound pattern. Get a quote.
Sources
- Shopify Help Center: inventory reports (Inventory sold daily by product, Inventory remaining per product, ABC analysis; On hand change from October 1, 2026), checked October 2026
- Shopify Help Center: understanding inventory states (Available, Committed, Unavailable, Incoming), checked October 2026
- Peter L. King, "Understanding safety stock and mastering its equations," APICS magazine, July/August 2011 (safety stock formulas, performance cycle, Z-score table)