Forecast demand from your own sales
Build the forecast at SKU level, using units rather than revenue. Start with completed orders from Shopify, then remove cancellations and separate fulfilled units from returned units. For each SKU, record the period, units sold, days available, selling price, promotion status and assortment status. ORVX can sync the underlying orders and products, while its return-rate report shows units returned and refund value against revenue.
Correct for stockouts before calculating the run rate. If a SKU sold 24 units during 12 in-stock days in a 30-day period, its observed rate is 2 units per available day, not 0.8 units per calendar day; the simple adjusted forecast is therefore 60 units for 30 available selling days. Mark this as an estimate: a stockout may have suppressed demand, but it does not prove that every missed day would have sold at the same rate.
Keep promotions separate from ordinary demand. Compare promoted periods with other promoted periods, or remove the temporary uplift rather than carrying it into every future period. Exclude discontinued SKUs from the active forecast. For a new style, use an explicitly labelled analogue, such as the same category, price band and fit, until its own history exists. Treat gross fulfilled demand, expected returns and resalable returned stock as separate measures; netting them into one figure can hide the units you still need to cover.
| Measure | What it shows | When to use it |
|---|---|---|
| Fulfilled units | Outbound demand before returns | Planning units to have available |
| Returned units | Demand that may come back into stock | Estimating usable inventory and refund exposure |
| In-stock-day run rate | Sales pace adjusted for stock availability | Forecasting a SKU that stocked out |
| Promoted-period units | Demand under a specific offer | Planning a repeatable promotion, not ordinary weeks |
Lead time changes the reorder point
The reorder point is the inventory position at which you place the next order. It combines lead-time demand, the units expected to sell while a replenishment is being made, shipped and received, with safety stock, an extra buffer for demand or delivery uncertainty. The basic formula is: reorder point = lead-time demand + safety stock. Compare this point with inventory on hand plus confirmed inbound units, not just the quantity on the shelf.
For illustration only, assume a dress sells 20 units per day and you keep 30 units as safety stock. A domestic supplier with a five-day total lead time creates 100 units of lead-time demand, so the reorder point is 130 units. If a regional supplier takes 12 days, the reorder point becomes 270 units. The longer timeline requires an earlier order and leaves more cash committed to stock before the next delivery arrives.
An overseas supplier may also add production scheduling, transit, customs and receiving time. With a 35-day total lead time, the same example produces a 700-unit lead-time demand and a 730-unit reorder point. That buffer is not automatically excess: it covers the period when you cannot replenish quickly. Use the inventory reorder calculator to test each supplier timeline and safety-stock assumption before deciding when to place the order.
Worked example with sample numbers
This example is illustrative. It uses sample numbers for a women's fashion store selling in the US. They are made up to show the method, they are not a benchmark, and your own figures will differ.
Formula: Reorder point = daily units x (lead time + safety days).
| Input | Value |
|---|---|
| Units sold per day | 10 |
| Supplier lead time in days | 33 |
| Safety cover in days | 11 |
| Reorder at | 10 x (33 + 11) = 440 units |
| Cash tied up at cost | $10700.33 |
Run the same sum with your own numbers in the free calculator, or browse guides for sellers in the US and more women's fashion guides.
General information only: tax, duty and import rules change and depend on your situation, so check with an accountant or the official source before acting.
Eligible Shopify stores can see these numbers for their own store through the Founding 10,000 offer.
How to read the worked calculation
Read the worked calculation from inputs to decision. The demand input is the SKU’s expected unit sales per day, and the lead-time input is the number of days until the next delivery is available to sell. Multiplying those inputs gives lead-time demand. Safety stock is the buffer added for uncertainty; it is not expected sales.
The first output is the reorder point: lead-time demand + safety stock. This answers, “At what inventory position should I place an order?” The second check is inventory position: on-hand units + units already on order − units committed to customers. Comparing inventory position with the reorder point shows whether replenishment is due. Do not compare the reorder point with warehouse stock alone when incoming or committed units exist.
If the example includes a recommended order quantity, read it separately from the trigger. The reorder point decides when to act; the order quantity decides how many units to request under the chosen stock target. Change one input at a time to test the effect, and keep units, days and the SKU consistent. You can verify the arithmetic with the inventory reorder calculator.
What inventory costs to hold
Separate the cash tied up in stock from the cost of carrying it. If 100 dresses cost $30 each, the purchase cash committed is 100 × $30 = $3,000. That amount becomes inventory value; it is not the same as the ongoing cost of keeping those units available. A cash view helps decide whether you can afford another purchase, while a profitability view shows whether the stock earns enough after carrying costs.
Use the measure that matches the decision. ORVX reports remaining stock value and dead stock, while its product cost model computes gross profit, net profit and margins. Add your own storage, financing or handling assumptions when testing a buying decision in the inventory reorder calculator.
| Measure | Simple calculation | Act on it when |
|---|---|---|
| Purchase cash tied up | Units on hand × unit purchase cost | Deciding whether a new buy fits available cash |
| Storage | Space or warehouse charge attributable to stock | A SKU occupies costly space for too long |
| Insurance | Inventory cover allocated to the stock | Coverage cost rises with stock value |
| Handling | Receiving, picking, packing or transfer cost | Extra touches reduce unit margin |
| Markdown | Original value − expected sale value | Expected sell-through requires a price reduction |
| Damage or shrinkage | Cost of units no longer saleable | Physical losses distort stock value |
| Financing | Capital tied up × financing rate × time | Borrowed or scarce cash remains in stock |
Seasonal peaks need a separate plan
Seasonal demand should use an event-specific forecast window, not the same window as a standard replenishment. For a Valentine’s or Mother’s Day collection, forecast backward from the target delivery and wearing date. For back-to-school, Thanksgiving, Black Friday, Cyber Monday and holiday gifting, include the weeks when customers shop, the promotion period and the time needed to sell through afterward. The relevant comparison is your own performance around a similar event, adjusted for the current assortment and planned discount.
Weather adds another timing layer. Cold-weather coats may need inventory before temperatures fall, while swimwear and lightweight dresses may need to arrive before regional demand appears. Use a base case and an earlier or later scenario rather than treating a weather signal as a confirmed sale. A launch date also fixes the latest acceptable arrival date: stock that arrives after the campaign may require markdowns or miss the selling window.
Promotions change both units and cash exposure. If a campaign needs 200 additional units at a $28 cost, it commits $5,600 before any customer pays. Compare that commitment with the planned selling period, expected full-price versus discounted units, and the cash required for core products. Act earlier when supplier lead time threatens the event; act later or buy less when the calendar depends on an unconfirmed promotion or weather shift.
Sources
- South Dakota v. Wayfair, Inc., 585 U.S. 162 (2018) (Legal Information Institute, Cornell Law School, checked 26 October 2026).
How this page was made: drafted with AI from a brief, then checked by automated rules for arithmetic, sources and duplication. The sample numbers were calculated, not reported.