Inventory how-to

How to exclude wholesale orders from your retail forecast

Separate your wholesale orders from retail orders before they hit your demand forecast, otherwise a single large wholesale order will make the forecast think retail demand spiked.

Short answer: Separate your wholesale orders from retail orders before they hit your demand forecast, otherwise a single large wholesale order will make the forecast think retail demand spiked. The cleanest fix is to tag or channel-separate wholesale orders in your store so forecasting tools can filter them out.

Why wholesale orders distort a retail forecast

A retail forecast works by looking at recent daily sales per variant and projecting that forward. A wholesale order is usually one big order placed rarely, not a pattern of daily customer demand. If it gets counted the same as retail sales, the forecast reads it as a demand surge and will suggest you reorder far more stock than retail customers actually need.

Separate wholesale at the order level

The most reliable method is to make wholesale orders identifiable at the source. Use a separate sales channel, a different price list, or tag every wholesale order with something consistent like "wholesale" at checkout or when you manually create the order. If wholesale customers order through a separate channel or draft order process, this happens automatically.

Filter or adjust before forecasting

Once wholesale orders are tagged or separated, you need a way to exclude them from whatever feeds your forecast. If you are doing this manually in a spreadsheet, filter out any order with the wholesale tag before calculating average daily sales. If you use a forecasting tool, check whether it can filter by order tag, channel, or customer type, and set that up once rather than adjusting it every time you place a big wholesale order.

Step by step

  1. Tag every wholesale order consistently, for example with an order tag or a separate sales channel
  2. Pull a sales export and confirm the tag or channel field is present on past wholesale orders too
  3. Calculate daily retail sales per variant using only untagged (retail) orders
  4. Recalculate your 28-day sales average after removing wholesale volume for any SKU that had a wholesale order in that window
  5. Set reorder points and order quantities using the cleaned retail-only daily sales figure
  6. Review monthly to make sure new staff or new order types are still being tagged correctly

Worked example

Say a SKU sold 140 units at retail over the last 28 days, averaging 5 units a day. One wholesale customer also placed a one-off order for 200 units in that window. If you forecast on total sales of 340 units, your daily average jumps to about 12 units a day, more than double the real retail rate. Stripping out the 200-unit wholesale order gets you back to the true 5 units a day, which is what should drive your retail reorder point.

How SteadyShelf helps

SteadyShelf forecasts from your store's sales data and does not have a built-in wholesale filter or multichannel sync, so it cannot automatically separate wholesale from retail orders today. The practical workaround is to keep wholesale orders out of the sales channel or order types SteadyShelf reads from, or to tag them and manually adjust the sales window before trusting the suggested order quantity for affected SKUs.

$29/month flat, unlimited products and suppliers, 30-day free trial. Run the numbers now with the free reorder point calculator.

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FAQ

Can I just exclude wholesale customers by tagging the customer instead of the order?

You can, but it is less reliable if a wholesale customer ever places a small retail-style order, since that order would also get excluded. Tagging at the order level is more accurate because it reflects the actual transaction type rather than assuming every order from that customer is wholesale.

What if wholesale and retail orders are mixed and I cannot separate past data?

Start tagging going forward and accept that your historical average will be skewed until you have a clean window of retail-only data, typically a few weeks to a month. Until then, treat any forecast as rough and double check order quantities manually against what you know about real retail demand.

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