Shopify Inventory Forecasting: Complete Guide for 2026

Search "how to forecast inventory for Shopify" and most results either sell you software before explaining the math, or explain the math with numbers that don't actually add up when you check them. This is the complete version: what forecasting is, how to actually do it by hand this week, which method fits your catalog, how to turn a forecast into an order, and how to keep it from going stale.

One running example carries through this whole guide — a candle called Cedar & Fig, 250g, selling 5 units a day, with a 12-day supplier lead time and a 30-unit safety stock buffer. It's the same example used across our inventory posts, so the numbers stay consistent from one guide to the next rather than resetting every time.

What Is Inventory Forecasting?

Inventory forecasting is the practice of estimating how much of a product you'll sell over a future period, using your own sales history, so you can decide how much stock to have ready before you need it. It's a number built from real sales data, not a guess, and it comes with a margin of error that narrows the more data you feed it. For the full definitional treatment, including how it differs from "demand forecasting" and the signs a store has outgrown guessing, see what inventory forecasting actually means. This guide picks up from there and walks through actually doing it.

Forecasting is one piece of a bigger discipline. See our inventory management fundamentals guide for the full picture, including stock counts, discrepancy prevention, and the metrics that tell you whether the stock you're holding is the right stock.

Why Shopify Stores Need It

Two mistakes happen to a store that isn't forecasting, and they pull in opposite directions. A product that's actually selling runs out, and the sale (sometimes the customer, for good) goes to whichever competitor still has it in stock. A product that isn't selling as well as the launch-week numbers suggested gets over-ordered, and the cash that paid for it sits in a warehouse instead of funding the next thing the store actually needs.

A forecast doesn't make either mistake impossible. No forecast is ever exactly right, and treating one as a guarantee is its own mistake, but it replaces a gut-feel number with one built from what customers have actually been buying. That's the difference between reacting to a stock problem after it happens and seeing it coming a few weeks out. If the terms demand planning and inventory planning get used loosely around your team, the FAQ at the end of this guide untangles them.

How to Forecast, Step by Step

Here's the process end to end. Each step is explained enough to actually do it; the sections after this one go deeper on the pieces that need it.

1. Pull your sales history. At minimum, 60 to 90 days for an established SKU — more if the product has a seasonal pattern you want the forecast to reflect. Where this data actually lives in Shopify, and how to clean it up before you calculate anything, is covered further down.
2. Calculate your baseline velocity. Divide total units sold by the number of days in your window. Cedar & Fig, 250g sold 450 units over the last 90 days: 450 ÷ 90 = 5 units a day. Every other number in this guide builds on that single rate.
3. Pick a method that fits the pattern. A flat, steady seller is a different forecasting problem from one with a strong trend, a seasonal swing, or almost no sales history at all, and the right method depends on which one you're looking at. Compared next.
4. Adjust for what a flat average won't catch. A steady velocity number assumes life stays boring, and it often doesn't. A real seasonal pattern, heavier in December, dead in July, gets smoothed away by a plain average; see forecasting seasonal inventory for building a seasonal index instead. A planned promotion causes a temporary spike a plain average will wrongly treat as the new baseline afterward; see how promotions affect inventory forecasting for estimating the uplift and resetting the baseline once it's over. And if a supplier's lead time isn't as fixed as the formula below assumes, that variability changes how much buffer the forecast needs on top of it; see how supplier lead times affect your forecast for the mechanics.
5. Turn the forecast into a number you can act on. A daily sales rate isn't itself a decision. You still need to know when to reorder and how much to order. Both formulas are below.
6. Check it against what actually happened, and adjust. A forecast that's never compared to real sales just quietly drifts wrong over a season. Covered in Keeping It Accurate, at the end.

Choosing a Method

For most established SKUs with steady, non-trending sales, a simple moving average (the average of your last several weeks of sales) is enough, and it's the default we'd point most small catalogs toward. It breaks down once a product's sales are trending up or down, because a plain average lags behind a moving trend rather than tracking it. Our moving average deep dive walks through the formula and exactly where it stops working; the full comparison of methods covers the alternatives, including exponential smoothing, which reacts faster to recent weeks at the cost of being noisier.

None of the above have anything to work from for a brand-new SKU with zero sales history. That's a different problem with a different method: comparing the new product to similar ones already in the catalog rather than averaging history that doesn't exist yet. See forecasting inventory for new products for how that works.

Calculating What You Need

A forecast on its own is a rate, units per day. Turning it into a decision means answering two different questions, and conflating them is one of the more common mistakes in this cluster.

The first is when to reorder — the reorder point.

Reorder Point = (Average daily sales × Lead time in days) + Safety stock

Back to Cedar & Fig, 250g: 5 units a day, a 12-day supplier lead time, and a 30-unit safety stock buffer. Reorder point = (5 × 12) + 30 = 60 + 30 = 90 units. When stock drops to 90, the purchase order goes out, not earlier, not later. The full formula, including how to size that safety stock buffer for a spikier seller or a less reliable supplier, is in our reorder point guide.

The second is how much to order — a target stock level, and it isn't the same number as the reorder point above. Once you know your forecast, your lead time, and how much buffer you want, that quantity is its own calculation, walked through in how much inventory you should keep.

Using Your Shopify Sales Data

Every forecast starts with real sales numbers, and Shopify has them spread across a few different places. Orders → Export in the admin gives you the rawest version, every line item, every SKU, every date, as a CSV. The Total sales by product and Total sales by variant reports are faster to skim, but each aggregates your whole selected date range into one row per product, so on their own they won't hand you a day-by-day trend; getting an actual time series means running them repeatedly over shorter windows, or using Total sales over time with product columns added.

Which report to pull, how far back to look, and what to clean out before you calculate anything (bulk orders, test orders, refunds) is its own guide: forecasting demand using your Shopify sales data.

Keeping It Accurate

A forecast isn't a one-time calculation. Sales velocity drifts, suppliers change their lead times without telling you, a product goes viral or quietly stops selling, and a forecast built once in January is wrong by June whether or not anyone noticed. The habit that keeps a forecast useful is comparing it against what actually happened and adjusting, not recalculating from scratch every time something feels off. How to improve forecast accuracy covers measuring that gap in plain terms; common forecasting mistakes is the checklist version of the same problem.

Doing all of this by hand is where forecasting stops being a spreadsheet exercise you set up once and starts being a recurring job. Recalculating velocity, checking every supplier's real lead time, watching every SKU against its own reorder point: across a catalog of even a hundred SKUs, that's a weekly task almost nobody actually keeps up with once a busy season hits. That's what StockCue exists to do: it recalculates demand forecasting with seasonality from your store's own order history automatically, on every plan including Free, and Growth adds a "why this qty?" breakdown behind every suggested order so the number isn't a black box.

Where to go next

Every deeper topic in this guide, in one place:

• What forecasting actually means, in more depth
• Using your own Shopify sales data
• Comparing forecasting methods
• The moving average formula, worked
• How much inventory you actually need
• Forecasting around supplier lead times
• Forecasting around a promotion
• Forecasting seasonal demand
• Forecasting a brand-new product
• Improving forecast accuracy
• Common forecasting mistakes to avoid

Frequently Asked Questions

What is inventory forecasting?

Inventory forecasting is the practice of estimating how much of a product a store will sell over a future period, based on its own sales history, so it can decide how much stock to have ready in advance. It's built from real sales data rather than guesswork, and it comes with a margin of error rather than a guaranteed number. The output typically feeds directly into a reorder point and an order quantity.

How do I start forecasting inventory for my Shopify store?

Pull at least 60 to 90 days of sales history for your best-selling SKUs and calculate the average daily sales for each — that single number is a working baseline velocity. From there, a simple moving average and the reorder point formula are enough to start; more advanced methods and adjustments for trend or seasonality can come once that foundation is in place.

Is inventory forecasting the same thing as demand forecasting?

In practice, on a Shopify store, yes — the terms are used interchangeably. Technically, demand forecasting predicts what customers want to buy, and inventory forecasting turns that prediction into a stocking decision, but for most single-location or online-only stores those collapse into the same calculation.

What is the difference between demand planning and inventory planning?

Demand planning estimates how much customers will want; inventory planning decides what to actually order and hold, using that estimate alongside lead time, cash and storage. The first produces a number, the second turns it into a purchase order. Most small Shopify stores fold both into one motion — often the same spreadsheet tab — and that is fine, as long as both questions get answered: how much will sell, and how much should I hold. The distinction starts to matter when different people own each step, because that is when a forecast can be right and the order still wrong.

Do I need special software to forecast inventory on Shopify?

No. A spreadsheet with your sales history and the moving average or reorder point formula gets you a working forecast. Software earns its place once you're recalculating those formulas across more SKUs than you can keep up with by hand, or want the forecast to update automatically as sales patterns shift.