Inventory Forecasting for E-commerce: Formulas, Methods and Template

It is Monday, 8:40 a.m. An operations lead at a Shopify apparel brand opens last week's sales export. The best-selling hoodie is down to 260 units. Meta is spending on it every day. The supplier needs 30 days. Is 260 enough, or is the reorder already late?

Inventory forecasting answers that question with arithmetic instead of a gut call. You estimate each SKU's daily sales, multiply by your supplier's lead time, add a safety buffer, and reorder when stock hits that number. This guide gives you the formulas, the methods, a template layout, and one worked example that runs through every section. It is the hands-on side of predictive analytics for e-commerce, applied to the boxes on your shelves.

What is inventory forecasting, and what does it actually decide for an e-commerce store?

Inventory forecasting is estimating how many units of each SKU you will sell over the next lead time plus a buffer, so you know when to place a purchase order and how much to order. For an online store it decides two things. It decides whether best sellers stay in stock while ads run, and how much cash sits in boxes instead of the bank.

💰 Two tracks, one decision

Every reorder is a cash decision first. One operator on the TaxCloud and Settle ops panel put it as two train tracks: inventory on one side, cash on the other, running in parallel or going off the rails. I see this pattern constantly in stores between $1M and $5M. The forecast and the bank balance live in different tools, so nobody checks one against the other.

Shopify defines inventory forecasting as predicting future stock needs from historical sales and seasonal trends. That definition is correct. It just leaves out the part that keeps founders awake, which is the money tied up while stock sits "on the water" for weeks.

⚠️ Both ways to get it wrong cost money

• ❌ Stock out: Ryan Daniel Moran calls running out the worst thing for a young brand, because you cannot take orders. His brand waited four weeks for a shipment and lost its momentum.
• ❌ Overstock: cash sits in a warehouse instead of funding ads or the next best seller.

One Shopify merchant described the second failure plainly:

"I overstocked 500 pairs of footwear products, which sat in the warehouse for two months."
KimSeoHo, Shopify merchant, Shopify Community Thread

A reply in the same thread named the root cause:

"ordering 500 units of something that only moves 10 a month."
Andrew_185, Shopify Community member, Shopify Community Thread

✅ Where Luca AI fits

Luca AI connects Shopify, your 3PL, and your accounting tool in one place. That means the reorder question and the cash question are answered from the same numbers. You ask in plain English, "Which SKUs will run out before their next shipment lands?" and get the list with the reasoning. Luca AI does the analysis. You still decide what to buy.

How do you forecast inventory, step by step?

Forecast inventory in five steps. First, clean sales history of stockout days and one-off spikes. Second, calculate average daily sales per SKU from in-stock days only. Third, measure real supplier lead time. Fourth, set safety stock for the variation you cannot predict. Fifth, set a reorder point and review it weekly. Skipping the first step is why most store forecasts run low.

📉 Step 1: Remove the days you had nothing to sell

Here is the mistake I see most often. A Shopify sales report counts every day, including days a SKU sat at zero. Those zero days were not low demand. They were no supply.

Take one SKU over 90 days. It sold 540 units, which looks like 6.0 units a day. But it was out of stock for 18 of those days. Divide 540 by the 72 in-stock days and the real rate is 7.5 units a day.

True demand is 25% higher than the raw average shows. Over a 30-day lead time, that is 45 units you never ordered. Next month you stock out again, the average drops again, and the error feeds itself.

🧹 Step 2: Strip the one-off spikes

Spikes distort the other direction. The Hammersley Brothers describe the "Netflix effect": a documentary features a product, sales jump, then fall back. Their advice is to rebuild last year's figures without the boost before you forecast from them. I agree. A spike you cannot repeat should not set next quarter's order.

Luca AI handles this cleanup on request. Ask it to "recalculate the daily sales rate for my top 20 SKUs using in-stock days only," and it reads stock levels next to sales instead of sales alone.

⏰ Steps 3 and 4: Lead time and safety stock

Lead time is the days from purchase order to stock you can sell. Safety stock is the extra units that cover demand or delays you cannot predict. Both get their own sections below, because each has a trap that costs real cash.

🔁 Step 5: Set the reorder point and keep a cadence

The reorder point is the stock level that triggers a new purchase order. Recalculate it on a fixed rhythm:

1. Weekly for your top sellers, the 20% of SKUs that drive most revenue.
2. Monthly for the full catalog.
3. Before every promotion and after any supplier changes terms.

Even with tools, stockouts still surprise people. One forecasting builder wrote:

"I've seen situations where stockouts still catch people off guard even with the tools in place."
DemandMind, forecasting tool builder, December 2025, Shopify Community Thread

A fashion founder on Reddit described the same blind spot from the analytics side:

"I run a fashion brand on Shopify and built an analytics app after getting frustrated with how hard it is to understand product-level performance, especially once returns and stock risk come into play."
Fashion brand founder, r/ShopifyAppDev Reddit Thread

Luca AI turns this cadence into a weekly report. It lists each SKU whose reorder point moved, and why, so the Monday check takes minutes instead of a morning of exports.

What is the reorder point formula, and how much should you order?

Reorder point = (average daily sales x lead time in days) + safety stock. A hoodie selling 6 units a day with a 30-day lead time needs 180 units to cover the wait. Add 60 units of safety stock and the reorder point is 240. When stock on hand plus stock on order falls to 240, place the purchase order.

🧮 The worked example, line by line

Back to the hoodie from the opening. It sells 6 units a day, already cleaned of stockout days.

Lead time demand is the units you expect to sell while you wait for the shipment. The safety stock is your cushion if sales run hot or the boat runs late. This is the same structure Shopify and Netstock teach. The formula is not where stores go wrong. The inputs are.

❌ Three input mistakes I see every month

• Ignoring stock on order. Compare the reorder point to on hand plus on order. Otherwise you place a second PO for stock already on its way.
• Using an annual average. Annual sales divided by 365 mixes peak and slow months. Use the last 60 to 90 in-stock days for steady SKUs.
• Forgetting the minimum order quantity (MOQ). The MOQ is the smallest order a supplier accepts. If it is 500, your order is at least 500, whatever the formula says.

📦 How much to order once you hit 240

The reorder point tells you when. Order quantity tells you how much. My default for a small brand is to cover the next order cycle, not the season. If you reorder this hoodie monthly, order about 30 days of sales, or 180 units, then round up to the MOQ.

Economic order quantity (EOQ) is a textbook check on that number. It balances the cost of placing an order against the cost of holding stock. It works for stable SKUs. It ignores cash limits and promotions, so treat it as a sanity check.

Smaller, more frequent orders tie up less cash, and they let you correct a bad forecast faster. That is the asymmetric bet: if you win, you reorder; if you lose, you lose little.

✅ Where Luca AI fits

Luca AI lets you set a stock threshold for each SKU, such as 240 units for this hoodie. When stock crosses it, Luca AI sends an alert on Slack, email, or the mobile app. The alert includes the current daily rate, so you see whether 240 still holds before you sign the PO.

How much safety stock do you need?

Use the statistical formula once you have a few months of daily sales and several purchase orders. Safety stock = z x square root of (lead time x demand variance + daily sales squared x lead time variance). For the hoodie at a 95% service level, that is 53 units. The popular max-minus-average shortcut gives 220, about four times the buffer and roughly $3,000 of extra cash on shelves.

⚖️ Three formulas, one hoodie

Same SKU, same history, three very different answers. The hoodie sells 6 units a day on average, with a 30-day average lead time. Its best day was 10 units. Its slowest PO took 40 days. Daily sales vary by a standard deviation of 2.2 units. Lead time varies by about 5 days.

A standard deviation measures how far daily numbers usually stray from the average. The z value sets the service level, the share of order cycles you expect to finish without a stockout. A z of 1.65 means about 95%.

💸 Why the shortcut quietly drains cash

The shortcut assumes your best sales day and your worst shipment happen together, every cycle. They rarely do. Shopify's own guide teaches this version. The result here is 167 more units than the full formula, which at an $18 unit cost is about $3,000 sitting on a shelf.

Multiply that across 50 SKUs and the shortcut can lock up six figures. Cyndi Thomason's test in Profit First for Ecommerce Sellers helps here: track the share of inventory value older than 90 days. If that share grows as you scale, your buffers are too fat.

🎯 Why demand-only is too thin

The demand-only formula, cited by SOS Inventory, gives just 20 units. It assumes the supplier always delivers on day 30. Anyone who has waited on a delayed container knows that is not true. Lead time swings add most of the risk here: 900 of the 1,045 inside the square root come from delivery delays.

So the full formula's 53 units is the honest number. For the hoodie I round it to 60, five cartons of 12. That is the 60 in the reorder point above.

✅ Pick a service level per SKU class

• Top sellers: 95% (z = 1.65). A stockout here also wastes ad spend.
• Steady mid-tier SKUs: 90% (z = 1.28).
• Long tail: 80 to 85% (z = 0.84 to 1.04). A short stockout costs little.

Luca AI calculates each SKU's demand variability straight from your Shopify history. Ask, "What is the standard deviation of daily sales for my top 30 SKUs over 90 in-stock days?" and you get the table. The statistical formula stops being a weekend spreadsheet project.

How do you forecast supplier lead times?

Measure lead time as the days from purchase order to stock available to sell, across at least your last eight orders per supplier. Plan on the median, not the mean. One port delay can pull the mean up by days and inflate every reorder point. Cover delay risk with the lead time variation term in safety stock instead.

📅 What counts as lead time

Most stores measure lead time from PO to "shipped." That undercounts. The clock should stop when units are sellable on your Shopify store. That includes freight, customs, the 3PL's receiving queue, and putaway.

A 3PL (third-party logistics warehouse) can take several days to receive a container in peak season. Those days are part of your lead time. If you skip them, every reorder point you set is short by that many days of sales.

📊 Why the median beats the mean

Kunle Campbell makes this point in E-commerce Growth Strategy. He advises basing lead time averages on the median, not the mean, to remove large outliers. I agree, with one condition: the outlier still belongs in your safety stock math.

Here are one supplier's last eight purchase orders, in days: 29, 30, 31, 32, 33, 34, 37, and 58.

Eighteen units on one SKU looks small. Across 80 SKUs from that supplier, it is 1,440 extra units ordered every cycle because of one late boat. The median plans for the normal order. The lead time variation in the Q4 formula covers the bad one.

⚠️ The delays you cannot average away

Ryan Daniel Moran tells the story of a founder whose Christmas order sat in customs for two weeks, even after paying for air freight. No average predicts that. A median lead time plus a statistical buffer is still your best defense, and an earlier order before peak season is cheaper than any expedite fee.

🌍 Short lead time vs low unit cost

Lead time is also a sourcing choice. The Hammersley Brothers compare a local supplier with about a 7-day lead time to a Chinese supplier at around 3 months. The overseas unit cost is lower. But you need a bigger warehouse and much more cash sitting on the sea.

• ✅ Short lead, higher unit cost: smaller orders, smaller buffers, faster correction.
• ❌ Long lead, lower unit cost: bigger orders, bigger buffers, more cash at risk if the forecast misses.

For a brand under $5M in revenue, I lean toward shorter lead times on best sellers. The margin gain overseas often disappears into extra safety stock.

✅ Where Luca AI fits

Luca AI runs vendor performance analysis across your purchase order history. Jen Van Wart, CEO of Twigs, described spending three weeks before each buying market pulling these reports by hand. Ask Luca AI, "Which suppliers delivered later than their median in the last six months?" and the answer arrives with each supplier's spread.

Which inventory forecasting method should you use for each SKU?

Match the method to the SKU. Use a moving average for steady sellers and exponential smoothing for SKUs whose demand drifts. Add a seasonal index for anything with a peak, and use judgment plus an analog SKU for launches with no history. Then check each method monthly with MAPE, the average percentage gap between forecast and actual sales, and keep the closest.

🧭 The method table

Red Stag Fulfillment's guide covers the same families. The difference that matters for a small brand is not which method is smartest. It is which one you can check every month.

🔢 One SKU, two methods

A SKU sold 40, 42, 45, and 49 units over the last four weeks. Demand is climbing.

• 4-week moving average: (40 + 42 + 45 + 49) / 4 = 44 units next week.
• Exponential smoothing with a weight of 0.5 on the newest week: the forecast steps 40, 41, 43, then 46 units.

The next week sells 52. The moving average missed by 8 units. Exponential smoothing missed by 6. On a trending SKU, the method that leans on recent weeks wins.

🎯 Score the methods with MAPE

MAPE stands for mean absolute percentage error. For each period, take the gap between forecast and actual, divide by actual, then average those percentages. Here, the moving average scored 8 / 52 = 15.4% and smoothing scored 6 / 52 = 11.5%.

Do this over at least eight weeks before switching methods. One week can flatter either one.

⚠️ When ARIMA is overkill

ARIMA (AutoRegressive Integrated Moving Average) is a statistical model that learns patterns from long histories. For a store with a few hundred SKUs and two years of data, it rarely beats a clean moving average by enough to matter. Common Thread Collective's point holds: forecasting is an exercise in execution as much as modeling. A simple method on clean data beats a clever model on dirty data.

✅ Where Luca AI fits

Luca AI predicts sales from your historical Shopify data and compares forecast to actual per SKU when asked. Ask, "Which SKUs missed their forecast by more than 20% last month?" and you get the list with likely causes, such as a stockout, a promotion, or a traffic drop.

How do you plan inventory demand for seasonality, promotions and new products?

Inventory demand planning starts from the clean baseline and adds what you already know is coming. Apply a seasonal index for the month, a lift factor for each planned promotion, and repeat demand from past customers. For new products, borrow the curve of a similar SKU, order small, and keep the reorder fast.

🍂 Seasonal index, calculated correctly

A seasonal index is how much a month sells compared with an average month. Divide the month's sales by the average monthly sales for the year.

Here is an illustrative example. A store sold 12,000 units last year, so an average month is 1,000 units. November sold 1,800. November's index is 1,800 / 1,000 = 1.8.

Apply it to the hoodie. Its clean baseline is 6 units a day, or 180 a month. November demand is 180 x 1.8 = 324 units. Your November reorder point must use that rate, not the yearly one.

📣 Promotions and the golden goose

A lift factor is how much a promotion multiplies normal sales. Measure it from your own past campaigns, not a vendor benchmark. If last Black Friday's week sold 2.5 times a normal week, plan with 2.5 on the same offer.

The bigger risk is running out mid-campaign. The Hammersley Brothers describe it well: when a best seller stocks out, the ad team shifts spend to other products. Those products cannot replace the "golden goose," so revenue starts to yo-yo. A stockout during a promotion wastes the ad budget twice.

One forecasting builder noticed the quiet version of this pattern:

"When a SKU shows steady week-over-week growth (even small), it often runs out of stock earlier than expected."
DemandMind, forecasting tool builder, December 2025, Shopify Community Thread

🔁 Repeat demand is your tailwind

The Hammersley Brothers also forecast a "tailwind" from past customers. In their example, about 30% of last year's buyers buy again, 10% from two years ago, and 5% from three years ago. Apply your own rates to your own customer cohorts.

If you had 2,000 buyers last year, 3,000 two years ago, and 2,000 three years ago, that is 600 + 300 + 100 = 1,000 repeat customers. Those orders come before you spend a dollar on ads.

🚀 New products with no history

Pick an analog, a past SKU with a similar price, audience, and channel. Use its first 60 days as the launch curve. Order a small first batch, then reorder fast once real sales arrive. Being slightly short for a week costs less than a warehouse of a product nobody wanted.

✅ Where Luca AI fits

Luca AI connects Meta, Klaviyo, and Shopify, so promo lift comes from your own past campaigns. Ask Luca AI, "What was the sales lift on the hoodie during last year's Black Friday email and Meta push?" and plan from your number.

How should you split stock across warehouses and channels?

Forecast each warehouse or channel separately and set reorder points per location. A SKU can stock out in one place while another holds plenty. Keep fast movers near most customers, keep the long tail in one central location, and pool safety stock where you can. One shared buffer needs fewer units than two separate ones.

🏬 Splitting stock multiplies the buffer

Split the hoodie's 6 units a day evenly across two warehouses. Each now sells 3 a day, and each sees more relative noise. The demand-driven part of safety stock grows by roughly the square root of the number of locations.

That is 40% more buffer for the same total demand, assuming the two regions' sales move independently. So split only the SKUs that earn faster delivery.

🚚 Express stock vs long-lead stock

One operator, Nicolo Grange, described a hybrid in a recorded interview. His team stocks about 3,000 of 10,000 labels locally for express shipping. The other 7,000 ship from a Hong Kong warehouse with a 15 to 20 day lead time. Best sellers stay close. The long tail stays central.

Small details matter here too. Carole, a seller featured in Profit First for Ecommerce Sellers, ships single-SKU cartons so the warehouse cannot misfile stock under the wrong SKU.

⚠️ Channel and store data must not double count

Multi-store setups break many tools. One Polar Analytics customer reported this:

"I've also reported an issue with inventory levels, as our inventory is multiplied with 6, since we have 6 different shopify stores connected to the same warehouse."
Maja, Polar Analytics customer, November 2025, Polar Analytics - Trustpilot Verified Review

A Triple Whale reviewer flagged the channel version, listing among their dislikes:

"Sales velocity and stock demand calculations"
G2 reviewer, Triple Whale user, February 2025, Triple Whale - G2 Verified Review

✅ Where Luca AI fits

Luca AI reads stock by location from your 3PL feed alongside Shopify sales. It alerts you when one location crosses its threshold while another sits overstocked. That way a transfer can happen before a reorder.

How do you predict stockouts and dead stock before they cost you?

Predict stockouts by comparing days of cover with lead time. Days of cover is stock on hand plus on order, divided by daily sales. When cover falls below lead time plus safety days, you are already late. Predict dead stock by tracking the share of inventory value older than 90 days. If it holds or grows while revenue grows, buying is getting worse.

⏰ Days of cover: the stockout early warning

Days of cover tells you how long current stock lasts at today's sales rate. Compare it with what you need to survive the wait.

Take the hoodie. It has 260 units on hand and nothing on order. At 6 units a day, that is about 43 days of cover. The supplier needs 30 days, and 60 units of safety stock equals 10 more days. So the hoodie needs 40 days of cover. It has 43. The reorder window closes in about three days.

Run this for every SKU weekly. Sort by days left. The top of that list is your purchasing agenda.

🪦 The 90-day test for dead stock

Dead stock is inventory that has stopped selling at a useful pace. It rarely announces itself. Cyndi Thomason, in Profit First for Ecommerce Sellers, cites Robyn Johnson's test: watch inventory older than 90 days as a percentage of total inventory value. If that share is flat or rising while you scale, your buying is not improving.

Here is an illustrative example. You hold $200,000 of inventory, and $50,000 of it is older than 90 days. That is 25%. If it was 20% last quarter while revenue grew, slow stock is piling up faster than sales.

💸 Why dead stock hurts more than it looks

Thomason describes the trap in her book. A founder feels rich in January after Q4 sales, then finds no cash for the slow summer months. Aged stock is part of that gap. The money exists on the balance sheet but not in the bank.

Margin math makes it worse. Supply Chain Lara puts it bluntly: "Gross margin is a lie." Gross margin covers what a product costs to make, not what it costs to sell. Storage, pick and pack, shipping, returns, and ad spend all come after. Slow stock keeps adding storage fees every month it sits.

✅ What to do with each signal

• ✅ Cover below lead time plus safety days: place the PO this week, or expedite part of it.
• ⚠️ Aged share rising: stop reordering the slowest SKUs and plan a markdown before the next peak.
• ❌ Both at once: you are buying the wrong things. Shift budget from slow SKUs to the ones running short.

Luca AI scans your Shopify and 3PL data around the clock. It pings you when a SKU's days of cover drops below its lead time, or when the aged stock share rises, without anyone opening a dashboard.

How does inventory forecasting work on Shopify?

Shopify gives you the inputs for inventory forecasting: sales by product and variant, and inventory by location. It does not calculate reorder points, safety stock, or supplier lead times for you. Stores either export to a spreadsheet, install a forecasting app, or connect Shopify to an analytics layer that reasons across sales, ads, and supplier data.

🛒 What Shopify gives you natively

Shopify's analytics include sales reports by product and variant. Depending on your plan, inventory reports such as sell-through rate and days of inventory remaining are also available. Sell-through rate is the share of received units you have sold. Shopify also tracks stock by location when you use more than one warehouse.

What is missing is the forecasting layer. Shopify does not know your supplier's lead time, your MOQ, or what is on order. Without those, no reorder point can be calculated inside Shopify alone.

📄 The spreadsheet path, and where it breaks

Most stores start by exporting reports into Excel or Google Sheets. That works until the refresh becomes the job. One Reddit reply summed up the friction:

"Shopify has built-in reports you can export, but on the lower-tier plans you only get the basics and you'll need to log in and pull them manually. Even on higher plans, Shopify doesn't really offer automated scheduled exports by itself."
Reddit user, r/shopify, Reddit Thread

Richie Jones, CEO of VAST, remembers the early version of this. Monday mornings went to manual reports built from Shopify and returns-system exports. Every hour spent rebuilding a sheet is an hour not spent fixing what the sheet shows.

⚠️ Apps and built-in AI: verify before you trust

Forecasting apps automate the math. Built-in AI features need checking. Deb Farnworth-Wood, CEO of Issada, said her inventory system's own AI forecasting was "rubbish." Her team tried it for about six months, saw it "hallucinating and telling fibs," and shut it down.

My rule: before trusting any tool's reorder date, check three SKUs by hand with the Q3 formula. If the numbers disagree and the tool cannot explain why, do not hand it your purchasing.

✅ Where Luca AI fits

Luca AI plugs into Shopify and normalizes the data as it comes in, so there is no cleanup project first. Ask, "Which SKUs hit their reorder point this week, and what is the reasoning?" Luca AI shows the inputs behind each answer, so you can check them against your own math.

What should an inventory forecasting Excel template include?

An inventory forecasting Excel template needs one row per SKU. Columns cover units sold, in-stock days, daily sales rate, median lead time, demand and lead time standard deviations, service level z, safety stock, reorder point, on hand, on order, days of cover, and a reorder flag. Every number should be a formula on raw exports, never a typed guess.

📋 The column layout

Build this in Excel or Google Sheets. Paste raw exports into separate tabs, and keep this tab formula-only.

Column I is the full safety stock formula from Q4. Column E uses the median from Q5. Column C applies the stockout cleanup from Q2.

🧪 Test it on one SKU first

Load the hoodie and check the output by hand. You should see a daily rate of 6, a reorder point near 240, and about 43 days of cover. If the sheet disagrees, a formula or an export range is off. Fix that before you add more SKUs.

⚠️ Where spreadsheets break

• Size runs. Jen Van Wart, CEO of Twigs, described a $6,000 commitment for one denim style across sizes 23 to 32. That is ten rows per style, each with thin history.
• Many locations. Each warehouse needs its own row set.
• The weekly refresh. Exports, pastes, and broken ranges eat the morning.

My rough line: below about 100 active SKUs, a sheet is fine. Past that, the refresh becomes a part-time job.

Luca AI can send this same reorder table as a scheduled weekly report on Slack or email, with reasoning for each flagged SKU. The template logic stays the same. The refresh disappears.

What does a full inventory forecast look like for one store?

For a hoodie selling 6 units a day, a 30-day lead time, and 53 units of safety stock rounded to 60, the reorder point is 240. With 260 on hand and nothing on order, the store has about three days to place the purchase order. Size it to cover the next order cycle, not a whole season.

🧵 The store and the inputs

Picture a Shopify apparel brand doing about $120,000 a month, running Meta ads, with one overseas supplier. It is Monday, and the hoodie is the hero product. Here is every input, pulled from the sections above:

1. Clean daily rate: 6 units, in-stock days only (Q2).
2. Lead time: 30-day median across recent POs (Q5).
3. Safety stock: 53 by the full formula, rounded to 60, five cartons of 12 (Q4).
4. Reorder point: 6 x 30 + 60 = 240 (Q3).
5. Days of cover: 260 / 6 = about 43, against 40 needed (Q9).

💰 The decision and the cash

The store places the PO this week. It orders one month of sales, 180 units, which clears a 150-unit MOQ in this example. At $18 a unit, that is $3,240 of cash committed.

Compare that with a store using the shortcut buffer of 220. Its reorder point would be 400, and it would have ordered weeks ago. It would also carry 160 more units of buffer than the 60 the numbers support, about $2,900 at $18. Same hoodie, same sales, very different bank balance.

Cyndi Thomason's habit fits here: keep a separate bank account just for inventory. A seller in her book called that account "sacred." Fund it from every sale, so the next PO never competes with payroll or ad spend.

🎲 Next week's routine

The Hammersley Brothers compare stock ordering to a roulette table. Their fix is to make each order an asymmetric bet: heads you win a lot, tails you lose little. Small, frequent orders on proven SKUs do exactly that.

So next Monday, repeat the check. Rerun days of cover. If an October order will land in November, apply the seasonal index from Q7 first. Watch the median lead time when the PO arrives.

Luca AI can run this check across every SKU each Monday and send only the ones that need a purchase order, with the inputs shown. You make the call. Luca AI does the arithmetic and keeps watching.

If your best seller is closer to its reorder point than you would like, start with that one SKU this week. Then tell us what broke when you ran the numbers. That is usually where the real forecast begins.

References

1. KimSeoHo and Andrew_185, "Inaccurate stock preparation leads to overstocking," Shopify Community. Shopify Community Thread
2. DemandMind, "A simple way to spot stockout risk in Shopify data," Shopify Community, December 2025. Shopify Community Thread
3. Fashion brand founder, r/ShopifyAppDev. Reddit Thread
4. Reddit user, r/shopify, on Shopify report exports. Reddit Thread
5. Maja, Polar Analytics review, Trustpilot, November 2025. Polar Analytics - Trustpilot Verified Review
6. G2 reviewer, Triple Whale review, G2, February 2025. Triple Whale - G2 Verified Review
7. Hammersley Brothers, "The Ecommerce Forecasting Mistake That Kills Growth," podcast episode.
8. Supply Chain Lara, "Profitable on Paper, Broke in Reality: The Math You're Avoiding," video.
9. Deb Farnworth-Wood (Issada), Richie Jones (VAST), Jen Van Wart (Twigs), and Nicolo Grange, recorded operator interviews.
10. Common Thread Collective, "Unbiased eCom Forecasting Starts with a Reality Check."
11. Cyndi Thomason, Profit First for Ecommerce Sellers (book), citing Robyn Johnson on the 90-day inventory age metric.
12. Ryan Daniel Moran, 12 Months to $1 Million (book).
13. Kunle Campbell, E-commerce Growth Strategy (book).
14. Shopify, "Inventory forecasting," Shopify Blog. Shopify Blog
15. Red Stag Fulfillment, "Inventory forecasting: Formulas, methods & step-by-step guide." Red Stag Fulfillment
16. Netstock, "Reorder point formula." Netstock
17. SOS Inventory, "Reorder point." SOS Inventory