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Blog›E-commerce

Stop Guessing on Inventory: Know What to Reorder and When

Stockouts cost you sales. Dead stock costs you cash. AnalityQa AI AI tells you exactly which SKUs need attention today — and backs every recommendation with your own order data.

Try AnalityQa AI AI free →See live examples
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The problem

  • →Shopify's inventory reports show current stock levels but give no signal on velocity, days-of-cover, or how close each SKU is to a stockout.
  • →Dead stock accumulates silently — units that stopped moving months ago still appear on the balance sheet until someone manually audits every SKU.
  • →Reorder points are set once and never updated, so fast-moving SKUs hit zero while slow movers pile up warehouse space.
  • →Inventory data sits in one export, sales velocity in another, and supplier lead times in a spreadsheet — joining them is a recurring manual job.

Why the usual approach breaks down

Shopify inventory and sales data are separate exports

Shopify's inventory CSV shows on-hand quantity. Your order-line-items export shows what sold and when. Joining them to compute days-of-cover per SKU requires a VLOOKUP or SQL join that most ops teams rebuild from scratch each time they need the number.

Excel joins break as soon as SKU names drift

A trailing space, a capitalisation change, or a variant rename in Shopify is enough to break a VLOOKUP. The result is phantom stockouts or missing SKUs in the analysis — and no easy way to know which rows failed silently.

Velocity-based reorder math requires rolling windows

Computing a 30-day average daily units sold, adjusting for recent trend direction, and then dividing into on-hand quantity to get days-of-cover is straightforward in SQL but painful in Excel. Most teams skip the math and use gut feel instead.

No single view of dead stock value across variants

A product with 12 colour-size variants may have three variants selling well and nine collecting dust. Shopify does not surface this at the variant level in a single report. Finding the dead stock requires filtering every variant individually.

How AnalityQa AI AI solves it

Upload your data — or connect it live — and ask in plain English.

01

Upload your Shopify inventory and order exports together

Drop your inventory CSV and your order line-items CSV into the same AnalityQa AI AI session. The system auto-joins them on SKU or variant ID, infers your units-sold velocity, and computes days-of-cover for every SKU — no schema mapping required.

02

Ask stockout-risk questions in plain English

Type 'Which SKUs will hit zero stock within 14 days at current sell-through rate?' and receive a ranked table showing SKU, current stock, daily velocity, and projected days remaining. No formulas. No manual calculations.

03

Identify dead stock by value in seconds

Ask 'Show me all SKUs with zero sales in the last 60 days and their current inventory value' and AnalityQa AI AI cross-references your cost or price column against on-hand quantity to surface the exact dollar value of non-moving stock.

04

Get reorder recommendations ranked by urgency

AnalityQa AI AI computes a simple reorder point for each SKU based on average daily velocity and a lead time you specify in the chat. The output is a prioritised table you can export directly to share with your supplier or 3PL.

05

Pin an inventory health dashboard that refreshes on each upload

Pin your stockout-risk chart, dead-stock table, and reorder list to a shared dashboard. Every time you upload a fresh Shopify export, the dashboard updates automatically — giving your team a live inventory health view without recurring manual work.

You askedGenerated in 4.2s

"Which SKUs are at risk of stocking out in the next 21 days based on current sales velocity?"

Total

12,840+9.2%

Average

324+4.1%

Top segment

38%+2pp

Table: SKUs at stockout risk — days of cover, current stock, daily velocity

Last 12 mo

Table: dead stock by SKU — units on hand, days since last sale, estimated value

Table: reorder recommendations — SKU, units to order, urgency tier

A dashboard built in AnalityQa AI — from question to chart, no SQL.

Real examples

Paste your data. Ask. Ship.

You

Which SKUs are at risk of stocking out in the next 21 days based on current sales velocity?

AI

AnalityQa AI AI computes average daily units sold for each SKU over the last 30 days, divides it into current on-hand stock, and returns every SKU with fewer than 21 days of cover, ranked by urgency.

Table: SKUs at stockout risk — days of cover, current stock, daily velocity
You

What is the total value of dead stock — units with no sales in the last 90 days?

AI

The system filters order records to identify SKUs with zero sales in the trailing 90 days, then multiplies on-hand quantity by unit cost (or selling price if cost is unavailable) to compute total dead-stock value.

Table: dead stock by SKU — units on hand, days since last sale, estimated value
You

Give me reorder recommendations for all SKUs with fewer than 30 days of cover, assuming a 14-day supplier lead time.

AI

AnalityQa AI AI identifies every SKU below the 30-day cover threshold, calculates the quantity needed to restore a 45-day buffer given 14-day lead time and current velocity, and returns a prioritised reorder list.

Table: reorder recommendations — SKU, units to order, urgency tier
You

Show me the top 10 SKUs by inventory turnover ratio for the last 6 months.

AI

The system divides cost of goods sold (derived from units sold and cost column) by average on-hand inventory for each SKU over the 6-month window, then ranks SKUs by turnover ratio.

Bar chart: top 10 SKUs by inventory turnover ratio
You

How has days-of-cover changed month over month for my top 20 revenue SKUs this year?

AI

AnalityQa AI AI computes end-of-month on-hand stock and that month's average daily velocity for each SKU, then plots days-of-cover as a line per SKU over the months selected.

Line chart: days-of-cover trend by SKU — top 20 revenue SKUs, monthly

What teams get out of it

✓Teams spot SKUs approaching stockout days before they actually hit zero, rather than discovering the problem after losing sales.
✓Dead-stock audits that previously took hours of manual filtering are completed in a single chat query.
✓Reorder decisions are based on actual velocity data rather than remembered rules of thumb.
✓Inventory health reporting that required weekly analyst effort is replaced by a dashboard that updates on each Shopify export.

Frequently asked questions

Does AnalityQa AI AI connect directly to Shopify, or do I need to export CSVs?+

You upload Shopify CSV exports or sync a Google Sheet populated via Shopify's built-in integration. A direct Shopify API connector is on the roadmap. The CSV workflow handles files up to 100 MB, which covers most stores with years of order history.

Can I analyse inventory across multiple Shopify stores in one session?+

Yes. Upload inventory and order exports from each store as separate files in the same session. AnalityQa AI AI keeps them distinguishable by filename and lets you query across stores or filter to a single one within the same conversation.

How do I specify supplier lead times for reorder calculations?+

You provide lead time in the chat — for example, 'assume a 12-day lead time for all SKUs' or 'lead time is 7 days for supplier A and 21 days for supplier B.' If you have a CSV with per-SKU lead times, you can upload that as a third file and the system will join it automatically.

How fresh is the data?+

Data is as fresh as your latest upload. If you use a Google Sheets connection, the analysis reflects the sheet's current state whenever you ask a question. Most inventory teams run a daily or weekly upload cadence, which is sufficient for reorder planning on most product catalogues.

Is my inventory and sales data stored securely?+

Your uploaded data is encrypted in transit and at rest, isolated per tenant, and never used to train models. You can delete your data at any time from account settings.

Do I need SQL knowledge to run these analyses?+

No. Every calculation — days-of-cover, turnover ratio, dead-stock value — is triggered by plain-English questions. If a result looks unexpected, you can refine it in the same conversation: for example, 'Exclude bundles from that dead-stock calculation.'

What plan do I need for inventory analysis with multiple files?+

Multi-file joins — such as combining your inventory export with your order line-items — require the Pro plan. The Pro plan also includes dashboard pinning and forecast features, which most inventory use cases rely on. The Starter plan supports single-file sessions.

Related guides

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Know Which Products Are Actually Making You Money

E-commerce

E-commerce Sales Analysis Without the Spreadsheet Chaos

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