Weeks Cover Explained: The Most Important Metric in Stock Management

Most inventory metrics are either backward-looking (what sold last month) or abstract (inventory turnover ratio). Weeks cover is forward-looking and immediately actionable. It answers the simplest question in stock management: "How long until I run out?"

The basic formula is straightforward:

Weeks Cover = Current Stock Quantity / Average Weekly Sales

But the devil is in the "average weekly sales" part. Which weeks do you average? If you sell Christmas baubles, your average weekly sales in July will give you a very different answer than your average in November. The period you use for the average directly affects the usefulness of the metric.

For most products, a rolling 8-week average works well. It's long enough to smooth out individual week fluctuations but short enough to reflect recent trends. For seasonal products, you need to use the equivalent period from the previous year — or a blended approach that weights recent weeks more heavily.

Inventory turnover is the metric most commonly taught in business textbooks. It tells you how many times you sold through your average inventory in a period. The problem? It's a lagging indicator calculated over months or quarters. By the time your inventory turnover ratio tells you something useful, the damage is already done.

Weeks cover is an instant health check. You can look at it today and know immediately which SKUs are in danger and which are over-ordered. More importantly, you can act on it today.

Consider three SKUs in your catalogue: - SKU A: 12 weeks cover — comfortable, no action needed - SKU B: 3 weeks cover with a 6-week lead time — you needed to reorder 3 weeks ago - SKU C: 52 weeks cover — you have a year's worth of stock, tying up cash

That snapshot tells you more in 5 seconds than a quarterly inventory turnover report. SKU B is an emergency. SKU C is a cash trap. SKU A is fine. No spreadsheet pivoting, no ratio calculations, no comparing against industry benchmarks. Just raw weeks of supply.

Bailey & Coco sells dog accessories with an enormous variant matrix: 152 patterns across collars, leads, harnesses, bow ties, and bandanas — each in 4-6 sizes. That's 2,845 active SKUs, each with its own sales velocity and stock level.

Before tracking weeks cover, their purchasing decisions were based on gut feel and whichever product the warehouse team noticed was running low. The founder would walk the shelves, spot a thin-looking shelf, and place an order. This approach has a name in inventory management: the "eyeball method." It works when you have 50 SKUs. It's catastrophic at 2,845.

Here's what went wrong. The "Teal Leopard" collar in size Medium was their #3 best-seller, averaging 38 units per week. But because it shared shelf space with the visually similar "Teal Safari" pattern, the warehouse team never noticed it running low. By the time they flagged it, they had 2 weeks of cover left — and their supplier lead time from China was 27 weeks (70-day production plus 120-day sea freight). They ran out of stock for 6 months.

Meanwhile, the "Blush Polka Dot" bandana in size Large — a seasonal summer line — had 74 weeks of cover. They'd ordered enough to last well into the following summer. That stock was tying up approximately £3,200 in cash that could have been used to purchase more Teal Leopard collars.

With a proper weeks cover dashboard, both of these problems are visible instantly. You sort by weeks cover ascending, and the SKUs about to stock out jump to the top. You sort descending, and the over-ordered SKUs are immediately obvious. No shelf-walking required.

The basic calculation is simple, but getting accurate results across a large catalogue requires discipline on a few points:

There is no universal "correct" weeks cover. It depends entirely on your supplier lead time, demand variability, and cash position. But here are useful benchmarks:

Domestic suppliers (1-2 week lead time): Target 4-8 weeks cover. Below 3 weeks is risky. Above 12 weeks is over-invested.

European suppliers (3-6 week lead time): Target 8-14 weeks cover. Below 6 weeks triggers a reorder. Above 20 weeks is over-invested.

China/Asia suppliers with sea freight (16-27 week lead time): Target 20-35 weeks cover. Below 18 weeks is critical. Above 52 weeks is a cash problem.

For Bailey & Coco, with their 27-week total lead time, the sweet spot is around 30 weeks cover. That gives them a 3-week buffer above lead time for demand spikes and shipping delays. Any SKU dropping below 27 weeks cover is flagged as urgent — they need to place a PO immediately or risk a stockout.

You can calculate weeks cover in a spreadsheet. Export your stock levels from Shopify, export your sales data, calculate the rolling average, and divide. The problem is doing this regularly. A spreadsheet calculation is a snapshot. The moment you export the data, it's already out of date.

For stores with under 100 SKUs, a weekly spreadsheet is manageable. Above that, you need a tool that calculates weeks cover continuously and alerts you when SKUs cross critical thresholds.

Canopy is being built with weeks cover as the foundational metric. Every SKU in your catalogue gets a live weeks cover figure that updates as sales come in and stock levels change. The dashboard sorts by urgency — SKUs below their reorder threshold are highlighted. Each SKU's reorder threshold is automatically set based on its specific supplier lead time, not a one-size-fits-all number. It's the approach Bailey & Coco needed when they were eyeballing shelves and hoping for the best.