Introduction
When it comes to inventory metrics, value and turnover are the usual suspects. So why did I go out of my way to add a volume axis alongside them?
The reason is simple: in a warehouse, taking up space is itself a cost. Value and turnover tell you about "worth" and "movement," but not about "how much space something eats." This article digs into how we collect and estimate volume data, and how having it changes the analysis.
Why Volume Is Needed
The "Bulk" Hidden Behind Value
Look only at inventory value, and attention gravitates toward high-unit-price items. Yet on the warehouse floor, cheap-but-bulky items frequently occupy several shelves each. An SKU judged "not much inventory" on a value basis can be a serious space burden on a volume basis — and closing that gap is exactly what volume is for.
Space Is Finite, and Stalls When It Doesn't Move
Warehouse space is finite. While an SKU occupies space, that spot can't be used for anything else. Sell and ship it, and the space is freed immediately; leave it idle, and it stays blocked indefinitely. Storage cost, then, comes down to "volume × dwell time." Only by multiplying turnover (the "movement" metric) by volume (the "space" metric) does the true storage efficiency come into view.
Volume is also a weight on turnover
Even at the same turnover, a small item and a large one place completely different burdens on the warehouse. Volume gives the turnover metric a "weight of space."
How to Collect Dimension Data
Using Category and EAN as Clues
To produce volume you need width, depth, and height. But dimensions are almost never maintained for every SKU. That's where product categories and EANs (barcodes) become useful. From product information tied to an EAN, and from typical dimensions per category, we look up each SKU's dimensions. Scraping together information scattered across existing masters is the first bit of unglamorous work.
Filling Gaps with Estimation
Even so, some SKUs will always be left without dimensions. Stop here in pursuit of perfection, and the analysis itself never moves forward. I fill the gaps using average dimensions of the same category or analogy from similar products, prioritizing assigning volume to every SKU first. The trick is to flag estimated values as "estimates" so accuracy can be improved later.
Balancing Data Quality Against Pragmatism
Dimension data can absorb unlimited effort. Volume differs between packaged and single-unit states, and some products are irregularly shaped. But the goal of the analysis is to roughly separate the stars from the problem children, not millimeter accuracy. Run the whole thing with estimates included first, then improve precision by actually measuring the highest-impact SKUs — that pragmatic cut was the key to getting volume analysis into real operation.
What Changes Once You Know Volume
A New Yardstick: Storage Efficiency
Once volume is in place, you can calculate storage efficiency by multiplying it with demand (shipment history). If a fast-moving item turns over in a small volume, efficiency is high; if a dead item occupies a large volume, efficiency is low. This surfaces previously invisible problem children — like "inventory with small value but atrocious space efficiency."
The Next Step: Rank Classification
With the volume axis in place, you have the foundation to evaluate inventory from multiple angles. From here, we combine turnover and storage cost to classify SKUs into ranks A/B/C/D. Volume becomes the single most important input feeding that rank classification.
You can only describe "the warehouse somehow feels cramped" by gut feel.
You can name, in numbers, exactly which SKUs are squeezing space.
Summary
The reason to add volume to analysis is that in a warehouse, taking up space is itself a cost. Collecting and estimating dimension data takes patient work and pragmatic trade-offs, but once volume is in place, the space-hogging problem children that value and turnover alone couldn't show become visible.
On the foundation of volume, it's finally time to rank the inventory. Next, in "ABC Ranking by Turnover × Storage Cost," we'll look at that calculation design. To revisit the big picture, see the hub article "Warehouse Inventory Analytics."