Introduction
When people think of inventory analysis, the first metrics that come to mind are usually inventory value and turnover. Those matter, no doubt. But once you actually run the warehouse of an import wholesaler, you notice a cost those numbers never reveal.
That cost is taking up space itself. When a small, fast-selling item and a large, dead item share the same shelf, the latter is squeezing the warehouse far more than its inventory value suggests. This article introduces the overall picture of an analytics tool that brings a volume axis into per-SKU inventory and multiplies it by turnover to make storage cost visible.
Why Inventory Value Alone Can't Optimize a Warehouse
What Value and Turnover Miss
Inventory value tells you how much capital is sitting idle; turnover tells you how much is moving. Yet neither says anything about how much space that stock occupies in the warehouse. Cheap but bulky items tend to be dismissed on a value basis, while they steadily eat up floor area. Look only at value and turnover, and these "space-hungry bargains" slip right through the net of your analysis.
In a Warehouse, Space Is the Cost
Warehouse rent and fixtures are fundamentally charged against area and volume. In other words, the cost of holding a given SKU is roughly proportional to the space it occupies. Sell it, and that space rotates immediately into the next product; leave it unsold, and it keeps occupying the same space indefinitely. This "space × dwell time" is the true storage cost that value alone can't show.
Why Analyze Per SKU
Looked at by product category or warehouse-wide averages, the star performers and the problem children cancel each other out and the reality disappears. So this tool analyzes inventory per SKU. This SKU sells well and takes little space; that one doesn't sell and hogs space — only at this granularity do you get insight that translates into concrete shelf-layout and disposal decisions. It runs as one feature of an internal business hub, on top of Neon Postgres.
Bringing in the Volume Axis
Computing Volume from Dimension Data
To use volume in analysis, you first need product dimension data. We compute volume as width × depth × height from dimension information tied to product categories and EAN (barcodes). That said, it's rare for every SKU to have accurate dimensions, so in practice estimation from category averages and gap-filling are indispensable. Collecting and estimating this dimension data is the first hurdle of volume analysis.
Measuring Storage Efficiency with Demand × Volume
Once you have volume, you can use metrics that multiply it by demand (shipment history). If a fast-moving item turns over in a small volume, that's ideal inventory for the warehouse. Conversely, if a dead item occupies a large volume, its storage efficiency is terrible. From the combination of demand and volume, we calculate a theoretical minimum storage cost and an allocation multiplier, quantifying each SKU's "space efficiency."
Bulky bargains go unnoticed, and you can't pin down why the warehouse "just feels cramped."
You can numerically separate "stars that sell and take little space" from "stock that doesn't sell and hogs space."
The Overall Picture of the Tool
The Data Flow
Analysis starts by tying several data sources together per SKU. We join inventory data, shipment history, and dimension data from categories and EANs, then compute volume and demand. From there we derive storage-efficiency cost and finally classify each SKU into ranks A/B/C/D. The results are aggregated by fixture, block, and location, giving a bird's-eye view of "what's sleeping where."
Tie inventory, shipment history, and dimension data together per SKU
Calculate each SKU's volume from category/EAN dimensions (estimate the gaps)
Derive storage-efficiency cost and allocation multiplier from demand × volume
Classify into A/B/C/D from turnover and storage cost
Aggregate by location to inform shelf-layout and disposal decisions
What It Makes Visible
What this tool ultimately wants to show is the separation of "stars" from "problem children." SKUs that sell well and take little space are color-coded as rank A, and unsold, bulky SKUs as rank D, with an indication of which fixtures and locations they cluster in. Just by scanning the list, the operator can intuitively see which inventory needs the next move.
Analysis exists for action
Producing numbers is not the goal. Only when it leads to concrete actions — changing the shelf layout, deciding on disposal, adjusting purchase quantities — does analysis create value.
Summary
By adding a volume axis to warehouse inventory analysis, you can make visible the "cost of space" that inventory value and turnover alone never showed. The key was the mindset that in a warehouse, taking up space is itself a cost.
This article served as a hub introducing the overall picture. Each theme is explored in more depth across the following three articles:
- Why Add "Volume" as an Inventory Analysis Axis: the difficulty of collecting and estimating dimension data, and what changes once you know volume.
- ABC Ranking by Turnover × Storage Cost: a close look at the calculation design that derives A/B/C/D ranks from demand and volume.
- Turning Analysis into Shelf-Layout and Disposal Decisions: report design that translates ranks and location analysis into concrete actions — relocation, disposal, and purchasing adjustments.