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March 18, 2026 • Industrial AI Team • 7 min read

Every warehouse manager knows the pain of the annual stocktake. Operations slow down or stop entirely. Staff spend days counting pallets, checking bays, and reconciling numbers against a WMS that may or may not reflect reality. Discrepancies are found. Arguments follow. And within weeks, the count is drifting again.

Computer vision offers a different approach: continuous, automated inventory visibility that does not require stopping operations. Cameras positioned throughout the warehouse monitor stock levels, count pallets, track movements, and flag discrepancies as they happen — not months later during a stocktake.

The Problem with Periodic Stocktakes

Traditional stocktaking is a snapshot in time. It tells you what was in the warehouse at 2am on a Sunday when the count was taken. By Monday morning, goods have moved, been shipped, received, and relocated. The count is already out of date.

The labour cost of manual stocktakes is substantial, but the bigger cost is often the operational disruption. Warehouses that cannot afford to shut down for counting resort to cycle counting — counting sections of the warehouse on a rolling basis — which is better but still labour-intensive and prone to the same human errors.

Barcode and RFID scanning have improved accuracy at the item level, but they still require a human to physically scan each item or pallet. They tell you what passed through a scan point, but they cannot tell you what is currently sitting in bay 47, row C, level 3.

How Computer Vision Changes the Equation

Camera-based inventory monitoring works by positioning cameras to observe storage areas — racking bays, bulk storage zones, loading docks, and staging areas. AI models analyse the video feeds to detect and count pallets, identify empty versus occupied bays, and track movement patterns.

The key difference from traditional approaches is that the monitoring is continuous. The system knows what is in each bay right now, not what was there last time someone counted. When a forklift removes a pallet from a bay, the system registers the change immediately. When stock is placed in the wrong location, the discrepancy is flagged.

For warehouses with existing camera systems, adding AI-powered inventory monitoring can often leverage cameras already in place, reducing the upfront investment significantly.

Practical Applications

Pallet counting and bay occupancy: Cameras overlooking racking can count pallets per bay and report occupancy levels across the warehouse. This data feeds into space utilisation analysis and helps planners optimise storage allocation.

Dock door monitoring: Cameras at loading docks can count pallets being loaded and unloaded, verifying shipment quantities against manifests automatically. Discrepancies between the manifest and the visual count are flagged in real time, before the truck leaves.

Bulk storage estimation: For warehouses storing bulk materials in open bays or bins, computer vision can estimate volume and fill levels without manual measurement. This is particularly useful for building materials, agricultural products, and recycling operations.

Misplacement detection: When goods are placed in the wrong bay — a common source of picking errors and lost inventory — the system can flag the discrepancy by comparing observed stock against expected locations from the WMS.

What About Barcode and RFID?

Computer vision is not a replacement for barcode or RFID systems — it is a complement. Barcode scanning provides item-level identification that cameras cannot match at distance. RFID provides tracking of tagged items through scan points. Computer vision adds the spatial awareness layer: it knows where things are physically located and whether bays are full or empty.

The most effective warehouse inventory systems combine these technologies. Barcodes or RFID handle item identification. Computer vision handles location verification and continuous monitoring. Together, they provide a level of inventory accuracy that neither achieves alone.

Limitations to Understand

Camera-based inventory monitoring works best for counting uniform items like pallets, cartons, and bins. It is less suited to counting individual small items inside packaging. It requires line-of-sight — cameras cannot see behind pallets or into enclosed containers. And it requires adequate lighting, though modern AI models perform well in the typical lighting conditions of most Australian warehouses.

The technology is also most valuable in warehouses with relatively stable storage patterns. A warehouse where stock is constantly being reorganised presents a more complex challenge than one with defined storage locations.

The Business Case

The cost of inventory inaccuracy in warehousing is well-documented: mispicks, lost stock, emergency reorders, and the labour cost of manual counting all add up. For warehouses spending significant labour hours on cycle counting and stocktakes, AI-powered monitoring can reduce that burden while improving accuracy. The system pays for itself through reduced labour on counting, fewer mispicks, and better space utilisation. For warehouse operations looking to improve without overhauling their entire system, it is a practical next step.