AI for Warehouse Picking Efficiency in Australia (2026)

Picking is the most labour-intensive activity in most warehouses — and the one with the greatest impact on both cost and customer service. In a typical pick-and-pack operation, picking accounts for 50–70 per cent of total warehouse labour costs. Improving picking efficiency directly reduces operating costs and improves the speed and accuracy of customer order fulfilment.

AI tools are being applied to picking in ways that deliver measurable results: optimised pick paths that reduce travel time, voice-directed picking that improves accuracy, goods-to-person robotics that eliminate travel entirely, and batch picking algorithms that increase throughput without sacrificing accuracy.

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The Picking Efficiency Challenge

Picking efficiency is constrained by several factors:

Travel time. In a traditional pick-and-pack warehouse, pickers spend 50–60 per cent of their time travelling between pick locations. Reducing travel time is the single biggest opportunity to improve picking efficiency.

Pick accuracy. Picking errors — wrong item, wrong quantity, wrong location — create downstream costs: rework, returns, customer complaints, and replacement shipments. High-accuracy picking requires clear direction, reliable product identification, and systematic verification.

Order complexity. As e-commerce has grown, average order sizes have fallen and order complexity has increased. A warehouse that once processed 100 pallet orders per day may now process 1,000 individual item orders. This shift requires different picking strategies and different technology.

Labour availability. Finding and retaining reliable warehouse pickers is a persistent challenge in the Australian labour market. Picking efficiency tools that allow the same number of pickers to process more orders reduce dependence on labour availability.

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AI Applications for Picking Efficiency

1. Pick Path Optimisation

Pick path optimisation uses AI to calculate the most efficient sequence for picking each order — minimising the distance a picker travels through the warehouse. For a warehouse with hundreds of pick locations, the difference between an optimised and unoptimised pick path can be significant — 20–30 per cent reduction in travel time is commonly reported.

How it works: The WMS analyses the locations of all items in an order and generates a pick sequence that minimises travel distance. The picker follows the sequence displayed on their RF scanner or voice device. The AI accounts for aisle layout, one-way traffic flows, and any blocked locations.

Batch picking optimisation. For warehouses processing many small orders, batch picking — picking items for multiple orders simultaneously — can dramatically improve efficiency. AI batch picking algorithms group orders with overlapping pick locations, generating a single pick path that covers multiple orders. The picker collects items for all orders in the batch, then sorts them at a packing station.

Zone picking optimisation. In zone picking, the warehouse is divided into zones with dedicated pickers. AI optimises the zone boundaries based on order patterns, ensuring that workload is balanced across zones and that the most efficient zone configuration is used.

2. Voice-Directed Picking

Voice-directed picking systems provide picking instructions through a headset — the picker hears the location and item to pick, confirms the pick verbally, and moves to the next location. Hands and eyes are free throughout the process, improving both speed and safety.

AI enhances voice picking by:

• Generating optimised pick sequences in real time, accounting for current warehouse conditions
• Adapting to exceptions — if a location is empty or a product is damaged, the AI reroutes the picker to an alternative location
• Learning from picker behaviour — identifying patterns that indicate process issues or training needs

Key platforms: Honeywell Vocollect and Zebra Technologies are the leading voice picking platforms used in Australian warehouses.

3. Goods-to-Person Robotics

Goods-to-person (GTP) systems eliminate picker travel entirely — instead of pickers travelling to products, robots bring products to stationary pickers. This is the most transformative picking technology available, capable of increasing picking throughput by 3–5 times compared to traditional pick-and-pack.

How it works: Products are stored on mobile shelving units (pods) in a dense storage area. When an order is received, robots retrieve the relevant pods and bring them to a picking station. The picker selects the required items from the pod, confirms the pick, and the robot returns the pod to storage.

Key platforms:

• Geek+ — goods-to-person robotics used by major Australian retailers and 3PLs
• 6 River Systems (Chuck) — collaborative mobile robots that guide pickers through optimised paths
• Locus Robotics — AMRs that work alongside human pickers in conventional warehouse layouts

Investment considerations. GTP systems require significant capital investment and are best suited to high-volume operations with large numbers of SKUs. For smaller operations, collaborative AMRs (like Locus or 6 River) that work in conventional warehouse layouts may be more appropriate.

4. Slotting Optimisation

Slotting — the placement of products in the warehouse — has a direct impact on picking efficiency. Products that are picked frequently should be located close to packing stations, at ergonomic heights, and in easily accessible locations. Products that are often picked together should be stored near each other.

AI slotting optimisation analyses order history to identify:

• High-velocity items that should be in prime pick locations
• Complementary items that are frequently ordered together and should be stored near each other
• Seasonal items that should be moved to prime locations during peak periods
• Slow-moving items that are occupying prime locations unnecessarily

Re-slotting a warehouse based on AI recommendations typically reduces pick travel time by 10–15 per cent. The analysis should be repeated regularly — order patterns change, and slotting that was optimal six months ago may no longer be optimal today.

5. Real-Time Labour Management

AI labour management systems track individual picker productivity in real time — units picked per hour, travel time, error rate — and compare performance against targets and benchmarks. This data enables:

• Real-time workload balancing — directing pickers to the highest-priority work
• Performance coaching — identifying pickers who need support or training
• Staffing optimisation — predicting workload and scheduling the right number of pickers for each shift

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Choosing the Right Picking Strategy

The right picking strategy depends on your operation's characteristics:

Operation type	Recommended strategy
Low volume, large orders	Single-order pick with path optimisation
High volume, small orders	Batch picking with path optimisation
Very high volume, many SKUs	Goods-to-person robotics
Mixed operations	Zone picking with batch optimisation per zone
High accuracy requirements	Voice-directed picking

Most operations benefit from a combination of strategies — for example, voice-directed picking with batch optimisation for standard orders, and goods-to-person for high-velocity SKUs.

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Measuring Picking Efficiency

• Units picked per labour hour — primary productivity measure
• Pick accuracy rate — percentage of picks completed without error (target: >99.5%)
• Order cycle time — time from order receipt to dispatch
• Travel time as percentage of total pick time — measure of path optimisation effectiveness
• Batch size — average number of orders per batch pick (higher is more efficient)