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To provide an A-to-Z e-commerce logistics solution that would complete Amazon fulfillment network in the European Union.
If you walk into a non-optimized warehouse, the first thing you will notice isn’t the inventory—it’s the walking. In traditional order fulfillment, pickers can spend up to 60% of their shift simply walking between locations. For an e-commerce business operating on thin margins, paying staff to walk rather than pick is a silent profit killer.
This is where specific picking strategies come into play. While single-order picking works for startups, scaling businesses eventually hit a wall. Cluster picking has emerged as one of the most efficient methodologies for reducing travel time and boosting Units Per Hour (UPH) without investing in heavy automation.
Below is a deep dive into the mechanics of cluster picking, the mathematical logic behind its efficiency, and how to implement it within your supply chain.
What is cluster picking?
At its core, cluster picking is a methodology where a picker harvests items for multiple orders simultaneously during a single trip through the warehouse.
Unlike "batch picking" (where a picker grabs multiple quantities of a single SKU for different orders to sort later), Cluster Picking involves picking distinct SKUs into separate containers located on a single cart.
Visual setup
Imagine a picker pushing a cart equipped with 12 separate bins or totes. Each bin corresponds to a unique customer order. The Warehouse Management System (WMS) directs the picker to a shelf location. The picker grabs the item and puts it directly into "Bin A." Then, they move to the next location and place an item into "Bin B."
By the time the picker returns to the packing station, they have effectively picked 12 orders in the time it would usually take to pick two or three using a discrete picking method.
Logic of logistics: Cluster vs. batch vs. discrete
To understand why cluster picking is often the superior choice for e-commerce, we must distinguish it from other common strategies. Misunderstanding these differences is a common source of inefficiency in warehouse management.
1. Discrete picking (Order picking)
- Process: One picker, one order, one trip.
- Flaw: Extremely high travel time. If an order requires an item from Aisle 1 and Aisle 20, the picker traverses the whole warehouse for a single box.
- Best for: Very heavy items or bulk orders where a cart cannot hold multiple shipments.
2. Batch picking
- Process: A picker gathers all required units of a specific SKU for multiple orders at once. These are then brought to a sorting area to be divided into individual orders.
- Flaw: It requires a secondary "sortation" step, which adds labor hours.
- Best for: Operations with few SKUs but high volume per SKU.
3. Cluster picking
- Process: Picking multiple orders into distinct slots on a cart. Sortation happens during the pick.
- Advantage: Eliminates the secondary sortation phase required in batch picking and drastically reduces the travel distance per unit compared to discrete picking.
- Best for: E-commerce businesses with high order volume and multi-line orders (orders containing distinct mixed items).
Analyzing the efficiency gains
Why does clustering work so well for online retail? The answer lies in pick density.
In a standard e-commerce environment, orders are often small (1 to 5 items) and sporadic. If you use discrete picking, the "pick path" is long and sparse. By clustering orders, you artificially increase the density of picks per aisle.
- Key stat: Implementing an optimized cluster picking strategy can typically increase picking productivity by 30% to 50% compared to discrete order picking.
Reduction of "deadhead" travel
"Deadhead" refers to travel time where no value is created (walking with an empty cart or walking between distant picks). Cluster picking ensures that every trip down an aisle yields multiple picks. If 4 out of the 12 orders on the cart require a specific shampoo from Aisle 4, the picker clears all those lines in one stop.
Is your warehouse ready for cluster picking?
Before restructuring your fulfillment center, you must evaluate if your order profile fits this methodology. Cluster picking is not a universal fix; it requires specific conditions to thrive.
Ideal profile
- Small to medium cube size: The items must physically fit into the totes on a picking cart. If you sell kayaks or large furniture, clustering is physically impossible.
- Multi-line orders: If your customers typically buy just one item, "Batch Picking" might actually be faster. Cluster picking shines when customers buy a mix of items (e.g., a shirt, a belt, and socks).
- High SKU count: If you have thousands of SKUs, the likelihood of different orders needing items from the same zone increases, making the optimized path of a cluster pick highly valuable.
Technical implementation: Hardware and software
Moving to cluster picking is not just a procedural change; it requires an infrastructure update.
1. Multi-tote cart
You need specialized equipment. The carts must be designed to hold multiple standard-sized totes (usually 4, 6, 9, or 12).
- Ergonomics: The cart must remain maneuverable even when fully loaded.
- Organization: Clearly labeled slots (Position 1, Position 2, etc.) are mandatory to prevent placing items in the wrong order bin.
2. WMS intelligence
You cannot do efficient cluster picking with paper lists. It requires a Warehouse Management System (WMS) capable of order batching logic. The WMS must be able to:
- Analyze the pool of open orders.
- Group orders that share common pick locations (this is called "proximity batching").
- Generate a "Pick Path" that guides the worker through the warehouse in a serpentine route (S-shape) to minimize backtracking.
3. Verification technology
The biggest risk in cluster picking is putting the right item in the wrong bin. To mitigate this, you need validation tech:
- RF scanners: The picker scans the item, and the scanner screen displays "Place in Bin 3." The picker then scans a barcode on Bin 3 to confirm placement.
- Pick-to-light (PTL): Lights mounted on the cart illuminate the correct bin, reducing cognitive load and speeding up the process.
Strategic slotting: Hidden success factor
Even the best picking strategy fails if your warehouse layout (slotting) is poor. To maximize cluster picking, you must adopt a data-driven slotting strategy.
- High-velocity SKUs (Fast movers): These should be placed in the "Golden Zone"—the area between waist and shoulder height—and ideally concentrated near the start of the pick path. This ensures that even within a cluster pick, the heaviest traffic happens in the most accessible areas.
- Slow movers: These can be relegated to higher shelves or the back of aisles.
By combining cluster picking with dynamic slotting (regularly moving inventory based on seasonal demand), you ensure that the cluster carts are filled as quickly as possible.
Common friction points and how to solve them
Transitioning to cluster picking can introduce new friction points. Being aware of them allows for proactive management.
"Bottleneck" effect
If too many pickers are sent to the same aisle because the WMS clustered similar orders for everyone, you create congestion.
- Solution: Use "Zone Picking" in conjunction with clustering. Assign pickers to specific zones and have them hand off carts or consolidate orders at the end.
Physical fatigue
Pushing a cart with 12 heavy orders is demanding.
- Solution: Limit the total weight per cart or invest in motorized picking carts. Alternatively, use AMRs (Autonomous Mobile Robots) that carry the bins while the human picker simply stays in the aisle and loads the robots as they approach.
Exception management
What happens when an item is missing (short pick) for one of the clustered orders?
- Solution: The WMS must allow the picker to mark the item as "short" and continue the rest of the cluster without freezing the entire batch. The incomplete order should be flagged for a special "exception handling" process at the packing station.
Integrating automation: Future of clustering
As we look toward 2025 and beyond, cluster picking is evolving. We are moving away from purely manual carts toward technology-assisted clustering.
Voice picking
Voice-directed warehousing frees the picker's hands and eyes. The system tells the picker: "Go to location A-02, pick 3 units, place in Bin 4, Bin 5, and Bin 6." This reduces the time spent looking at a scanner screen.
Collaborative robotics
"Chuck" style robots (collaborative mobile robots) are essentially smart cluster carts. They meet the picker at the location, display the item needed on an iPad-sized screen, and light up the correct bin. This hybrid model combines human dexterity with robotic efficiency.
Scaling operations beyond the warehouse walls
Optimizing the "pick" is the most effective lever you can pull to improve internal warehouse speed. However, implementation requires capital investment in WMS licenses, hardware, and training.
For many growing e-commerce brands, there comes a tipping point where managing complex picking strategies in-house yields diminishing returns. When your SKU count explodes or seasonal spikes make workforce planning impossible, the logic of cluster picking often points toward a different solution: decentralization.
Partnering with a specialized 3PL partner allows you to leverage enterprise-grade cluster picking systems without the direct overhead.
Whether you choose to optimize your own facility or integrate with a 3PL that has already mastered these flows, the goal remains the same: shortening the distance between the product and the customer, one optimized pick at a time.
