Warehouse order picking is critical in supply chain management, directly affecting productivity, accuracy, and fulfillment speed. Choosing the right order-picking method depends on various factors, including order volume, warehouse layout, technology, product diversity, and labor efficiency. The most used methods are discrete order, cluster, batch, and sort picking. Each method has its strengths and weaknesses, making the choice context-dependent.
This article will compare these three picking methods and explore the factors that influence the design decision in a warehouse.
Discrete Order Picking (Single Order Picking)
In discrete order picking, an employee picks one order at a time. The picker walks through the warehouse following a specific route to collect all the items for a single customer order, then delivers them to the packing or shipping area before starting the next order. This method is straightforward and is often used in warehouses with low order volumes or small operations.
Advantages
Simplicity: Discrete order picking is easy to manage and requires minimal training. Workers are assigned individual orders, so executing is clear and straightforward.
Accuracy: Because each picker is focused on one order at a time, the chances of mixing up items are reduced. It ensures a high level of picking accuracy, making it ideal for environments where accuracy is critical.
Flexibility: This method allows for easy changes in the warehouse setup. No complex coordination is required between orders, which makes it flexible in smaller or less automated environments.
Disadvantages
Inefficiency in Large Warehouses: Discrete order picking can be inefficient in large warehouses or those with a high order volume, as pickers spend a lot of time walking between picking locations for individual orders.
Low Productivity: Since each picker handles one order at a time, productivity can be low, especially if items are scattered throughout the warehouse. This method doesn’t capitalize on the opportunity to consolidate picking activities.
Cluster Picking
In cluster picking, a single picker is assigned multiple orders and simultaneously picks items for several orders. The picker has a picking cart or tote system with various compartments, one for each order. Moving through the warehouse, they pick items for all the assigned orders simultaneously, placing them into the corresponding compartments.
Advantages
Increased Productivity: Cluster picking reduces walking time, as pickers collect items for multiple orders in one trip. This boosts productivity compared to discrete order picking.
Efficiency in Medium to High Order Volumes: This method is well-suited for medium-to-high-volume warehouses, where several orders can be picked concurrently without overwhelming the picker.
Improved Space Utilization: The warehouse layout is better utilized since pickers complete multiple orders in one trip.
Disadvantages
Complexity: Cluster picking is more complex than discrete picking. It requires a system to manage multiple orders simultaneously, which increases the likelihood of errors if not properly managed.
Less Focus on Individual Orders: When multiple orders are picked at once, the picker must stay organized to avoid confusion, which can lead to potential mistakes in order assignments.
Technological Dependency: Cluster picking is often efficient only when it uses advanced warehouse management systems (WMS) to coordinate and assign picking tasks effectively.
Batch and Sort Picking
In batch picking, a picker collects many items for several orders in a single trip through the warehouse. Still, unlike cluster picking, they do not immediately separate the items by order. Instead, items are picked in bulk and sorted later (usually in a dedicated sorting area). In this method, sorting typically happens after picking and dividing items into individual customer orders.
Advantages
Highly Efficient for Large Volumes: Batch and sort picking is particularly effective for large-scale operations with many small, similar orders. It minimizes the walking time by allowing pickers to gather many items in one trip.
Maximized Picking Speed: Since pickers aren’t sorting items during the picking process, they can move through the warehouse quickly and efficiently, collecting large quantities of items in a short period.
Suitable for Automation: This method is often used with automated sorting systems, making it highly efficient for warehouses investing in automation.
Disadvantages
Requires Sorting Infrastructure: Batch picking requires a dedicated sorting area or system, which adds to the infrastructure cost. Without this setup, it can become labor-intensive and prone to errors during the sorting stage.
Increased Complexity: While picking can be fast, the later sorting stage adds complexity. Proper systems and processes are required to ensure that items are accurately sorted into the correct orders.
Potential for Errors During Sorting: The handoff from picking to sorting creates a potential point of failure, especially if the sorting process isn’t well-managed or if human error is introduced during the manual sorting stage.
Comparing Order Picking Methods
| Factor | Discrete Order Picking | Cluster Picking | Batch and Sort Picking |
| Picking Speed | Slow, as pickers handle one order at a time | Moderate, as multiple orders are picked concurrently | Fast, since items are picked in bulk |
| Accuracy | High, as the focus is on one order | Moderate, more room for errors | Lower, if the sorting process is not well-managed |
| Warehouse Layout | Works well in smaller layouts | Best for medium layouts | Efficient in large, high-volume operations |
| Labor Efficiency | Low, due to inefficiencies in travel time | Higher, due to reduced walking time | Very high if batch picking is combined with automated sorting |
| Complexity | Simple, with minimal training required | Medium complexity requires some organizational skills | High, as it requires sorting infrastructure |
| Technological Needs | Minimal | Requires WMS for efficient coordination | High, often paired with automated sorting systems |
| Best Fit | Small-scale or low-volume operations | Medium-volume or moderate complexity | Large, high-volume operations with many similar orders |
Factors Influencing the Choice of Order Picking Method
Several factors must be considered when selecting the appropriate picking method for a warehouse. The best method will depend on the specific needs and characteristics of the warehouse, including the following:
Order Volume
Low-order picking is best for handling low-order volumes, as there’s no need for complex coordination or multi-order handling.
Medium order volumes benefit from cluster picking, where the increase in efficiency and productivity helps balance the need for multiple orders while effectively managing space.
High order volumes, especially in e-commerce or distribution centers, are often best handled with batch and sort picking, allowing efficient picking and post-pick sorting.
Order Size and Variety
If orders tend to be large or highly variable in items picked, discrete picking may be best, as it reduces the complexity of managing diverse products.
Cluster picking is ideal for small-to-medium orders with similar items. It reduces the chance of confusion while optimizing picking time.
Batch and sort picking is suited to environments with a high volume of small, similar orders where bulk picking is efficient.
Warehouse Layout and Size
In small warehouses, where the distance between pick locations is short, discrete order picking may suffice due to the lack of excessive travel time.
Cluster picking works well in medium-sized warehouses, reducing travel time by consolidating multiple orders into one trip.
Batch and sort picking is often the method of choice for large, complex warehouse layouts, as pickers can focus on gathering items without worrying about sorting until later.
Technology and Automation
Discrete order picking requires minimal technological investment, making it appropriate for smaller or less automated facilities.
Cluster picking often requires a warehouse management system (WMS) to efficiently assign orders and track picker progress, making it more suitable for semi-automated environments.
Batch and sort picking typically demand the highest level of automation, especially in the sorting stage. Conveyors, automated sortation systems, and advanced software are common in batch-picking environments.
Labor and Skill Level
Because of its simplicity, discrete picking may be preferable in warehouses with limited labor or low worker experience.
Cluster picking requires workers who can handle multiple orders simultaneously and stay organized throughout the picking process.
Batch and sort picking requires fewer pickers but more workers or automation on the sorting side, making it best for operations that can invest in skilled labor and technology for the sorting process.
Accuracy Requirements
Suppose order accuracy is paramount (e.g., in pharmaceutical or luxury goods warehousing). In that case, discrete order picking may be the best choice due to its inherent focus on one order at a time.
For less error-sensitive industries (e.g., bulk goods or commodity items), batch and sort picking offer greater efficiency at the cost of potential sorting mistakes.
Each order-picking method—discrete order picking, cluster picking, and batch and sort picking—has distinct advantages and disadvantages. Discrete picking offers high accuracy and simplicity but can be inefficient for larger operations. Cluster picking balances efficiency and accuracy, making it ideal for medium-sized warehouses. Batch and sort picking maximizes efficiency and is suited to high-volume environments but requires more complex infrastructure and technology.
Choosing the right method depends on order volume, warehouse size, product variety, technology availability, and labor costs. Understanding these elements will help warehouse managers design an order-picking system that maximizes efficiency while meeting their operations’ specific needs.
