Picking Methodologies for High Performance
Connect, Autumn 2007

How to select the optimal solution for your operation

Almost universally, companies face pressure to reduce costs and improve productivity within distribution. Picking—which can account for more than 50 percent of recurring warehouse operation costs—is one such area for improvement. Indeed, the August 2007 Aberdeen Group report High Octane Warehousing found that best-in-class companies are twice as likely as their peers to implement advanced picking methodologies and associated technologies as a means to increase efficiency and reduce labor costs. Here, we take a closer look at the picking strategies used by best-in-class distribution facilities.

Picking Methodologies

In case and piece picking operations where the worker seeks out the SKUs, travel time is the biggest efficiency hindrance, notes Aberdeen Group. For large or automated warehouses, four different types of advanced picking methodologies can be employed—separately or together—to cut travel time. To successfully pick using any of these methodologies, however, the warehouse’s contents must be appropriately slotted. (See the Connect article Slotting: A Winning Strategy for slotting best practices.)

In Discrete Order Picking, one person travels through the facility to pick a single order. This strategy is frequently employed by companies with lower case volumes where automation can not yet be justified, says Dave Gealy, senior consultant with FORTE. “It is also ideal when extremely high volumes exceed reasonable automation capabilities—such as the high-volume, less-than-full-pallet case picks of a single SKU, sometimes found in grocery applications,” he notes.

Although this can be the most labor- and time-intensive picking method, there are advantages. With only one person responsible for the order, accountability for accuracy is clear. Discrete Order Picking also accommodates specific customer requirements for pallet structuring, as the process itself often allows loads to be built to certain customer specifications.

When Cluster Picking, one person travels through the warehouse picking for multiple orders during a single trip. Limiting the number of times the worker goes through the pick line increases efficiency. The key to optimizing Cluster Picking is grouping orders that have commonality, advises Gealy. “Group orders that have the same SKU, SKUs within ten feet of each other or SKUs in the same aisle.”

Cluster picking also works well in pick-to-cart operations, particularly in direct-to-consumer operations where orders are typically not more than two lines, product sizes are small and 12-30 orders can be grouped together. This method also requires an intelligent application that structures the order groupings to minimize the pick time, says Gealy.

Picking for multiple orders at one time could cause a decrease in accuracy if an item picked for one order is placed with another. Therefore, Cluster Picking demands a certain level of technology—like scanning—to help the picker keep multiple orders straight. (See the Connect article Technologies to Support Advanced Picking)

Warehouses structured for Zone Picking enable multiple workers to simultaneously pick portions of a single order. This strategy depends heavily on technology and automation with conveyors, scanners and bar coded collection totes transporting the order throughout the facility. The Warehouse Management System (WMS) and the Warehouse Control System (WCS) work in tandem, sorting and grouping order waves and directing containers (totes or cartons) to the appropriate zone. When the container arrives, a worker stationed there fills it with the appropriate SKUs and returns it to the conveyor. All containers converge in an order consolidation area for packing and/or shipping.

Successful Zone Picking requires appropriately slotting across each zone to balance work. By dispersing a wide variety of different velocity SKUs throughout the warehouse, the chance of overburdening a particular zone diminishes.

“Zone Picking leverages technology to eliminate walking distance and travel time,” says Gealy. “On the flip side, there’s probably always going to be some minimal downtime in at least one zone where the picker is waiting for the work to reach that area. This can be minimized by effective slotting and grouping, and releasing work to the floor in an optimal manner to promote workload balancing.”

Batch Picking is often utilized where many orders require the same SKUs. This involves retrieving the required quantity of all SKUs for an entire set of multiple orders, and then distributing those items to the orders. For example, if 50 orders each require one unit of SKU A, then a picker retrieves those 50 items in one visit to the SKU’s pick location and delivers them to a packing area—either manually or via conveyor—where they are packed to the order level.

“Batch Picking is effective for multiple or single line orders that require the same SKU,” Gealy observes. “It gives the flexibility to send those orders to the floor in one pick wave, and the efficiency of only visiting a pick location once per wave.”

On the other hand, Batch Picking typically requires a second touch to disperse the batch of SKUs to their unique orders. However, says Gealy, the reduced travel time and savings gained from batch picking can outweigh additional time spent in downstream sortation for companies with the right business conditions and order profiles.

Conclusion

Deciding which picking methodology is right for your business should ultimately be based on the unique needs of your facility.

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