Data-driven strategies are at the core of successful, growing companies. As operational volumes increase, having access to relevant data and tracking relevant key performance indicators (KPIs) becomes crucial for understanding the impacts on warehouse management operations.
Picking performance is one of the main metrics used in warehouses to measure the efficiency of outbound operations. This KPI helps to identify areas for improvement and allows warehouse managers to estimate the time required for specific picking tasks.
How is Picking Performance Measured?
Pick performance gives insights into how the picking process is working from receiving the orders to shipping them. It can be measured using different types of indicators.
-
Picking Rate
The picking rate measures how many items are picked per hour. It can be tracked at the order level, indicating the time taken to pick a complete order by using the following calculation:
According to Warehousing and Fulfillment, the average of the industry is around 71 items per hour which is a good picking rate. It is a key indicator for Picking Performance, providing insight into the productivity of the workforce. Keep in mind that the average pick rate in a warehouse varies based on multiple factors, such as warehouse layout, order profile, warehouse location of the products, type of products: bulky versus nuts&bolts, the picking process and strategy as well as if there happen to be congestion in the picking process.
-
Picking Accuracy
Picking accuracy shows the number of orders that are picked accurately, without errors or discrepancies. Higher order picking accuracy indicates that pickers are avoiding errors such as selecting the wrong item, quantity, or location, which helps reduce returns, complaints, and rework. The error rate can be measured by using the following calculation:
-
Order Picking Cycle Time
Order picking cycle time is the duration between receiving an order and preparing it for shipping. It demonstrates the efficiency and flexibility of your order-picking process in meeting customer demands and timelines. You can assess order picking cycle time by monitoring the time intervals of order receipt, assignment, picking, packing, and shipment within a specific timeframe.
-
Picking Utilization
Picking Utilization measures how much of the available picking capacity is being used and shows how effectively you are managing your picking resources. Higher utilization indicates that your pickers are working efficiently and making the most of your warehouse space, equipment, and labor.
-
Pick Quality
Pick quality measures how well your picking process meets customer needs and expectations. Higher quality means that your pickers are ensuring that items are handled correctly, packed well, and meet customer specifications. Tracking customer feedback and reviews can help measure pick quality over time.
How to Improve Picking Performance
Employee Training
First and foremost, employees are the core of picking operations, therefore, picking performance is closely tied to employees' knowledge and understanding of their tasks.
By equipping employees with the necessary skills and knowledge, warehouses can enhance accuracy and efficiency in the picking process. Training programs should focus on proper picking techniques, technology such as barcode scanners and pick-to-light systems, and best practices for minimizing errors. Addressing any gaps in understanding through training, education, or counseling can further improve performance.
The Right Picking Strategy
Choosing the right picking strategy can speed up order fulfillment by allowing multiple picks to be completed at once. It should enable a smooth operation in selecting the goods and avoid congestion. The right picking strategy for a warehouse depends on factors such as product mix, warehouse layout, and the volume of the orders.
A guide to order-picking methods can help identify the best picking strategy for every warehouse.
Comparison of order-picking methods
Warehouse Picking Optimization
Warehouses can implement addons on top of their existing warehouse management systems (WMS) allowing them to reduce pick distances by 25 - 40% with a 2-step algorithm approach:
(1) Order clustering - strategically group orders together while considering all factors and reduce pick distances by 15 to 30%.
(2) Picking path optimization - generate the shortest pick path avoiding the typical scenario where a picker will follow intuition which will drive them in a snake pattern picking path, causing longer than necessary walking distances.
The picking path optimization module is applied to the optimized clusters to get the shortest pick path within those clusters allowing to cut walking distance by an additional 10 to 20%.
Improving Warehouse Layout
Determining the optimal slot for each stock keeping unit (SKU), depending on SKU velocity can be done by applying the ABC analysis. In a warehouse, you can categorize your products into A, B, and C based on how quickly they move out of the warehouse.
The principle of ABC analysis includes listing all SKUs which have been ordered over a certain period of time (the longer the time period, the more accurate the data). Then the SKUs are divided into 3 categories – A, B, and C.
A. SKUs are fast-moving items placed near the shipping area for quick picking.
B. SKUs are moderately moving items placed closer to the shipping area.
C. SKUs are slow-moving items placed farther away.
Then depending on your warehouse size and SKU diversity, each category has an allocated percentage. Read more in detail about best practices of warehouse slotting.
Dynamic Slotting
A more advanced approach to ABC analysis is by applying dynamic slotting. Dynamic slotting use cases in warehouses can be divided into 3 main approaches.
- The first use case is during replenishment during inbound operations. When new SKUs arrive and there are multiple available locations dynamic slotting considers factors beyond ABC rotation classes, such as item correlations—like placing keyboards and mice together because they are often sold together, weight, dimensions, product affinity, and stacking priorities.
- The second use case involves re-slotting. At the end of the day, when a warehouse operator has 20 minutes left, they can use this time to swap two SKUs based on SKU velocity or item correlation from that day's order history.
- The third use case involves housekeeping. Over time, an SKU may be spread across multiple locations within the warehouse. Dynamic slotting helps consolidate these SKUs into fewer locations, opening up storage space and improving inventory management.
Learn more about warehouse picking performance & how to maximize order fulfillment efficiency.