Why picking performance matters
Successful warehouses rely on data-driven strategies. As order volumes grow (especially in high-volume e-commerce operations), it's important to track the right data and Key Performance Indicators (KPIs) to understand and improve warehouse operations. Warehouse picking performance is one of the main KPIs for outbound fulfillment efficiency.
This metric helps managers spot areas for improvement and estimate how long picking tasks should take.
Picking performance provides insight into how well your order-picking process works, from the moment orders come in to when they ship out. It can be measured using several key metrics and indicators. Below are some of the most important ones and what they mean.
Picking KPIs that explain what is happening
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
Picking rate is the speed of picking, usually measured as how many items a worker picks per hour. It can be calculated by dividing the total number of picks by the total time spent picking.
Picking rate = Total Picks / Total Time Spent
For example, if a picker collected 120 items during an 8-hour shift, their picking rate is 15 items per hour. According to industry data, a good average picking rate is about 71 items per hour.
Keep in mind this number can vary widely based on factors like your warehouse layout, order profiles, product types, and any congestion in the aisles. A well-organized warehouse with a logical layout and an efficient picking method will help workers maintain a high picking rate.
Picking Accuracy
Picking accuracy measures how often orders are picked correctly without errors.
A higher accuracy means pickers are consistently choosing the right items in the right quantities, which leads to fewer returns, customer complaints, or repacking work.
One simple way to measure accuracy is to compare the number of error-free orders to the total number of orders.
Picking accuracy = Overall number of Orders / Number of faulty orders
For instance, if 100 orders were picked and 5 of them had errors (wrong item or quantity), that means 95% of orders were correct. In other words, the picking accuracy in that case would be 95%.
Order Picking Cycle Time
Order picking cycle time is the duration from when an order is received to when it’s ready for shipping.
This metric reflects how efficient and responsive your picking process is. A shorter cycle time means your warehouse can fulfill orders faster, which is critical for meeting customer expectations in e-commerce.
To measure cycle time, track timestamps in your process: when the order is received or released to the warehouse, when picking for that order starts, when the order is packed, and finally when it’s shipped out.
For example, if there's a long gap between order receipt and picking start, you might need to improve how orders are prioritized or assigned to pickers. Reducing the order picking cycle time leads to faster order fulfillment and happier customers.
Picking Utilization
Picking utilization shows how much of your warehouse’s picking capacity is being used at a given time.
It answers the question: Are your picking resources (people and equipment) being used efficiently, or do you have idle time? A higher utilization percentage indicates that your pickers are busy and the warehouse is making full use of its labor and equipment.
Ideally, you want a high utilization without overworking your staff. Monitoring this metric helps in labor planning and in identifying if you can handle more order volume with existing resources or if there's room to improve how work is scheduled.
Pick Quality
Pick quality measures how well the picking process meets customer needs and expectations. It’s not just about speed, but about doing the job right. High pick quality means items are handled correctly (not damaged), the correct items are picked, they’re packed properly, and orders are complete and accurate when they reach the customer.
You can track pick quality by looking at customer feedback, return rates, or internal quality checks. For example, if customers frequently report broken items or wrong products, that’s a sign of poor pick quality.
How to improve picking performance
Improving picking performance often involves a combination of better training, smarter processes, and the right tools. Here are some effective strategies for boosting your warehouse’s 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.
Well-trained pickers work faster and make fewer mistakes.
To improve performance, make sure every picker is properly trained in the best practices of your warehouse. This includes training on the correct picking techniques (like how to physically handle items and navigate the aisles efficiently) and on how to use any technology or systems you have in place. For example, if your warehouse uses barcode scanners or pick-to-light systems, ensure that all staff know how to use them quickly and accurately.
The Right Picking Strategy
Selecting the most suitable picking strategy for your operation can significantly increase efficiency. There are various order picking methods (such as single-order picking, batch picking, zone picking, wave picking, cluster picking, etc.), and each has its pros and cons.
The key is to choose a strategy that lets your pickers do more work in less time without causing congestion or confusion in the warehouse. A good picking strategy should enable pickers to handle multiple orders or items in one go when possible, rather than making separate trips for each item.
Warehouse Picking Optimization
Beyond training and strategy, warehouses can leverage optimization tools and techniques to improve picking performance even further, optimization that typical warehouse systems often lack. One effective approach is to use software add-ons or advanced algorithms on top of your existing Warehouse Management System (WMS), commonly known as Warehouse Optimization Software (WOS). These optimization tools analyze orders and warehouse layout to reduce unnecessary travel for pickers.
For instance, warehouses can implement a two-step algorithm that can cut down walking distances by 20-50%:
- Order Clustering: Instead of picking orders one by one, the system groups orders in a way that items located near each other are picked together. By strategically grouping orders, pickers can collect items for multiple orders in a single trip through a section of the warehouse. This method alone can reduce the distance traveled during picking by about 15% to 30%.
- Optimized Picking Paths: After clustering orders, the next step is to determine the shortest route a picker should take to grab everything in that cluster. Often, if left to intuition, a picker might walk in a "snake pattern" up and down aisles, which isn’t always efficient. Picking path optimization uses algorithms to map out the ideal path that avoids backtracking and unnecessary steps. By following an optimized path, a picker can further cut down walking distance by roughly 10% to 20%.
Warehouse Slotting
How you organize your warehouse slotting directly affects picking performance. One popular approach to layout optimization is the ABC analysis for slotting products. This method involves categorizing your inventory into three groups based on how fast items move (their turnover rate or "velocity"):
- "A" items (fast-movers): These are your top sellers or most frequently picked items. Store A items closest to the packing/shipping area or at the most easily accessible locations in the warehouse. The idea is to minimize the travel time for items that you pick often. For example, in an e-commerce warehouse, A items might be the top 20% of SKUs that account for the bulk of orders, and they should be right at hand for pickers.
- "B" items (moderate-movers): These products sell regularly but not as frequently as A items. B items should be kept a little further from the shipping area than A items, but still in reasonably accessible locations. They might make up the next 30% of your products in terms of order frequency. You don't need them at the absolute front of the warehouse, but they shouldn't be too far away either.
- "C" items (slow-movers): These are infrequently ordered products. C items can be stored in the back or upper levels of the warehouse since they are rarely picked. They could represent, say, 50% of your SKUs but only a small fraction of order lines. Keeping them out of the prime picking zones frees up space for faster-moving goods.
Using ABC categorization, you allocate space according to item importance: typically a small portion of products (A) get the most convenient locations, and the slowest movers (C) occupy the least convenient spots.
This ensures that the majority of picks (which come from A and B items) happen in the most efficient area of the warehouse. Periodically reviewing your ABC classifications is important, because items can move between categories if demand changes. By implementing these slotting best practices, you reduce walking time and make your picking process more efficient.
Dynamic Slotting
While ABC analysis is based on historical data and usually updated periodically, dynamic slotting is a more advanced and continuous approach to managing product locations. AI slotting means regularly adjusting where items are stored in the warehouse based on real-time data and changing demand patterns. This approach can be broken down into a few key use cases:
1. Inbound Replenishment Placement: When new stock arrives (during the replenishment process), dynamic slotting determines the best available location for it right away. Instead of just refilling a standard shelf location, the system considers factors beyond simple A/B/C velocity classes. It looks at item correlations (for example, placing items commonly bought together, like keyboards and mice, close to each other), as well as the item's weight, size, and stacking or handling requirements. By taking these into account, the new inventory is placed in a spot that will make future picking easier and faster. Essentially, every time you restock, you are automatically optimizing your layout for upcoming orders.
2. On-the-fly Re-slotting: Dynamic slotting can also be used for re-slotting items during downtime. For instance, at the end of the day, a worker might have 20 minutes free, during which they could swap or move products to a better location. If a certain SKU’s demand spiked today, dynamic slotting might move it to a more accessible spot for tomorrow. This kind of continuous improvement ensures your layout adapts quickly to short-term trends without a big reorganization project. Even small adjustments, like moving a suddenly popular item closer to the front, can save time if that popularity continues.
3. Housekeeping and Consolidation: Over weeks and months, inventory can become scattered – for example, a single SKU might end up split across multiple bins or shelves as stock was put away in various open spots. Dynamic slotting includes housekeeping routines to consolidate those scattered items. This means gathering the same SKU into one location (or fewer locations) when possible. By doing so, you open up extra space and make inventory counts easier to manage. It also prevents pickers from having to search multiple spots for the same product. Regular consolidation keeps the warehouse organized and ensures that your fast-moving items stay in prime locations while slower items don't clutter up valuable space.
These adjustments can lead to sustained improvements in picking efficiency and are particularly useful in high-volume environments where demand can change quickly. (For more insights on optimizing picking performance and maximizing order fulfillment efficiency, you can refer to additional resources or case studies.)
Calculating picking optimization savings
After implementing improvements, it's a good idea to quantify the benefits.
In other words, calculate how much time and money you save by optimizing picking. For example, if you reduced the average picking path by 30%, how does that translate into labor hours saved per week or per year? Many warehouse managers are surprised at how much even small efficiency gains can add up.
By estimating your picking optimization savings, you can build a strong business case for continued investment in training, better tools, or software enhancements. In high-volume operations, every minute saved per order contributes to significant yearly savings. Knowing these numbers helps highlight the value of the changes and can guide where to focus next.
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