I recently visited a client last week who had a dilemma with his online retail business; something they were willing to pay hundreds and thousands of dollars to fix. They needed help with their picking efficiency.
My customer came to me and asked me whether their picking process was efficient or not. They asked for a benchmark to compare themselves with, but the simple truth is that it’s very difficult to find one. After taking a look at their historical data, we analyzed their data and fetched their report from their system.
I came up with two KPI’s that I think are great benchmarks for measuring picking efficiency:
The first KPI should be as high as possible, and the second should be as low as possible for an e-commerce warehouse. Why?
First, let’s take a look at what types of picking they were using in their warehouse, and what types of orders they were fulfilling.
They were using cluster or batch picking in their warehouse, picking around 15 orders per trip. Our system was directing them to specific locations, wherein they would simply pick what was there. The client hadn’t considered other methods of picking, or what types of orders were being fulfilled.
Let’s take a more in-depth look the types of picking methods they were using for picking efficiency:
Cluster picking is the process of picking multiple orders in the same picking trip into distinct totes or bins. Generally, multi-tote or multi-bin car/carts are used to execute a cluster pick batch. In a cluster picking batch, each order is assigned to a distinct tote or bin. This is useful for orders with multiple different items, because it minimizes the amount of walking required for each picker, thus increasing picking efficiency.
Batch picking is the process of picking multiple orders in the same picking trip into same car/cart. Generally, a batch picking trip is followed by a sorting or consolidation process which you sort or consolidate batch picked products into distinct totes. The only exception to this process is single item orders. Single items orders are directly transferred to packing stations to be packed. Batch picking is handy for picking multiple single orders at once.
So How Do You Measure Picking Efficiency?
To create the two KPI’s, I analyzed data produced from jobs. For each job, I checked the start and finish time, the number of ORDERS and SKUS the picker picked, the total quantity picked, and the number of locations visited.
From these numbers, I found that their average quantity picked/location was around 2, but it should have been 4. I also determined that their locations visited/ job or trip was around 17, and it should be around 8 or 10 for their warehouse.
We also determined that 35% of their orders are single item orders, meaning they had 1 product in each order. They were picking these orders with a cluster picking approach, which results in a lower number of items picked per trip.
From a productivity point of view, visiting less locations per trip means your trip will be shorter, increasing picking efficiency. Additionally, picking more products per location means you have aligned orders correctly so that most orders that have the same SKUs are in the same batch/trip. This number should always be high.
My first overall solution for picking efficiency was to split or separate their single item order base to a different job. That way, we could create one big batch trip for all single items, and the customer would not need to sort or split those items, since there is only one SKU per order. This will help increase the first KPI because 80% of their orders are single item orders using the same 10 SKU’s. Because of this, one job dedicated to single item orders will take care of 80% all the orders at once.
My second solution was to analyze the data further, and see if any orders are much larger than others. I determined that 5% of their orders have more than 15 products in each order, which would be more efficiently handled with order based picking. I then suggested that they use order-based picking for the 5% of these orders, since this will increase the picking efficiency and productivity in their warehouse. It is difficult to pick larger orders in to small totes in a cluster pick trip.
Visiting less warehouse locations or bins per picking trip means that your picking process will be shorter, and therefore more efficient. Picking more items per warehouse location or bin means that you have aligned and batched your orders correctly, ensuring that most orders that have similar products are in the same batch. Additionally, changing the way you pick your orders can save the number of locations/bins visited per trip, thus saving time and promoting efficiency. The customer loved using these two KPIs as a benchmark to measure their picking process — and now you know how to create your own, too!