Two-stage order picking
Two-stage picking, also known as batch picking or two-stage order picking, is an efficient procedure in logistics that aims to optimize order processing and speed up picking processes. In this process, several customer orders are combined into a so-called batch and processed in two successive stages.
In the first stage, picking takes place on an item-by-item basis, with the items being removed in the total quantity required. The grouping of multiple orders enables more efficient use of warehouse resources and reduces the effort required to pick each order separately. This results in higher productivity and improved utilization of warehouse staff.
The second stage is order-by-order sortation, where previously picked items are distributed to individual customer orders. An automatic sorter is often used here, which sorts the items according to specific criteria such as order number, destination or product type. The use of an automatic sorting system further speeds up the process and minimizes the risk of errors when allocating items to the respective orders.
One advantage of two-stage picking is that it is particularly well suited to companies that process a high number of smaller orders. By bundling multiple orders into batches and using automated sortation systems, throughput time can be significantly reduced. This results in faster provision of inventory and an increase in customer satisfaction.
There is also a variant of two-stage picking called „shortened two-stage picking.“ In this case, the items are not picked item by item in the first stage, but order by order, and then sorted directly. This method is particularly suitable for companies with a high volume of similar orders.
In summary, two-stage picking (batch picking) is an innovative logistics process that increases the efficiency of order processing. By grouping customer orders into batches and using automated sortation systems, companies can optimize their picking processes, reduce throughput times and improve the overall performance of their logistics system.
Source: logipedia / Fraunhofer IML