Order picking is an indispensable part of modern warehousing and distribution processes. It forms the link between warehousing and shipping and thus plays a key role in intralogistics. Due to the increasing growth of e-commerce, the rising demand for short delivery times, and the individualization of customer orders, order picking is becoming increasingly important. Companies are faced with the challenge of responding to changing customer requirements with a high degree of flexibility while ensuring maximum efficiency. This guide provides a comprehensive overview of all relevant aspects of order picking – from the basics and procedures to modern technologies and optimization strategies.

Order picking refers to the process of assembling goods from warehouse stock in line with demand to fulfill specific customer orders or production orders. This activity encompasses all process steps from receiving order picking requests to picking the items and handing them over to shipping. The relevance of order picking lies not only in the operational processing of orders, but also in its strategic importance for the entire supply chain process. Efficiently organized order picking shortens delivery times, reduces costs, and improves customer satisfaction.

Order picking can be either order-based (single-stage), where each customer order is processed separately, or item-based (multi-stage), where the same items are picked for several orders at the same time and then sorted. While single-stage picking is mainly common in smaller warehouses with lower order volumes, multi-stage picking is often used in large distribution centers with thousands of small orders.

Single-stage picking is the classic form of picking. The picker processes an order from start to finish, collecting all the required items one after the other. This method is particularly easy to organize and offers clear order allocation, eliminating the need for subsequent sorting. However, the effort per order is relatively high, especially if the walking distances through the warehouse are long. Single-stage picking is therefore more suitable for warehouses with a manageable range of products or low order density; in some cases, it can even be done without system support.

In contrast to this is multi-stage picking, also known as batch picking. Here, several customer orders are bundled and picked item by item in a first stage. Only in a second stage are the items assigned to the individual orders. This method significantly reduces travel times, as each storage location is only accessed once, regardless of how many orders require the respective item. The sorting work is usually done by automatic sorters, which further increases efficiency. Multi-stage processes are particularly advantageous when a company processes a large number of orders with only a few items per order, as is common in mail order, for example.

There are various organizational approaches within single-stage picking. In serial picking, an order is processed sequentially by one picker or several employees in different zones. This has the advantage that each employee is only responsible for a specific area and the distances within their zone are minimized. However, the disadvantage is that the order must be temporarily stored at transfer points, which can lead to delays.

Parallel picking, on the other hand, divides an order into several zones that are processed simultaneously. Once all sub-orders have been completed, the items are consolidated at a consolidation point. This process significantly reduces the throughput time per order, but requires precise coordination by a warehouse management system and an additional consolidation stage.

Traditionally, order picking is carried out using paper pick lists, but companies are increasingly turning to paperless methods to save time and reduce errors. Modern pick-by systems such as pick-by-voice, pick-by-light, or pick-by-scan guide the picker digitally through the process. With pick-by-voice, the employee receives all instructions via a headset and confirms the picks by voice command, leaving both hands free. Pick-by-light, on the other hand, uses LED signals directly at the storage location to indicate which items need to be picked. Pick-by-scan and mobile data collection devices replace the traditional paper slip with a display that dynamically shows the orders and communicates with the warehouse management system in real time.

Even more innovative are pick-by-vision systems, which guide the picker through the warehouse with AR data glasses. Relevant information is displayed directly in the field of vision, such as the exact position of the item or the required quantity. Some companies are even using robots that independently pick items from shelves and bring them to a collection station. This automation, known as pick-by-robot, is particularly interesting where monotonous and physically demanding tasks can be automated.

A fundamental criterion in order picking is whether the employee goes to the goods or the goods come to the employee. In man-to-goods order picking, the order picker moves through the warehouse and collects the required items. This method is flexible and requires only a small investment in technology, but it involves a lot of walking. This can be a significant time factor, especially in large warehouses.

Goods-to-person picking follows the opposite principle. Here, the required goods are automatically brought to a central picking station where the employee picks the items. This is often done using conveyor systems, shuttles, or automatic small parts warehouses. This method reduces physical strain and increases productivity but requires high investment costs and reliable conveyor technology.

According to VDI guideline 3590 Sheet 1, the order picking process consists of several consecutive steps, ranging from order creation to final delivery. First, the picking orders are generated and transmitted to the picker, either electronically or in paper form. The goods are then transported to the staging area or the picker goes to the storage location. The actual removal is confirmed by a receipt, which simultaneously triggers an inventory reconciliation in the warehouse management system. The picked items are then transported to the delivery point, where they are either packed or sorted directly, depending on the procedure.

The exact design of these steps depends heavily on the structural and procedural organization of the warehouse. Factors such as the warehouse structure, the type of items (e.g., hazardous goods or temperature-sensitive goods), and the technology used play a decisive role here.

Picking time consists of several sub-times, including base time, picking time, dead time, and travel time. Travel time is a decisive factor, as pickers in an average warehouse often cover several kilometers per day. Therefore, route optimization or route planning is a central issue in logistics. The aim is to make the walking distance as short as possible by arranging storage locations sensibly and optimizing picking routes.

A classic mathematical problem in this context is the “traveling salesman problem.” It describes the challenge of finding a route that connects all picking locations with minimal time or distance. Modern warehouse management systems use algorithms to solve this problem dynamically and calculate the most efficient route. In addition, items with a high turnover rate are preferably placed in the front areas of the warehouse, while items that are rarely needed are stored further back.

Batch formation is one of the most effective strategies for increasing efficiency. This involves combining several customer orders and picking them in a single round trip. Instead of processing each order individually, the picker picks a larger quantity of an item and then divides it among the respective orders. This approach significantly reduces the number of trips and increases picking performance. In conjunction with sorting systems or intelligent software solutions, the process can be further automated and accelerated.

Various key figures are used to evaluate the efficiency of an order picking process:

• Order picking time: The average time required to complete an order.
• Picking performance: Number of items or orders successfully picked per unit of time.
• Error rate: Percentage of orders assembled incorrectly.
• Throughput time: Total time from order receipt to completion.

These indicators help to identify potential for improvement and to control processes.

In practice, companies rely on different strategies depending on their industry and order structure. Amazon, for example, uses a combination of robotics, pick-to-light systems, and batch picking to process millions of orders every day. In the automotive industry, sequence-accurate picking according to the just-in-time principle is common, whereby components are provided in exactly the order in which they are needed in production. Pharmaceutical wholesalers, on the other hand, often work with highly automated small parts warehouses and sorting systems to be able to process the large number of small orders quickly.

Order picking is a complex interplay of processes, technologies, and organization. It has a decisive influence on the performance of a warehouse and thus on the competitiveness of a company. While traditional methods such as single-stage, order-oriented picking in small structures continue to make sense, large logistics centers are increasingly relying on multi-stage, automated solutions.

Digitalization is fundamentally changing order picking. Robots are increasingly taking over picking tasks, for example for standardized small parts. At the same time, AI and machine learning are finding their way into intralogistics. They help with inventory forecasting, route optimization, and dynamic control of batch processes.

The future of order picking will be shaped by automation, digitalization, and artificial intelligence. Fully automated warehouses, in which robots and driverless transport systems handle orders independently, will continue to gain in importance. At the same time, there will be demand for flexible solutions that can efficiently process both small and large order quantities. Augmented reality, robotics, and predictive analytics are just the beginning. Sustainability will also play a role in the future, for example through energy-efficient systems or optimized warehouse logistics to avoid unnecessary transport.