Zugriffshäufigkeit

What is access frequency?

Access frequency describes how often an item or storage location is retrieved, picked, or moved within a specified period – for example, per day, week, or month. It is typically measured by the number of picks, withdrawals, or warehouse movements recorded by the warehouse management system (WMS). Unlike inventory turnover, which relates to stock levels, access frequency is a process-oriented metric: it does not describe how much goods flow through the warehouse, but how often a person – or an automated system – physically accesses a storage location.

How is access frequency used in practice?

The most important application is ABC analysis by access frequency. Items with a high access frequency (A-items) are placed as close as possible to the dispatch point to minimize travel distances. Items with a low frequency (C-items) are moved to more remote or less ergonomically favorable storage areas. This principle is known as slotting or storage location optimization and is one of the most effective measures for improving picking performance without capital investment.

In addition, access frequency feeds into the following decisions:

• Zone planning: High-frequency items are assigned to a fast-pick zone, slow-moving items to a slow-mover zone.
• Re-slotting: Regular review and adjustment of storage location assignments when the product range or seasonality changes.
• Choice of picking method: For very high access frequencies, the use of automated systems such as goods-to-person solutions may be economically viable.
• Evaluation of automation projects: The frequency distribution across all items influences whether and to what extent automation yields a viable business case.

What matters when collecting and using this data?

For access frequencies to provide a reliable basis for decision-making, several points must be observed. First, the observation period should match the question at hand: seasonal fluctuations can distort a purely weekly view. It is advisable to compare multiple time windows. Second, data quality is critical – incorrect warehouse postings or unrecorded withdrawals will skew the analysis. Third, a distinction should be made between the frequency of picks and the quantity moved per pick: an item may be accessed infrequently but withdrawn in large quantities – this gives a different picture than a small item picked every day. Depending on the objective, one perspective or the other will be more relevant. Vendors of WMS and slotting software offer different analysis models; which approach fits best depends on assortment size, order structure, and existing IT infrastructure.