In the world of logistics and warehouse management, inventory is a critical process. With increasing digitalization and the constant quest for process optimization, inventory is becoming more and more important. It is not only a legal requirement, but also an essential tool for efficient warehouse management, an effective supply chain and high inventory quality.
The latter, in turn, is an enabler for further process optimization. For example, high inventory quality is a prerequisite for accurate forecasting. In this blog post, we look at the basics of warehouse inventory, its importance to modern businesses, and how it can be made more efficient.
Inventory is an essential process in which all goods and materials in a warehouse are physically counted and verified. The goal is to reconcile actual inventory with book values to ensure the accuracy of data in inventory control systems. This is a necessary and legally required procedure to ensure transparency in accounting and financial reporting. Companies must comply with national and international regulations, which can increase complexity. Despite the often manual and resource-intensive nature of inventory taking and the associated disruption to regular operations, it is critical to perform regular physical counts. They reveal discrepancies between physical inventory and system data, help identify errors, theft, or loss, and play an important role in process control by highlighting shortages and surpluses. They also support regulatory compliance and risk mitigation.
Sampling and partial inventories are often used to reduce the effort involved, but they do not replace full inventories. This is where inventory automation comes in, to further increase efficiency and transparency, and improve inventory quality over time.
Good preparation of the inventory is critical to its success. A detailed plan is usually created for this purpose. This plan specifies which areas of the warehouse are to be counted, by whom, and at what time. It is often the case that there are not enough staff available, or that the existing staff cannot cope with the additional workload.
As a result, temporary workers are often used for short periods of time during the inventory process: They need to be trained and equipped with the right tools and technology to ensure the accuracy and efficiency of the count.
There are several challenges associated with inventory. The time required to complete an inventory can be significant, especially in large warehouses with extensive inventory. Another challenge is accuracy, as counting errors can lead to inaccurate inventory data. In addition, an inventory can disrupt normal operations, especially if parts of the warehouse must be temporarily closed for the count. These challenges require careful planning and may require the use of specialized technology and processes.
In addition, manually counting products is a tiring task for many people - the susceptibility to error of possibly inexperienced personnel is increased by hours of monotonous work.
Human error: Errors are common when counting or recording inventory. Especially when the work is done manually.
Countermeasure: Supporting the counting process with hand scanners and mobile devices to avoid media breaks, training employees, introducing double checks or AI-assisted recording of products using image recognition systems.
Unclear processes or lack of organization: Unclear or poorly defined inventory processes and poor warehouse organization can lead to inventory errors.
Countermeasure: Standardization of the inventory process and implementation of an efficient, modern warehouse management system, as well as clear instructions for employees. AI approaches can also be used to optimize warehouse organization and reduce walking distances, for example.
Outdated or inaccurate systems and data: Outdated systems and data may not reflect reality, leading to incorrect inventory data.
Countermeasure: Modernization of the systems used, regular updates and maintenance of the software used. Regular comparison of system settings and actual warehouse operations can also help identify error-prone processes early on.
SAP-based solutions provide efficient ways to optimize warehouse inventory. By integrating SAP into the inventory process, companies can manage and analyze their data in real time. SAP enables accurate inventory tracking and control, improves data quality, and supports automated processes. For example, instead of entering count results on paper and then transferring them, you can enter them directly into the SAP interface using a mobile application (such as a tablet or smartphone). Alternatively, the use of scanners or other handheld terminals is also possible. Solutions for special products are also possible: When small goods need to be recorded, it may make sense to weigh the goods. Here, too, a secure and efficient system connection can be implemented. All in all, the inventory process can be optimized, and the inventory results can be recorded more quickly.
It should be noted, however, that the support of counting activities naturally has its limits. Once the possibilities have been exhausted, further efficiency gains can only be achieved through automation.
The use of AI technologies has the potential to fundamentally optimize warehouse inventory. Automated recognition of products in a warehouse can be implemented in a number of ways. In particular, the identification and classification of products is a challenge. Common and widely used solutions are reading product labels or using sensor technology. Reading product labels is generally a suitable approach for identifying specific products.
The prerequisite for reading product labels is that the products to be captured have labels and that these labels are visible. This is not always the case, especially with very inexpensive goods or equipment such as containers or empties. There may also be storage conditions, e.g. in outdoor warehouses, where the application of printed labels is not suitable, e.g. due to weather conditions. The use of sensors also has its drawbacks: By no means all products are already equipped with sensor labels (e.g. RFID or NFC); in practice, this is usually only the case for certain products, usually high-priced or particularly important materials. In addition, there may be interference factors in the warehouse that make reading the tags even more difficult.
The use of AI-based vision can help in these areas. Products that do not have any of the identification features described above can also be detected, as optical features can be used to classify products using computer vision techniques. This approach is like how humans recognize products: When we see a certain product, we often recognize what it is based on visual features such as color, shape, or a distinctive logo. The only requirement is that the products also have visual features that can be used to differentiate them. It is also important that the images of the products, i.e. pictures and videos, are of good quality - where these images come from, whether from a drone, a smartphone, or a fixed camera, plays only a secondary role for computer vision analysis.