Why clean master data is the basis for successful inventory sampling

Inventory is a mandatory part of every company that stores goods or materials. It is required by law, provides information about actual stock levels, and ensures transparency in accounting and controlling. In practice, however, it is often associated with high costs, errors, and downtime.

Inventory sampling offers an efficient alternative to traditional full inventory. It reduces the counting effort by up to 95 percent and is permissible under Section 241 (1) of the German Commercial Code (HGB) and the principles of proper accounting (GoBD), provided that recognized mathematical-statistical methods are used. These methods are defined by the Institute of Public Auditors (IDW) and are accepted by tax authorities and auditors.

However, for sample inventory to work smoothly, more than just a recognized method is needed: the quality of the master data determines success or failure.

What is master data and why is it so important for inventory?

Master data is the basic information that uniquely describes items, materials, and storage locations. This includes, among other things:

• Item numbers and descriptions
• Serial and batch numbers
• Storage location identifications
• Units of measure and product groups
• Product information such as dimensions, weight, or value

This data is the “map” of the warehouse. Every movement of goods, from storage to delivery, relies on this master data. If it is complete and correct, stocks can be recorded clearly. If it is incorrect, it leads to inconsistencies that become apparent during inventory at the latest.

Spot checks and master data – a close connection

While a full inventory counts all stocks, a spot check works with a selection: only certain items or storage locations are checked, and the result is extrapolated.

In Germany, two methods are primarily permitted:

• Extrapolation: For manual warehouses with normal stock reliability. The total inventory is determined statistically from a random sample.
• Sequential test: For automated warehouses with high inventory quality. Here, a few random samples are often sufficient to verify the inventory.

Both methods are only audit-proof if the underlying master data is reliable. This is because master data determines what is included in the sample.

Practical examples of risks

• Duplicate item numbers: An item is listed multiple times in the system, distorting the sample.
• Missing storage location data: An item is available in the system but cannot be found in the warehouse.
• Incomplete batch data: Spare parts or semi-finished products cannot be clearly assigned.

The result: discrepancies between target and actual inventory that cannot be plausibly explained. In auditing practice, this often leads to rework or even rejection of the inventory taken.

Typical consequences of poor master data quality

1. Inventory differences – discrepancies that cannot be traced.
2. Correction effort – additional tasks in controlling and accounting.
3. Audit risks – doubts about the reliability of inventories, in extreme cases non-recognition of the procedure.
4. Loss of efficiency – the advantage of sample inventory is lost due to rework and uncertainty.

Requirements for reliable master data

In order for sample inventory to deliver its benefits, companies should ensure that the following criteria are met:

• Uniqueness: No duplicate item numbers or conflicting labels.
• Up-to-date: All postings are recorded on a daily basis.
• Completeness: Master data contains all relevant information – from the item number to the storage location.
• Traceability: Movements are transparently documented and can be checked at any time.
• Responsibilities: Clear responsibilities for maintaining and controlling master data.

Spot inventory versus full inventory

In a full inventory, all stocks are counted, but errors in the master data usually remain undetected. Spot inventory goes one step further: it not only provides an inventory result, but also reveals how reliably processes and data function within the company. This makes inventory more than just a counting procedure – it becomes a quality assurance tool. Any anomalies during the process reveal where master data or processes are unstable. This provides companies with valuable information for improvements that go beyond the actual inventory. The advantages are therefore obvious: those who use random inventory identify weaknesses at an early stage, strengthen the quality of their master data, and thus create a solid basis for secure and sustainable digital inventory management.

Practical check: Is your master data ready for sample inventory?

A quick self-test shows whether the requirements are met:

1. Are all inventories in the system posted on a daily basis?
2. Are serial numbers, batches, and storage locations clearly documented?
3. Are goods receipts and issues recorded correctly?
4. Are inventory differences from previous years within the tolerance range?
5. Is the article master data complete and well maintained?
6. Are there clear responsibilities for data maintenance?

Only if the majority of these questions are answered with “yes” is the basis for an audit-proof sample inventory established.

Conclusion – master data as the key to audit compliance

Inventory sampling is a highly efficient and legally recognized method for conducting inventories faster, more efficiently, and in an audit-compliant manner. In order for it to reach its full potential, a solid foundation is required: clean and reliable master data.

Companies that consistently maintain their master data benefit twice over: Not only do they secure the efficiency gains of random inventory sampling, but they also increase data quality throughout the entire company. This creates trust among auditors, transparency in controlling, and a solid foundation for future digital developments.

Investing in master data quality is therefore much more than an obligation – it is a strategic success factor for modern inventory management, legal security, and sustainable competitiveness.

What measures do you take to improve the quality of your master data?

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