Reliable Stock Values with Less Counting Effort: Is That Really Possible?

For many companies, the annual inventory count is a major undertaking: Parts of the warehouse come to a standstill, employees count shelf by shelf, and the timeline for the year-end financial statements comes under pressure. Yet in the end, the question often remains: Are all the numbers correct? Inventory sampling promises less counting effort. 

But does a method in which only a portion of the inventory is counted provide figures that the balance sheet and auditors can rely on?

The short answer:

Yes, and not despite, but because of the methodology. Inventory sampling yields a result whose accuracy can be statistically calculated and verified for the auditor. To learn under what conditions this method is permissible under Section 241(1) of the German Commercial Code (HGB), read our article “Inventory Sampling According to the HGB: When It Is Permissible.”

In this article, we answer the question of why the result is reliable and how it is derived.

Why Fewer Counts Do Not Mean Less Accuracy

You’re familiar with this principle from election night: Shortly after the polls close, an exit poll is available that usually comes very close to the official final result. At that point, no one has counted all the votes. The exit poll is still reliable because the data analyzed is selected according to fixed statistical rules, and the potential deviation can be calculated.

The situation is similar with inventory sampling. Legally recognized mathematical and statistical methods assess the inventory quality in your merchandise management, ERP, or warehouse management system and show how reliable your inventory data is. The key factor here is that the items to be counted are selected at random. Each item thus has a known probability of being included in the sample. This allows the result to be reliably extrapolated to the total inventory.

Furthermore, the accuracy is measurable: For the extrapolation, it can be calculated within what section the actual stock value lies with a specified level of confidence. If the deviation remains within the limits set by auditing practice, the result is equivalent to that of a full count.

In addition, fewer items to count mean fewer sources of error. With a full inventory, numerous items must be counted under time pressure. If these are then recorded using paper lists and manual transfers are added to the process, errors can quickly occur. No one measures the extent of these errors. Inventory sampling significantly reduces the number of items to be counted and the associated stress. This gives teams the necessary time and peace of mind to record the selected items with much greater care.
 

How inventory sampling works

1. Provide inventory data:
   The basis is the quantities and values from warehouse accounting. Inventory software, such as INVENT XPERT, applies these data via standard file formats from the ERP, WWS, or warehouse management system—without programming and without direct database access.
    
2. Divide into groups and select a sample:
   In the standard procedure, the software divides the inventory into groups based on value. Items with particularly high value are usually counted in full, while the software draws a random sample from the remainder. This ensures that the portion of inventory that has the greatest impact on the balance sheet value is directly accounted for.
    
3. Counting: 
   Only the selected items are counted using the usual methods: on paper, with a barcode scanner, or with mobile devices. Existing processes remain unchanged.
    
4. Evaluation: 
   In manual warehouses, the result is extrapolated. The software extrapolates the discrepancies between the count and the book inventory to the entire warehouse. In automated warehouses, a sequential test typically verifies whether the book inventory can be confirmed.
    
5. Correcting discrepancies and recording the result: Quantity discrepancies identified in the counted items are corrected directly in the master system. If the error limits are met, the inventory value is finalized. The selection, count, and calculation are thoroughly documented for the year-end audit.
    

What Matters in Inventory Sampling

Proper warehouse record-keeping and traceable documentation are basic requirements. In addition, two factors influence the effort and process:

• Discrepancies lead to a controlled expansion of the sample.
  If the error limits are not met, the sample is expanded—in extreme cases, until the entire affected inventory has been included. Unlike formal deficiencies, which are often only noticed during the audit, statistical deviations become apparent already during the inventory count. The expansion can thus be planned.
   
• Warehouse size and variation determine the effect.
  The required sample size depends primarily on how widely the inventory varies in quantity and value. It increases only slightly with the number of inventory items. The method is therefore most effective in large warehouses with uniform inventory levels.
  
  A practical tip: Coordinate the procedure and process with your auditor before the first count. This avoids discussions shortly before the balance sheet date.

Conclusion: Count Less Without Sacrificing Reliability

Reliable stock values with less counting effort are not a contradiction. Reliability stems precisely from the methodology: The key difference from a full inventory is that errors in recording are not reported anywhere, whereas the accuracy of inventory sampling is verifiable. A stock value is therefore not reliable simply because everything was counted. The groundwork for this is laid before the first count begins: in the data foundation, the process, and early coordination with the auditor.