%201.svg){kind=small}
Latest posts
Download resources
Sampling: definition and important aspects for buyers
.jpeg)
Sampling in a nutshell:
A random sample inspection is a statistical procedure for quality control in which only a defined part of a quantity of goods is inspected according to specified criteria. For the purchasing department, this enables efficient quality assurance while minimizing the time and cost of incoming goods inspections.
Example: For a delivery of 10,000 screws, 200 pieces are randomly selected and tested according to the AQL standard (Acceptable Quality Level), whereby the entire delivery is accepted if there are a maximum of 3 defective parts in the random sample.
Contents
Sample testing in purchasing controlling
What is sampling?
Sampling is an inspection method in which only part of a total quantity is examined in order to draw conclusions about the whole. In procurement, this means that not all delivered products or documents are fully inspected, but the quality or correctness of the entire delivery is assessed on the basis of selected samples. This method makes it possible to work more efficiently and conserve resources without significantly compromising the reliability of the inspection.
Core elements of sampling
Importance of sampling in purchasing
In the procurement process, random sample testing is an important tool for quality assurance and risk minimization. By carrying out targeted spot checks, buyers can ensure that suppliers comply with the agreed quality standards without having to check each item individually. This saves time and money and helps to make supply chains more efficient.
Application of random sample inspection in incoming goods
The random sample inspection makes it possible to assess the quality of a delivery based on a selected number of products. Statistical methods are used to determine the number of units to be tested in order to be able to make statements about the total quantity with a high degree of certainty.
Calculation example
Situation: A company receives a delivery of 10,000 electronic components.
Step 1: Determining the AQL value (Acceptable Quality Level)
The company accepts a maximum of 0.65% defective parts (AQL = 0.65).
Step 2: Determination of the sample size according to DIN ISO 2859-1
With a batch size of 10,000 and normal inspection, this results in a sample size of 315.
Step 3: Determining the acceptance and rejection number
The following applies for AQL 0.65: acceptance number = 3, rejection number = 4.
Step 4: Carrying out the sampling inspection
A total of 315 components are taken at random and tested.
Result: 2 faulty components were found.
Evaluation: Since the number of errors (2) is less than or equal to the acceptance number (3), the delivery is accepted.
Based on the provided context about statistical sampling in incoming goods inspection, I'll adjust the evaluation section accordingly:
Evaluation and strategic findings
✓ Critical success factors
→ Precise AQL definition: Determination of realistic quality levels based on product risks and customer requirements
→ Training management: Continuous qualification of employees in statistical methods and test procedures
→ Documentation quality: Complete traceability of test results for audit purposes
⚠ Challenges
→ Sampling risk: Despite statistical validation, residual risk of misjudgment remains
→ Weighing up the costs: Balance between testing effort and potential quality risks
→ Supplier integration: coordination of test methods with supplier QM systems
Future trends:
"The integration of AI and machine learning will intelligently optimize spot checks and minimize risks."
→ Automated test procedures with AI-supported fault detection
→ Dynamic adjustment of sample sizes based on supplier history
→ Predictive quality management through real-time data analysis
→ Integration of IoT sensors in quality inspection processes
Conclusion on sampling
ConclusionSample testing is an indispensable tool in modern quality management. It enables efficient quality control with simultaneous cost optimization. The success of this method depends largely on the correct statistical implementation and careful definition of the test parameters. With the advent of digital technologies and AI-supported systems, random sample testing is becoming even more precise and reliable, further minimizing quality risks. The balance between inspection effort and quality assurance remains crucial.
