What is adaptive sampling in SAP SRD-PLM-QA - PRO-Quality Assurance?

SAP Term: adaptive sampling

• Component: SRD-PLM-QA

• Component Name: PRO-Quality Assurance

• Description: A method that uses inspection history to determine sample size and inspection frequency. It is defined using the adaptive sampling rule.

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• Key Concepts: 
  Adaptive sampling is a quality assurance technique used in SAP SRD-PLM-QA PRO-Quality Assurance. It is a statistical sampling method that uses the results of previous samples to adjust the size and selection of subsequent samples. This allows for more efficient and accurate sampling, as it takes into account the variability of the population being sampled. 
  
  How to use it: 
  Adaptive sampling can be used in SAP SRD-PLM-QA PRO-Quality Assurance to ensure that the quality of products is maintained. It can be used to identify areas where quality needs to be improved, as well as to monitor the effectiveness of corrective actions taken. To use adaptive sampling, a sample size and selection criteria must be determined. The sample size should be large enough to provide an accurate representation of the population being sampled, while the selection criteria should be based on the characteristics of the population. Once these parameters have been determined, samples can be taken and analyzed. The results of these samples can then be used to adjust the sample size and selection criteria for subsequent samples. 
  
  Tips & Tricks: 
  When using adaptive sampling in SAP SRD-PLM-QA PRO-Quality Assurance, it is important to ensure that the sample size and selection criteria are appropriate for the population being sampled. Additionally, it is important to ensure that the results of each sample are accurately recorded and analyzed in order to properly adjust the sample size and selection criteria for subsequent samples. 
  
  Related Information: 
  Adaptive sampling is just one of many quality assurance techniques used in SAP SRD-PLM-QA PRO-Quality Assurance. Other techniques include statistical process control, process capability analysis, and design of experiments. Additionally, there are a variety of tools available for analyzing data collected from quality assurance processes, such as statistical software packages and data visualization tools.