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AQL Table – Acceptance Quality Limit for Product Inspections

AQL Table – Acceptance Quality Limit for Product Inspections

An Acceptable Quality Limit (AQL) table is a standard reference tool used in product inspections to determine the acceptable level of quality defects in a batch of products. The AQL table helps inspectors and quality control personnel make decisions about whether to accept or reject a shipment based on the number of defects found during inspection.

The AQL table typically consists of rows and columns, with each row representing a different sampling plan and each column representing a different AQL level. The sampling plan specifies the number of units to be inspected from the batch, and the AQL level specifies the maximum number of defects allowed in the sample for the batch to be accepted.

Here’s an example of a simplified AQL table:

Sampling PlanAQL LevelSample Size (n)Acceptance Number (c)
I0.65805
II1.01257
III1.520010
IV2.531514
V4.050021
VI6.580032
VII10.0125050

In this example:

  • Sampling Plan: Refers to different inspection levels or sample sizes.
  • AQL Level: Indicates the acceptable quality level, which represents the maximum percentage of defects allowed in the batch.
  • Sample Size (n): Specifies the number of units to be randomly selected and inspected from the batch.
  • Acceptance Number (c): Represents the maximum number of defects allowed in the sample for the batch to be accepted.

Inspectors compare the number of defects found in the sample during inspection to the acceptance number specified in the AQL table. If the number of defects is equal to or less than the acceptance number, the batch is accepted. If the number of defects exceeds the acceptance number, the batch is rejected.

It’s important to note that the AQL table is just one component of a comprehensive quality control process, which may include other inspection methods, quality standards, and criteria specific to the product and industry.

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Milind patel is an experienced practitioner and thought leader in the field of Business Process Management (CI) and 0.4 lean application. He co-founded Pro lean academy, a consulting company focusing on performance improvements and appropriate digitalization application in manufacturing process