Acceptance sampling uses statistical sampling to determine whether to accept or reject a production lot of material. It has been a common quality control technique used in industry.
It is usually done as products leave the factory, or in some cases even within the factory. Most often a producer supplies a consumer with several items and a decision to accept or reject the items is made by determining the number of defective items in a sample from the lot. The lot is accepted if the number of defects falls below where the acceptance number or otherwise the lot is rejected.[1]
In general, acceptance sampling is employed when one or several of the following hold:[2]
- testing is destructive;
- the cost of 100% inspection is very high; and
- 100% inspection takes too long.
A wide variety of acceptance sampling plans is available. For example, multiple sampling plans use more than two samples to reach a conclusion. A shorter examination period and smaller sample sizes are features of this type of plan. Although the samples are taken at random, the sampling procedure is still reliable.[3]
Sampling provides one rational means of verification that a production lot conforms with the requirements of technical specifications. 100% inspection does not guarantee 100% compliance and is too time-consuming and costly. Rather than evaluating all items, a specified sample is taken, inspected or tested, and a decision is made about accepting or rejecting the entire production lot.
Plans have known risks: an acceptable quality limit (AQL) and a rejectable quality level, such as lot tolerance percent defective (LTDP), are part of the operating characteristic curve of the sampling plan. These are primarily statistical risks and do not necessarily imply that a defective product is intentionally being made or accepted. Plans can have a known average outgoing quality limit (AOQL).
When a measured characteristic produces a number, other sampling plans, such as those based on MIL-STD-414, are often used. Compared with attribute sampling plans, these often use a smaller sample size for the same indexed AQL.
Kreyszig, Erwin (2006). Advanced Engineering Mathematics, 9th Edition. Wiley. p. 1248. ISBN 978-0-471-48885-9. Eraldo Banovac, Dražan Kozak. "An analytic review of the characteristics of the Lot Acceptance Sampling Plans used for acceptance of large lots". International Review of Electrical Engineering (I.R.E.E.), Vol. 3, No. 6, November–December 2008, pp. 1070-1076.
Eraldo Banovac, Igor Kuzle. Applicability of the LASPs in the electric-power industry. Proceedings of the International IEEE Conference EUROCON 2009, Saint Petersburg, Russia, May 18–23, 2009, pp. 1152-1157.
Montgomery, D. C. (2009). Statistical Quality Control: A Modern Introduction, Wiley, ISBN 978-0-470-23397-9
Books
- Pyzdek, T, "Quality Engineering Handbook", 2003, ISBN 0-8247-4614-7
- De Feo, J. A., "Juran's Quality Handbook", 2016, ISBN 978-1-25964-361-3
- ASTM E105 Standard Practice for Probability Sampling of Materials
- ASTM E122 Standard Practice for Calculating Sample Size to Estimate, With a Specified Tolerable Error, the Average for Characteristic of a Lot or Process
- ASTM E141 Standard Practice for Acceptance of Evidence Based on the Results of Probability Sampling
- ASTM E1402 Standard Terminology Relating to Sampling
- ASTM E1994 Standard Practice for Use of Process Oriented AOQL and LTPD Sampling Plans
- ASTM E2234 Standard Practice for Sampling a Stream of Product by Attributes Indexedby AQL
- Sampling procedures for inspection by attributes, ISO 2859-1:1999
- Sampling procedures for inspection by attributes, JIS Z 9015-1:2006