Acceptance Sampling Plans Based on Conformance Probability—Inspection of Lots and Processes by Attributes

Steffen Uhlig; Bertrand Colson; Roger Kissling; Sam Ellis; Mike Hicks; John Vandenbemden; Francesca Pennecchi; Rainer Göb; Petra Gowik

doi:10.20944/preprints202409.2228.v1

How to cite: Uhlig, S.; Colson, B.; Kissling, R.; Ellis, S.; Hicks, M.; Vandenbemden, J.; Pennecchi, F.; Göb, R.; Gowik, P. Acceptance Sampling Plans Based on Conformance Probability—Inspection of Lots and Processes by Attributes. Preprints 2024, 2024092228. https://doi.org/10.20944/preprints202409.2228.v1 Uhlig, S.; Colson, B.; Kissling, R.; Ellis, S.; Hicks, M.; Vandenbemden, J.; Pennecchi, F.; Göb, R.; Gowik, P. Acceptance Sampling Plans Based on Conformance Probability—Inspection of Lots and Processes by Attributes. Preprints 2024, 2024092228. https://doi.org/10.20944/preprints202409.2228.v1

Abstract

A novel approach for designing acceptance sampling plans is presented. It is based on the Bayesian conformity assessment framework from JCGM 106. In particular, the concept of conformance probability plays a central role in the new approach. The new approach takes into account both the producer's and the consumer's perspective by allowing each to have their own prior. In parallel, the JCGM 106 framework allows the definition of Bayesian consumer's and producer's risks which complement the "classical" risks defined in the ISO 2859 and ISO 3951 series. The new approach also casts a new light on the plans from the ISO standards.

Keywords

acceptance sampling plan; conformity assessment; Bayesian statistics; producer's risk; consumer's risk; conformance probability; lot inspection by attributes

Subject

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Acceptance Sampling Plans Based on Conformance Probability—Inspection of Lots and Processes by Attributes

Abstract

Keywords

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