Acceptance sampling plan based on an exponentially weighted moving average statistic with the yield index for autocorrelation between polynomial profiles

Fu Kwun Wang*, Yeneneh Tamirat

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

8 Scopus citations

Abstract

Acceptance sampling plans based on process yield indices provide a proven resource for the lot-sentencing problem when the required fraction defective is very low. In this study, a new sampling plan based on the exponentially weighted moving average (EWMA) model with yield index for lot sentencing for autocorrelation between polynomial profiles is proposed. The advantage of the EWMA statistic is the accumulation of quality history from previous lots. In addition, the number of profiles required for lot sentencing is more economical than in the traditional single sampling plan. Considering the acceptable quality level (AQL) at the producer's risk and the lot tolerance percent defective (LTPD) at the consumer's risk, we proposed a new search algorithm to determine the optimal plan parameters. The plan parameters are tabulated for various combinations of the smoothing constant of the EWMA statistic, AQL, LTPD, and two risks. A comparison study and two numerical examples are provided to show the applicability of the proposed sampling plan.

Original languageEnglish
Pages (from-to)4859-4871
Number of pages13
JournalCommunications in Statistics - Theory and Methods
Volume47
Issue number19
DOIs
StatePublished - 02 10 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.

Keywords

  • Acceptance sampling plan
  • autocorrelation between polynomial profiles
  • exponentially weighted moving average
  • yield index

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