Optimal designs of the omnibus SPRT control chart for joint monitoring of process mean and dispersion

Jing Wei Teoh, Wei Lin Teoh, Michael B. C. Khoo, Giovanni Celano, Zhi Lin Chong

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The vast majority of control schemes related to the sequential probability ratio test (SPRT) are designed for the purpose of monitoring only the process mean. Nonetheless, most manufacturing processes are vulnerable to external factors that cause the process mean and variability to change simultaneously. It is, therefore, crucial to consider a joint scheme for monitoring both the location and scale parameters of a production process. In this article, we develop a scheme that combines both mean and variance information in a single SPRT, known as the omnibus SPRT (OSPRT) chart. Expressions for the run-length properties of the OSPRT chart are derived by means of the Markov chain approach. We also propose optimal designs for the OSPRT chart based on two different metrics, i.e. by minimising the average time to signal and the average extra quadratic loss. Through a comprehensive analysis, this article reveals that the optimal OSPRT chart outperforms the classical (Formula presented.) -S, weighted-loss cumulative sum, absolute-value SPRT, and two maximum weighted-moving-average-type charts. The optimal OSPRT chart also has the advantage of collecting a small number of samples on average before producing a decision. Finally, the implementation of the OSPRT chart is presented with a wire bonding industrial dataset.

Original languageEnglish
Pages (from-to)4202-4225
Number of pages24
JournalInternational Journal of Production Research
Volume62
Issue number12
Early online date11 Sept 2023
DOIs
Publication statusPublished - 17 Jun 2024

Keywords

  • Average extra quadratic loss
  • average time to signal
  • joint monitoring scheme
  • sequential probability ratio test
  • statistical process control

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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