Optimal process adjustment by integrating production data and design of experiments

Jing Li, Hairong Xie, Jionghua Jin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This paper proposes a method to improve the process model estimation based on limited experimental data by making use of abundant production data and to achieve the optimal process adjustment based on the improved process model. The proposed method is called an Estimation-adjustment (EA) method. Furthermore, this paper proves three properties associated with the EA, which guarantee the feasibility and effectiveness of using EA for integrating production and experimental data for optimal process adjustment. Also, the paper develops a sequential hypothesis testing procedure for implementing the EA. The properties and implementation of the EA are demonstrated in a cotton spinning process.

Original languageEnglish (US)
Pages (from-to)327-336
Number of pages10
JournalQuality and Reliability Engineering International
Issue number3
StatePublished - Apr 2011


  • design of experiments
  • process adjustment
  • sequential hypothesis testing

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


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