Quality-driven workforce performance evaluation based on robust regression and ANOMR/ANOMRV chart

Zhenrui Wang, Sean Dessureault, Jian Liu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The integration of quality improvement and manufacturing system management has emerged as a promising research topic in recent years. Since operators performance variation can be reflected in product quality, workforce performance evaluation should be conducted with quality-based metrics to improve product quality as well as manufacturing system productivity. In this article, a methodology incorporating regression modeling and multiple comparisons is proposed to aid the performance evaluation. The effects of other impacting factors that contribute to operators performance variation are quantified with a robust zero-inflated Poisson regression model. The model coefficients are analyzed with multiple hypothesis tests to identify underperforming machines. Two statistical charts used in multiple comparisons are adopted for identifying underperforming operators. A case study with data from a real-world production system and a simulation experiment are presented to demonstrate the proposed methodology.

Original languageEnglish (US)
Pages (from-to)644-657
Number of pages14
JournalIIE Transactions (Institute of Industrial Engineers)
Volume45
Issue number6
DOIs
StatePublished - Jun 1 2013

Keywords

  • Performance evaluation
  • analysis of means
  • zero-inflated Poisson regression

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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