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Integration of PCA and DEA for identifying and improving the impact of Six Sigma implementation on job characteristics in the automotive industry

Summary: [This abstract is based on the authors' abstract.] This study presents an integrated approach, based on data envelopment analysis (DEA) and principal component analysis (PCA) methods, to evaluate the influence of Six Sigma deployment on key job characteristics in an automotive industry. The job characteristics are defined as satisfaction, stress, and security. A standard questionnaire is designed and distributed among the employees at the company’s production site, who were affected by the implementation of Six Sigma. DEA and PCA methods are applied to measure the performance of the sub-groups of employees in the company. Consequently, the most efficient and inefficient sub-groups are determined. According to the findings of this investigation, it was perceived that the implementation of Six Sigma has had the greatest impact on job satisfaction. Additionally, a design of experiment was carried out to recognize the most effective job factor, which was identified to be the overall working conditions for the related case study. This is the first study that integrates DEA and PCA toward identifying and optimizing job characteristics in terms of Six Sigma implementation. The approach, employed in this study, can be easily used in the other manufacturing systems, in order to assist them to identify and improve their key job characteristics.

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  • Topics: Engineering, Design of Experiments, Six Sigma
  • Keywords: Six Sigma, Design of experiments (DOE), Case study, Implementation, Principal components, Analysis
  • Author: Azadeh, A.; Nasirian, B.; Salehi, V.; Kouzehchi, H.;
  • Journal: Quality Engineering