Affiliations: [a] Ministry of Education, Science and Technological Development – Republic of Serbia, Belgrade, Serbia
| [b]
Industrial Engineering Department, Faculty of Mechanical Engineering, University of Belgrade, Belgrade, Serbia
| [c] Faculty of Mechanical Engineering, University of Belgrade, Innovation Center, Belgrade, Serbia
| [d] Technical Faculty Mihajlo Pupin, University of Novi Sad, Zrenjanin, Serbia
Correspondence:
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Address for correspondence: V.K. Spasojevic Brkić, Faculty of Mechanical Engineering, Department of Industrial Engineering, University of Belgrade, Kraljice Marije 16, Belgrade, Serbia. Tel./Fax: +381113302318; E-mail: vspasojevic@mas.bg.ac.rs.
Abstract: BACKGROUND: There are recent studies using new industrial workers’ anthropometric data in different countries, but for Serbia such data are not available. OBJECTIVE: This study is the first anthropometric study of Serbian metal industry workers in the country, whose labor force is increasingly employed both on local and international markets. The metal industry is one of Serbia’s most important economic sectors. METHODS: To this end, we collected the basic static anthropometric dimensions of 122 industrial workers and used principal components analysis (PCA) to obtain multivariate anthropometric models. To confirm the results, the dimensions of an additional 50 workers were collected. The PCA methodology was also compared with the percentile method. RESULTS: Comparing both data samples, we found that 96% of the participants are within the tolerance ellipsoid. According to this study, multivariate modeling covers a larger extent of the intended population proportion compared to percentiles. CONCLUSIONS: The results of this research are useful for the designers of metal industry workstations. This information can be used in dimensioning the workplace, thus increasing job satisfaction, reducing the risk of injuries and fatalities, and consequently increasing productivity and safety.
Keywords: Anthropometric measurements, principal components analysis, percentiles
