@article{ref1,
title="A model to predict and optimise machine guarding operator's compliance activities in a bottling process plant: a developing country experience",
journal="International journal of occupational safety and ergonomics",
year="2018",
author="Uzor, Chukwunedum and Oke, Sunday Ayoola",
volume="ePub",
number="ePub",
pages="1-22",
abstract="INTRODUCTION: The accurate tracking, elimination and control of hazards are fundamentals in accident avoidance at the operational machine guarding stations. This paper develops a machine guard usage compliance (MGUC) model. Nonetheless, very few studies account for operator's compliance to the usage of machine guards in workplaces. <br><br>METHODS: This paper contributes by first building up a multiple regression (MR) model, and second, to propose a novel integrated MR and Taguchi method (MR-TM) model that optimises the operator's compliance to guard usage. The comparative significance of the diverse factors was appraised and examined via analysis of variance (ANOVA). <br><br>RESULTS: A bottling process data from Nigeria illustrates the effectiveness of the proposed model. The coefficient of determination (R<sup>2</sup> = 0.997) established the efficient predictive ability of the MR model. The significant variables are the number of functional guards and damaged guards, and the number of non-compliants (p < 0.05). Simulated and field data variables exhibited good agreement (R<sup>2</sup> = 0.997). From the MR-TM model, the most significant result is the highest operator's compliance for machine guard usage with mean and signal-to-noise ratio values of 269.28 and 48.60, respectively. <br><br>CONCLUSION: The work provides safety managers with snapshot information for planning and control purposes.<p /> <p>Language: en</p>",
language="en",
issn="1080-3548",
doi="10.1080/10803548.2018.1520471",
url="http://dx.doi.org/10.1080/10803548.2018.1520471"
}