A model to predict and optimize machine guarding operator compliance activities in a bottling process plant: a developing country experience

Abstract

Introduction. The accurate tracking, elimination and control of hazards are fundamentals in accident avoidance at operational machine guarding stations. This article develops a machine guard usage compliance model. Nonetheless, very few studies account for operator compliance to the usage of machine guards in workplaces. Methods. This article contributes by first building up a multiple regression (MR) model, and, second, proposing a novel integrated MR and Taguchi method (MR-TM) model that optimizes operator compliance to guard usage. The comparative significance of the diverse factors was appraised and examined via analysis of variance. Results. Bottling process data from Nigeria illustrate the effectiveness of the proposed model. The coefficient of determination (R 2 = 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-compliances (guards present and operational but not used) (p < 0.050). Simulated and field data variables exhibited good agreement (R 2 = 0.997). From the MR-TM model, the most significant result is the highest operator compliance for machine guard usage with mean and signal-to-noise ratio values of 269.28 and 48.60, respectively. Conclusion. This work provides safety managers with snapshot information for planning and control purposes.