A model to predict and optimize machine guarding operator compliance activities in a bottling process plant: a developing country experience. Issue 4 (1st October 2020)

• Record Type: Journal Article
• Authors: Uzor, Chukwunedum; Oke, Sunday Ayoola
• Is Part Of: International journal of occupational safety and ergonomics. Volume 26:Issue 4(2020)
• Page Start: 780
• Page End: 801
• Publication Date: 2020-10-01
• Subjects: safety -- hazards -- process plant -- manufacturing -- risk; Human engineering -- Periodicals; Industrial safety -- Periodicals; Human Engineering -- Periodicals; Occupational Health -- Periodicals; Ergonomie -- Périodiques; Sécurité du travail -- Périodiques; Human engineering; Industrial safety; Periodicals; 620.8
• DOI: 10.1080/10803548.2018.1520471
• Languages: English
• ISSNs: 1080-3548

Abstract

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.