Investigating Occupational Accidents based on the Human Factors Analysis and System Classification method: A Case Study in an Industrial and Mining Company

Abstract

Background and Objective: Human errors are a significant issue in various work environments, including industry and mining. The present study aimed to assess occupational accidents in a large industrial mining company using the human factors analysis and classification system (HFACS) method. Materials and Methods: This study conducted a retrospective longitudinal analysis of 253 mining industrial accidents from 2011 to 2019 using the HFACS. Descriptive statistics, such as frequency and percentage of subgroups at each level, were calculated using the HFACS method and SPSS software. Inferential statistics, including analysis of variance, t-tests, and chi-square tests, were performed with a significance level of 0.05. Results: The type of accident was related to throwing/collision or contact with a foreign object, which resulted in a total of 232 injuries and one death. Statistical analysis demonstrated no significant link between the accident type and the educational levels of those involved, as indicated by a P-value greater than 0.05. Still, it pointed to a significant relationship with three other demographic variables: age, marital status, and work experience (P˂0.05). The highest frequency and percentage of the causes of accidents were also due to organizational effects and unsafe practices. Conclusion: As evidenced by the obtained results, the frequency of human errors in the levels of unsafe supervision and unsafe practices significantly contributed to the occurrence of occupational accidents in the company. Analyzing accidents and understanding the relationship between causal factors at the four levels of the HFACS method is vital for implementing effective strategies to reduce accidents.

Keywords

• hfacs
• human error
• human factors
• mining industry
• occupational accident