Enhancing Process Safety and Manufacturing Performance Through 5M-Based Causal Analysis and Source–Detection Matrices

Occupational safety is increasingly addressed through engineering-based, proactive risk management approaches that emphasize early identification and mitigation of hazards within industrial systems. In manufacturing environments, the analysis of minor process deviations, near misses, and low-impact operational failures provides critical insights for improving system reliability and safety performance. This paper proposes an integrated engineering framework that combines the 5M model (Environment, Man, Method, Material, Machine) with Source–Detection Matrix analysis to support structured identification, classification, and control of safety-related process deviations. The approach enables systematic root-cause analysis by categorizing contributing factors according to the 5M model, followed by mapping each deviation based on its origin and point of detection within production processes. The methodology was validated through case studies conducted in cosmetics and perfumery manufacturing, involving process-related hazards such as electrical failures and exposure to volatile substances. Validation was also supported by operational data collected over two 6-month periods before (n1) and after implementation (n2), based on incident reports, near-miss records, nonconformity reports, and internal audit data (n1 = 128; n2 = 95). Quantitative results show improved safety performance, including an increase in Detection at Source Rate from 42% to 74% and a reduction in Minor Incident Frequency from 11 to 5 cases/month. The results demonstrate that integrating causal analysis with detection mapping enhances early-stage identification of process deviations, effectively limiting failure propagation across operational stages and improving overall process safety performance. The proposed framework provides a practical and data-driven tool for improving process reliability, operational safety, and continuous improvement in complex manufacturing environments.