Occupational health and safety (OHS) is essential for protecting the life, health, and physical integrity of workers. In a complex and dynamic professional context, the prevention of occupational risks has become a priority for employers and decision-makers, going beyond legal compliance to create a safe and efficient work environment. This article explores the history and the main theoretical aspects of OHS and explores the implementation of the ISO 45001 standard and introduces managing workplace health and safety (WHS) risks based on the 5M Method and a weighted composite algorithm for OHS risk assessment integrating factors such as severity, probability, frequency of exposure, number of exposed employees, organizational response capacity, and incident history. Applied in a mixed industrial case study, this approach demonstrated superior risk prioritization compared to the classic severity–probability model. The findings have practical applications: organizations can use the Weighted Composite Score to prioritize interventions, allocate resources efficiently, and prevent high-risk incidents. The approach is adaptable across industries, supporting data-driven safety decisions. The integration of this method supports ISO 45001’s principles of a systematic, proactive, and continuous improvement approach to OHS management.

This paper presents a new risk assessment methodology called the Integrated Risk Framework (IRF) through the application of Ishikawa diagrams combined with the enhanced Hazard Analysis and Critical Control Point (HACCP) system. This risk investigation technique aims to ensure a significantly higher level of quality, safety, and sustainability in food products by using improved classical methods with strong intercorrelation capabilities. The methodology proposes expanding the typology of basic physical, chemical, and biological risks outlined by the ISO 22000 Food Safety Management System standard, adding other auxiliary risks such as allergens, fraud/sabotage, Kosher/Halal compliance, Rapid Alert System for Food and Feed notification, or additional specific risks such as irradiation, radioactivity, genetically modified organisms, polycyclic aromatic hydrocarbons, African swine fever, peste of small ruminants, etc. depending on the specific technological process or ingredients. Simultaneously, it identifies causes for each operation in the technological flow based on the 5M diagram: Man, Method, Material, Machine, and Environment. For each identified risk and cause, its impact was determined according to its severity and likelihood of occurrence. The final effect is defined as the risk class, calculated as the arithmetic mean of the impact derived at each process stage based on the identified risks and causes. Within the study, the methodology was applied to the spring water bottling process. This provided a new perspective on analyzing the risk factors during the bottling operations by concurrently using Ishikawa diagrams and HACCP principles throughout the product’s technological flow. The results of the study can form new methodologies aimed at enhancing sustainable food safety management strategy. In risk assessment using these two tools, the possibility of cumulative or synergistic effects is considered, resulting in better control of all factors that may affect the manufacturing process. This new perspective on studying the dynamics of risk factor analysis through the simultaneous use of the fishbone diagram and the classical HACCP system can be extrapolated and applied to any manufacturing process in the food industry and beyond.

Non-conformities—deviations from established standards or procedures—can significantly impact product quality and process performance. Although various tools and methodologies exist, current research lacks an integrated, deferred, and corrective approach to non-conformance management that bridges day-to-day operations with systematic quality control. The proposed tool aims to address this gap by providing a practical framework that combines batch data processing using the “Daily Challenge” tool with structured problem solving and corrective strategies. It serves as a comprehensive decision-making tool for systematically managing deviations. The methodology begins with identifying non-conformities through data collection and direct observation, followed by focused reporting and active discussion during departmental meetings. Issues are then categorized based on their frequency, operational impact, and resource requirements to determine the appropriate resolution path—whether through immediate correction or detailed analysis using structured tools such as the “Daily Challenge” sheet. It integrates well-established methodologies such as 5M and PDCA into a structured, daily workflow for resolving non-conformities. Implemented solutions are evaluated for effectiveness with ongoing monitoring to ensure continuous improvement. A key feature of this system is the use of the “Daily Challenge” form, which facilitates documentation, accountability, and knowledge retention—helping to reduce the recurrence of similar situations. The case studies illustrate the methodology through two examples: a labeling issue involving the omission of quantity information on product labels due to operator oversight and the management of production downtime caused by equipment and sensor failures. Although a standard existed, the errors revealed the need for reinforced procedures. Corrective actions included revising procedures, retraining personnel, repairing and recalibrating equipment, enhancing maintenance protocols, and using visual documentation to enhance process understanding. The “Daily Challenge” tool provides a replicable framework for managing non-conformities across various industries, aligning operational practices with quality assurance goals. By integrating structured analysis, clear documentation, and corrective strategies, it fosters a culture of continuous improvement and compliance.

This study presents an integrated approach combining environmental risk assessment and experimental performance evaluation for asphalt production plants incorporating reclaimed asphalt pavement (RAP). Unlike previous studies, which focus separately on mechanical performance or environmental impact, our methodology applies a semi-quantitative Environmental Impact Score (EIS), calculated using legal requirements (L), pollutant characteristics (P), and control measure effectiveness (C). The EIS framework is based on ISO 14001 and ISO 31000 principles. The results indicate that significant impacts are mainly associated with high-temperature processes and hazardous materials, while mitigation measures effectively reduce residual risks. The experimental investigation compared conventional asphalt mixtures with mixtures containing 9.71% RAP across different bitumen contents. Key quantitative findings include a 3-point increase in EIS for RAP mixtures due to higher volatile organic compound (VOC) emissions and a 3–8% improvement in Marshall stability and stiffness at lower bitumen contents (3.8–4.2%). The results demonstrate that RAP can enhance mechanical performance while supporting circular economy objectives, provided that environmental risks are actively managed through process control and mitigation measures. This work highlights the novel integration of quantitative environmental scoring with laboratory validation, providing a reproducible framework for sustainable and risk-informed asphalt production.

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.