New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes.

Area of Science:

• Occupational Health and Safety
• Statistical Modeling
• Risk Management

Background:

• The EU-28 region reported 2 fatal accidents per 100,000 people in 2019, with construction, manufacturing, and logistics sectors most affected.
• Existing occupational risk management tools were reviewed based on prevention, simultaneity, and immediacy characteristics.

Purpose of the Study:

• To develop a dynamic methodology for early detection of situations exceeding acceptable occupational risk limits.
• To enable timely corrective actions to mitigate risks and prevent accidents in the workplace.

Main Methods:

• A novel Statistical Risk Control (SRC) methodology was developed, integrating Bayesian inference, control charts, and hidden Markov chain analysis.
• Five inference models utilizing Poisson, exponential, and Weibull distributions were tested.
• Risk parameters were modeled using gamma and normal distributions.

Main Results:

• The SRC methodology demonstrated effectiveness in providing prevention, simultaneity, and immediacy characteristics for risk management.
• The study offered enhanced understanding of operator dynamics and safety barrier performance within the tested scenario.
• The methodology successfully detected deviations from normal operating conditions, allowing for proactive intervention.

Conclusions:

• The Statistical Risk Control (SRC) methodology provides a robust framework for proactive occupational risk management.
• Early detection and intervention are crucial for reducing accident rates in high-risk industries.
• The integration of advanced statistical techniques enhances the ability to predict and control workplace hazards.