Machine Learning for Enhancing Workforce Safety in Automotive Manufacturing

Predictive analytics is a powerful machine learning tool for enhancing worker safety in the auto manufacturing sector by proactively identifying hazards and preventing accidents. This work uses supervised learning models, such as random forests and gradient boosting, to examine sensor data, operating logs, and historical accident reports to identify high-risk regions and harmful trends. By using risk score systems, the model assigns safety risk ratings to specific behaviors and regions, allowing for preventative interventions. Predictive maintenance algorithms also assess the machinery's state, reducing potentially dangerous equipment failures. The inclusion of real-time risk assessment dashboards ensures that supervisors receive automatic alerts, enabling timely corrective action. This strategy optimizes workplace safety by reducing manual oversight and improving decision-making with AI-driven insights. By continuously learning from new data, predictive analytics ensures a flexible and dynamic approach to risk minimization, regulatory compliance, and workforce protection.

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