Applying Predictive Analytics to Occupational Health and Safety in India

Predictive analytics is revolutionizing occupational health and safety (OHS). It offers evidence-based insights. These insights enable proactive risk management and informed, data-driven decision-making in organizational settings. This paper explores the key components of predictive analytics in OHS, beginning with data collection, management, and preparation, and moving through to advanced predictive modelling techniques. We emphasize the importance of data integrity through processes such as missing value imputation, anomaly detection, and feature engineering to ensure accurate model predictions. Risk prioritization identifies and ranks hazards across various factors, including employee behaviours, organizational policies, environmental conditions, and operational practices. We posit that insights derived from predictive models must be effectively interpreted and implemented. These insights guide organizations to focus on high-impact areas for accident prevention and resource optimization. The integration of predictive analytics in OHS brings notable benefits, including enhanced decision-making, greater operational efficiency, cost savings, and improved compliance with safety standards. We examine applications of predictive analytics in OHS in Indian settings. India has the largest workforce in the world, and the predominance of it is in the informal sector - a sector largely unprotected by the already inadequate OHS laws. Ethical considerations, data privacy concerns, and the risk of overdependence on predictive models are discussed. We conclude with a discussion on the potential for predictive analytics to create a data-oriented, adaptive approach to OHS in India. We posit that, using predictive analytics, India can develop high safety standards while traversing the complexities of its workforce setting.