Health and Safety in Engineering and Manufacturing
HEALTH & SAFETY A 2026 PERSPECTIVE
Health and Safety in Engineering and Manufacturing
Health and safety in engineering and manufacturing have always been critical priorities, but by 2026 they have evolved into far more strategic, technology-driven, and peoplecentred disciplines. As industries continue to embrace automation, digitalisation, and sustainability, the approach to protecting workers has shifted from reactive compliance to proactive risk prevention and continuous improvement. In 2026, health and safety are no longer viewed as standalone functions but as integral components of operational excellence and organisational resilience.
The Changing Risk Landscape
Engineering and manufacturing environments remain inherently hazardous, involving heavy machinery, high temperatures, chemicals, electrical systems, and complex processes. However, the nature of risk has changed. While traditional hazards such as manual handling injuries, machinery accidents, and exposure to harmful substances persist, new risks have emerged alongside technological advancements.
The increased use of robotics, autonomous systems, and artificial intelligence has reduced some physical risks but introduced others, including human – machine interaction failures, system malfunctions, and cybersecurity-related safety incidents. Additionally, the growing pace of production and demand for flexibility can increase cognitive load, fatigue, and stress among workers. In 2026, organisations recognise that health and safety must address both physical and psychological wellbeing to remain effective.
Technology as a Safety Enabler
One of the most significant developments in health and safety by
2026 is the widespread adoption of digital tools. Smart sensors, wearable devices, and the Industrial Internet of Things( IIoT) now play a key role in hazard identification and real-time monitoring. Wearables can track worker fatigue, posture, and exposure to noise or hazardous substances, alerting individuals and supervisors before incidents occur.
Predictive analytics and artificial intelligence are increasingly used to analyse historical incident data, near misses, and equipment performance. These systems can forecast potential failures or highrisk scenarios, enabling preventative maintenance and targeted safety interventions. Digital twins of factories and engineering systems
64 PECM Issue 78