Risk assessment for modern industry

Abstract

Modern industry is undergoing rapid transformation driven by various factors and challenges like digitalization, climate change and systems interdependencies, necessitating a paradigm shift in risk assessment. Traditional methods, often static and siloed, struggle to address dynamic risks such as aging infrastructures, cyber-physical interdependencies and climate-induced hazards. This paper explores cutting-edge approaches to dynamic, data-driven risk assessment, integrating advanced simulation, artificial intelligence and real-time monitoring to enhance reliability and resilience in industrial systems, and shares some reflections and studies on them with the purpose of stimulating further development. Through case studies spanning manufacturing, energy and critical infrastructures, we demonstrate how modern methodologies, from digital twins to probabilistic climate impact models, enable proactive risk management. Key themes include: i ) the role of real-time adaptability of safety-critical systems, ii ) computational advances for rare-event and cascading-risk analysis, and iii ) the integration of environmental stressors into long-term risk analysis frameworks. We conclude with an analysis of possible research directions to bridge gaps between technology, regulation and scalability, offering a roadmap for next-generation risk assessment methods.

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