Modelling risks in transition from Industry 4.0 to Industry 5.0

Industry 5.0 builds upon the advancements of Industry 4.0 by shifting its focus from pure automation to a more human-centric approach. This research aims to identify and model the key enablers contributing to the transitional shift from Industry 4.0 to Industry 5.0. The paper introduces a novel theory-driven framework, utilizing diverse methodologies such as Exploratory Factor Analysis (EFA), Fuzzy Set Theory (FST), Evidential Reasoning Algorithm (ERA), Expected Utility Theory (EUT), and Risk Profiling. The framework concentrates on evaluating the significance of these enablers during the transition and prioritizing them from multiple perspectives. The proposed model generates a continuum of scenarios illustrating the relative importance of resilience, sustainability, and the human-centric dimensions of Industry 5.0. The research identifies several risks associated with these enablers. A comprehensive risk profiling analysis is conducted to classify the risks according to predefined parameters. It is observed that, as the importance perspective changes, some risks exhibit robustness in their severity, while others are sensitive to slight variations in parameters. This study utilizes risk profiling based on dynamic changes in the perception of risk assessment. The risk profiling approach aids decision-makers in effectively planning actions to address the various risks associated with the transition from Industry 4.0 to Industry 5.0.