An Assignment Model for High-Cognitive-Workload Maintenance Activities in Industry 5.0

Industry 5.0 paradigm emphasises human-centricity, sustainability, and resilience in production systems. If, on the one hand, Industry 4.0 (I4.0) promoted production efficiency and quality through the development and implementation of advanced technologies, on the other hand, this paradigm has main limitations due to the limited consideration of industrial sustainability and workers’ welfare. In the I4.0 context, the operator may face cognitive overload due to the inherent complexity of ordinary activities. In this scenario, maintenance operations are of utmost relevance. They are indeed critical in any production context, as they are not value-adding but directly determine factors such as the safety and performance of industrial systems. In the context of I4.0, a paradigm known as Maintenance 4.0 has developed, which involves adopting advanced technologies for maintenance activities. While this paradigm allowed for advantages such as the implementation of predictive maintenance policies, it has also complicated ordinary activities, especially from a cognitive point of view. To this concern, the objective of the present work consists of a task assignment model that supports the company in identifying the proper operator/s to accomplish maintenance tasks with high cognitive workloads. Identifying the proper operator for each task led to reducing the probability of accidents, increasing human well-being, and improving the reliability of the maintained assets. A numerical application of the proposed model proved its effectiveness in identifying the operator to be assigned a specific maintenance activity based on its skills and considering the cognitive workload of previous maintenance tasks assigned to the same operator.