A preliminary investigation on task-feature combinations for assigning maintenance tasks in Industry 5.0

The transition from Industry 4.0 (I4.0) to Industry 5.0 (I5.0) emphasises the need for human-centric industrial systems. In this context, maintenance operations represent a critical domain since they are inherently complex and directly related to the performance of production systems. According to the I5.0 perspective, a maintenance task allocation system should therefore be designed to optimise operator-task matches, minimising cognitive overloads and ensuring the operator's well-being. However, current literature lacks a standardised framework for identifying which operator's features significantly impact the proper accomplishment of maintenance tasks. This research therefore aims to understand the operator's features that most influence the proper accomplishment of maintenance tasks within the I4.0 context. To achieve the objective of the present work, a two-phase methodology was employed: firstly, two Systematic Literature Reviews allowed for identifying a sample of maintenance tasks and operator's features, and then a targeted survey was administered to professionals from companies with expertise in maintenance and I4.0 to understand which of the identified features are most relevant for the correct accomplishment of the analysed tasks. Although the study identified differences regarding the so-called cognitive and hybrid tasks, it generally demonstrated the importance of professional over individual features for the proper accomplishment of the identified maintenance tasks.