Development of an analytical model for operator-task matching in maintenance 5.0

The evolution towards Industry 5.0 (I5.0) has increased the complexity of industrial environments, making effective operator-task matching crucial in maintenance activities. This study presents an analytical model that integrates the identification of key features for operators and tasks, validated by industry experts and operator self-assessments. The model was tested through a lab case study involving 22 participants, including both experts and non-experts, using completion time (Tc), the number of restart attempts (Rn), and reporting accuracy (Cr) as performance indicators. A global key indicator (GKI) was estimated by normalising the sum of the three indicators. The GKI was then estimated for each operator to assess the model’s reliability in predicting the correct operator-task matching in accomplishing maintenance activity within I5.0 work environments. The results indicate that although the model is more accurate in predicting the performance of less experienced operators, its reliability is strongly influenced by the self-assessment test, which, in some cases, leads to overestimating the actual performance of the operators. However, the model has the potential to support adaptive operator-task matching in Industry 5.0 contexts.