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An Ontology-Based Model for Training Evaluation and Skill Classification in an Industry 4.0 Environment

Abstract : The recent advancements of manufacturing towards the Industry 4.0 paradigm should be supported by the effective training of industrial workers in order to align their skills to the new requirements of companies. Therefore, the evaluation of the training is becoming in this context increasingly important, given also the possibility of exploiting a huge amount of data from the shop floor about the workers’ activities. These data – indeed – can be properly collected and analysed so as to provide real-time indications about the workers’ performances and an evolving classification of their skills. In order to pursue this objective, a solution can be represented by the integration of semantic technologies with training evaluation models. For this reason, the paper aims at presenting a Training Data Evaluation Tool (TDET), which is based on the integration of a Training Evaluation Ontology (TEO) with a Training Analytics Model (TAM) for the definition of the skill levels of the workers. The main components and features of the TDET are provided in order to show its suitability towards the collection of data from the shop floor and their subsequent elaboration in summary indicators to be used by the management of the company. Finally, the implications and next steps of the research are discussed.
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Stefano Perini, Damiano Arena, Dimitris Kiritsis, Marco Taisch. An Ontology-Based Model for Training Evaluation and Skill Classification in an Industry 4.0 Environment. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2017, Hamburg, Germany. pp.314-321, ⟨10.1007/978-3-319-66923-6_37⟩. ⟨hal-01666223⟩

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