COMPETENCY PROFILES FOR THE USE OF PREDICTIVE MAINTENANCE IN SMES

Predictive Maintenance is a key element of data-driven maintenance strategies in Industry 4.0. It promises improved asset availability and operational efficiency. In addition to data analytics and machine learning, simulation-based assessment and digital representations of production systems increasingly complement Predictive Maintenance by enabling the evaluation of maintenance decisions and failure risks before physical intervention. However, small and medium-sized enterprises (SMEs) often face challenges in realizing these benefits due to insufficient employee competencies across all domains. As a prerequisite, a clear understanding of competency gaps that hinder employees to use relevant key technologies such as artificial intelligence and data analytics is needed. This paper presents competency profiles that define relevant competencies for the use of simulation-based Predictive Maintenance and map these competencies to relevant roles that need them. The study is based on a systematic literature review about competencies for digitalization processes in industry focusing on maintenance and, more specifically, data technologies in maintenance. The competency profile is validated through a Delphi-Study with industry representatives assessing the identified competency levels. The results support a more holistic understanding of Predictive Maintenance and provide guidance for targeted competency development in SMEs.