Work and Competencies in Industry 4.0

© industrieblick – Fotolia/Fraunhofer IAO

Industrial production faces a technological development which is going to merge the real and virtual worlds into an Internet of Things, Data and Services within the concept of the »Smart Factory«. The introduction of cyber-physical systems (CPS) in industrial production promises a sustainable increase in productivity and flexibility of production enterprises. This development is promoted in Germany under the concept of »Industrie 4.0«.

To date, Industry 4.0 was predominantly considered to be a technological matter. However, the development also changes the future nature of production work and calls for different competencies of the workforce in production. A predictive human resources policy is only possible if we succeed in identifying these competency requirements in good time. Consistent competency development by near-the-job advanced training will be of decisive importance as a component of such a human resources policy.

An important objective for companies is to ensure stability and reliability of intelligent production. This depends substantially on the employees' competency to secure the migration steps. The systems must be controlled reliably at any point of time and redundancy and fallback systems must be maintained. If the new tasks cannot be mastered reliably from the employees' perspective, this will also be a risk for mastering the intelligent systems from the enterprise's perspective.

The new technologies of intelligent production involve a fundamental change in the division of work between humans and machines. This will bring about substantial consequences for the tasks, quality of work and competencies of the employees. Since there is still a lack of knowledge about the characteristics of the new working processes so far, potential barriers to implementation and acceptance represent a substantial risk for enterprises aiming at introducing intelligent technologies.

Range of Services

  • Identifying future competencies at an early stage
  • Arbeitsprozesse modellieren
  • Analyzing risks in mastering systems and tasks