Veröffentlichungen

Text-Mining and Gamification for the Qualification of Service Technicians in the Maintenance Industry of Offshore Wind Energy

Thies BEINKE, Annabell SCHAMANN, Michael FREITAG, Klaas FELDMANN, Matthias BRANDT

In: International Journal of e-Navigation and Maritime Economy, Volume 6, April 2017, Pages 44–52, https://doi.org/10.1016/j.enavi.2017.05.006.

And In: Park, Gyei-Kark; Kim, S. June (eds.): Proceedings of International Conference on Advanced Intelligent Maritime Safety and Technology. International Associartion of e-navigation and Ocean Economy, Mokpo, Korea, 2016, pp. 137-146

Abstract: The competition of maintenance services in the offshore wind industry is continually increasing. The quality of the services acts as the distinguishing feature in the industry. Furthermore, there are public standards, which lead to the permanent necessity to offer further education and training programs for employees. To meet the requirements for further training in the specific field of application within the offshore wind industry, a gamified e-learning application has been developed and is introduced in this paper. It consists of a complete solution, which contains the automated analysis of service protocols to identify qualification needs, the involvement of service technicians in the generation of learning materials, the preparation, transmission as well as the further development of those materials in accordance with the principles of e-learning. Finally, the solution contains a gamified mobile application for qualification, which is designed to meet the individual learning needs of the service technicians. This concept paper follows a problem-centred approach. Based on the current state of technology and research, the problem and motivation are identified and the urgency is verified. Furthermore, a detailed specification of the solution and a first implementation approach is presented.

Anwendungsspezifische Auswahl von Text-Mining-Methoden – Identifikation von Qualifizierungsbedarfen für Servicetechniker

Thies BEINKE, Michael FREITAG, Nico NIENABER, Annabell SCHAMANN, Klaas FELDMANN

In: Industrie 4.0 Management, 33(2017)4, S. 12-16.

Die computergestützte Analyse großer Datenmengen verspricht für unterschiedlichste Anwendungsfälle und Bedarfe erheblichen Nutzen. Ziel dieses Beitrags ist die Entwicklung eines Ansatzes zur Ermittlung geeigneter Text-Mining-Methoden ausgehend vom spezifischen Anwendungsfall. Dieser Ansatz umfasst zwei Phasen, welche den Analytic Hierarchy Process sowie den Text-Mining-Prozess einbeziehen. Für den Anwendungsfall der Identifikation von Qualifizierungsbedarfen von Servicetechnikern wird der Ansatz beispielhaft durchlaufen. Das Ergebnis dieser beispielhaften Betrachtung verdeutlicht, dass die jeweiligen Methoden des Text-Minings bzw. ihre Mehrwerte nicht nur von dem Anwendungsfall, sondern auch stark von der jeweiligen Zielstellung abhängen.