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Predictive maintenance in the context of service - a state-of-the-art analysis of predictive models and the role of social media data in this context

Predictive maintenance, predictive analytics, predictive model, social media, customer service
 
: Grambau, Jens; Hitzges, Arno; Otto, Boris

:

Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
20th International Conference on Enterprise Information Systems, ICEIS 2018. Vol.2. Proceedings : March 21-24, 2018, in Funchal, Madeira, Portugal
SciTePress, 2018
ISBN: 978-989-758-298-1
pp.223-230
International Conference on Enterprise Information Systems (ICEIS) <20, 2018, Funchal>
English
Conference Paper
Fraunhofer ISST ()

Abstract
The aim of this study is to identify existing Predictive Maintenance methods in the context of service and the role of Social Media data in this context. With the help of a Systematic Literature Review eleven researches on notable Predictive Maintenance methods are identified and classified according to their focus, data sources, key challenges, and assets. It can be revealed that existing methods use different Prediction technologies and are mainly focused on industries with highly critical products. Existing methods provide value for B2B and B2C as well as products and services. Moreover, the majority is using heterogenous data that was generated automatically. However, it can be perceived that the consideration of Social Media data offers benefits for Prediction methods through identifying and using personal user data, the current usage is rare and only in the B2C sector recognizable. Thus, this research shows a gap in current literature as no universal Predictive Maintenance solution is available, that enables organizations to enhance their services by using the full potential of Social Media. Thus, future research needs to focus on the integration of Social Media data in Prediction methods for the B2C sector. With this it is deeply interesting how Social Media data has to be gathered and processed and if existing Predictive algorithms can be extended by Social Media data.

: http://publica.fraunhofer.de/documents/N-531343.html