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Authors: Jens Grambau 1 ; Arno Hitzges 1 and Boris Otto 2

Affiliations: 1 Hochschule der Medien, Germany ; 2 TU Dortmund, Germany

ISBN: 978-989-758-298-1

ISSN: 2184-4992

Keyword(s): Predictive Maintenance, Predictive Analytics, Predictive Model, Social Media, Customer Service.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Data Communication Networking ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Internet of Things ; Knowledge Management ; Ontologies and the Semantic Web ; Sensor Networks ; Signal Processing ; Society, e-Business and e-Government ; Soft Computing ; Software Agents and Internet Computing ; Software and Architectures ; Strategic Decision Support Systems ; Telecommunications ; Web Information Systems and Technologies

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 solu tion 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. (More)

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Paper citation in several formats:
Grambau, J.; Hitzges, A. and Otto, B. (2018). 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. In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-298-1 ISSN 2184-4992, pages 223-230. DOI: 10.5220/0006669902230230

@conference{iceis18,
author={Jens Grambau. and Arno Hitzges. and Boris Otto.},
title={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},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2018},
pages={223-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006669902230230},
isbn={978-989-758-298-1},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - 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
SN - 978-989-758-298-1
IS - 2184-4992
AU - Grambau, J.
AU - Hitzges, A.
AU - Otto, B.
PY - 2018
SP - 223
EP - 230
DO - 10.5220/0006669902230230

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