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Towards Designing a User-centric Decision Support System for Predictive Maintenance in SMEs

Author:
Kellner, Domenic [DBLP] ;
Lowin, Maximilian [DBLP] ;
von Zahn, Moritz [DBLP] ;
Chen, Johannes [DBLP]
Abstract
In manufacturing, small and medium-sized enterprises (SMEs) face global competition. In the field of predictive maintenance (PdM), artificial intelligence (AI) helps to prevent machine failures and has the potential to significantly reduce costs and increase process efficiency. Even though PdM has several benefits, it also entails considerable challenges for SMEs, especially when it comes to user interactions. In this short paper, we harness the design science methodology and discuss several problems regarding user interactions with predictive maintenance applications. We incorporate two different literature streams, namely, predictive maintenance and decision support systems. Finally, we present necessary design requirements, principles, features, and propose a research design to further develop and evaluate a user-centric PdM decision support system. Thereby, we contribute to making AI tangible in SMEs.
  • Citation
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Kellner, D., Lowin, M., von Zahn, M. & Chen, J., (2021). Towards Designing a User-centric Decision Support System for Predictive Maintenance in SMEs. In: , . (Hrsg.), INFORMATIK 2021. Gesellschaft für Informatik, Bonn. (S. 1255-1260). DOI: 10.18420/informatik2021-104
@inproceedings{mci/Kellner2021,
author = {Kellner, Domenic AND Lowin, Maximilian AND von Zahn, Moritz AND Chen, Johannes},
title = {Towards Designing a User-centric Decision Support System for Predictive Maintenance in SMEs},
booktitle = {INFORMATIK 2021},
year = {2021},
editor = {} ,
pages = { 1255-1260 } ,
doi = { 10.18420/informatik2021-104 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
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More Info

DOI: 10.18420/informatik2021-104
ISBN: 978-3-88579-708-1
ISSN: 1617-5468
xmlui.MetaDataDisplay.field.date: 2021
Language: en (en)

Keywords

  • predictive maintenance
  • machine learning
  • decision support systems
  • design science research
  • explainable artificial intelligence
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  • P314 - INFORMATIK 2021 - Computer Science & Sustainability [168]

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Diese Digital Library basiert auf DSpace.

 

 


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Gesellschaft für Informatik e.V. (GI), Kontakt: Geschäftsstelle der GI
Diese Digital Library basiert auf DSpace.