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Predictive Maintenance Information Systems: The Underlying Conditions and Technological Aspects

Predictive Maintenance Information Systems: The Underlying Conditions and Technological Aspects

Michael Möhring, Rainer Schmidt, Barbara Keller, Kurt Sandkuhl, Alfred Zimmermann
Copyright: © 2020 |Volume: 16 |Issue: 2 |Pages: 16
ISSN: 1548-1115|EISSN: 1548-1123|EISBN13: 9781799805021|DOI: 10.4018/IJEIS.2020040102
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MLA

Möhring, Michael, et al. "Predictive Maintenance Information Systems: The Underlying Conditions and Technological Aspects." IJEIS vol.16, no.2 2020: pp.22-37. http://doi.org/10.4018/IJEIS.2020040102

APA

Möhring, M., Schmidt, R., Keller, B., Sandkuhl, K., & Zimmermann, A. (2020). Predictive Maintenance Information Systems: The Underlying Conditions and Technological Aspects. International Journal of Enterprise Information Systems (IJEIS), 16(2), 22-37. http://doi.org/10.4018/IJEIS.2020040102

Chicago

Möhring, Michael, et al. "Predictive Maintenance Information Systems: The Underlying Conditions and Technological Aspects," International Journal of Enterprise Information Systems (IJEIS) 16, no.2: 22-37. http://doi.org/10.4018/IJEIS.2020040102

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Abstract

Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.

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