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Predictive maintenance as an internet of things enabled business model: A taxonomy

Jens Passlick (), Sonja Dreyer, Daniel Olivotti, Lukas Grützner, Dennis Eilers () and Michael H. Breitner
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Jens Passlick: Leibniz Universität Hannover
Sonja Dreyer: Leibniz Universität Hannover
Daniel Olivotti: Leibniz Universität Hannover
Lukas Grützner: Leibniz Universität Hannover
Dennis Eilers: DeepCorr GmbH
Michael H. Breitner: Leibniz Universität Hannover

Electronic Markets, 2021, vol. 31, issue 1, No 7, 67-87

Abstract: Abstract Predictive maintenance (PdM) is an important application of the Internet of Things (IoT) discussed in many companies, especially in the manufacturing industry. PdM uses data, usually sensor data, to optimize maintenance activities. We develop a taxonomy to classify PdM business models that enables a comparison and analysis of such models. We use our taxonomy to classify the business models of 113 companies. Based on this classification, we identify six archetypes using cluster analysis and discuss the results. The “hardware development”, “analytics provider”, and “all-in-one” archetypes are the most frequently represented in the study sample. For cluster analysis, we use a visualization technique that involves an autoencoder. The results of our analysis will help practitioners assess their own business models and those of other companies. Business models can be better differentiated by considering the different levels of IoT architecture, which is also an important implication for further research.

Keywords: Taxonomy; Predictive maintenance; Business models; IoT; Cluster analysis (search for similar items in EconPapers)
JEL-codes: L86 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12525-020-00440-5

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