Predictive maintenance as an internet of things enabled business model: A taxonomy
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RESEARCH PAPER
Predictive maintenance as an internet of things enabled business model: A taxonomy Jens Passlick 1
&
Sonja Dreyer 1 & Daniel Olivotti 1 & Lukas Grützner 1 & Dennis Eilers 2 & Michael H. Breitner 1
Received: 14 March 2019 / Accepted: 7 September 2020 # The Author(s) 2020
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 JEL classification L86
Introduction The introduction of the Internet of Things (IoT), in terms of both theory and practice, is currently the subject of intense discussions (Whitmore et al. 2015). The IoT has enormous potential in both the private and industrial environments (Manyika et al. 2015). The term Industrial Internet of Things (IIoT) is used for such applications. Prior research discusses the characteristics of business models that successfully use the
This article is part of the Topical Collection on Internet of Things for Electronic Markets Responsible Editors: Maria Madlberger and Martin Smits * Jens Passlick [email protected] Dennis Eilers [email protected] 1
Information System Institute, Leibniz Universität Hannover, Königsworther Platz 1, 30167 Hannover, Germany
2
DeepCorr GmbH, Göttinger Hof 7, 30453 Hannover, Germany
possibilities offered by IIoT (Herterich et al. 2016). Previous research on the IoT environment shows that understanding the business models of company partners is important for longterm success (Dijkman et al. 2015). Digital business models in general are analyzed in prior research (e.g., Hartmann et al. 2016; Bock and Wiener 2017; Rizk et al. 2018). However, the more general taxonomies used for digital business models include aspects that are not relevant for every company with an IoT or IIoT business model (Bock and Wiener 2017). Particularly in the context of Industry 4.0, in which IIoT is a major component, a more concrete consideration of the changes that have been made to business models is important. In the area of value creation, value offer, and value capture, specific aspects m
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