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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jens Passlick & Sonja Dreyer & Daniel Olivotti & Lukas Grützner & Dennis Eilers & Michael H. Breitner, 2021. "Predictive maintenance as an internet of things enabled business model: A taxonomy," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 67-87, March.
    • Handle: RePEc:spr:elmark:v:31:y:2021:i:1:d:10.1007_s12525-020-00440-5
    DOI: 10.1007/s12525-020-00440-5
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    References listed on IDEAS

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    Cited by:

    1. Gunasekaran Manogaran & Naveen Chilamkurti & Ching-Hsien Hsu, 2021. "Internet of Things for Electronic Markets," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 13-15, March.
    2. Laurin Arnold & Jan Jöhnk & Florian Vogt & Nils Urbach, 2022. "IIoT platforms’ architectural features – a taxonomy and five prevalent archetypes," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 927-944, June.
    3. Estelle Duparc & Frederik Möller & Ilka Jussen & Maleen Stachon & Sükran Algac & Boris Otto, 2022. "Archetypes of open-source business models," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 727-745, June.
    4. Tessmann, R. & Elbert, R., 2022. "Multi sided platforms in competitive B2B networks with varying governmental influence – a taxonomy of Port and Cargo Community System business models," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 132320, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Frederik Möller & Maleen Stachon & Can Azkan & Thorsten Schoormann & Boris Otto, 2022. "Designing business model taxonomies – synthesis and guidance from information systems research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 701-726, June.
    6. Ruben Tessmann & Ralf Elbert, 2022. "Multi-sided platforms in competitive B2B networks with varying governmental influence – a taxonomy of Port and Cargo Community System business models," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 829-872, June.

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    More about this item

    Keywords

    Taxonomy; Predictive maintenance; Business models; IoT; Cluster analysis;
    All these keywords.

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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