Details
| Original language | English |
|---|---|
| Pages (from-to) | 67-87 |
| Number of pages | 21 |
| Journal | Electronic markets |
| Volume | 31 |
| Issue number | 1 |
| Early online date | 19 Oct 2020 |
| Publication status | Published - Mar 2021 |
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
- Business models, Cluster analysis, IoT, Predictive maintenance, Taxonomy
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Business and International Management
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
- Computer Science(all)
- Computer Science Applications
- Business, Management and Accounting(all)
- Marketing
- Business, Management and Accounting(all)
- Management of Technology and Innovation
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In: Electronic markets, Vol. 31, No. 1, 03.2021, p. 67-87.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predictive maintenance as an internet of things enabled business model
T2 - A taxonomy
AU - Passlick, Jens
AU - Dreyer, Sonja
AU - Olivotti, Daniel
AU - Grützner, Lukas
AU - Eilers, Dennis
AU - Breitner, Michael H.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - Business models
KW - Cluster analysis
KW - IoT
KW - Predictive maintenance
KW - Taxonomy
UR - http://www.scopus.com/inward/record.url?scp=85092727586&partnerID=8YFLogxK
U2 - 10.1007/s12525-020-00440-5
DO - 10.1007/s12525-020-00440-5
M3 - Article
AN - SCOPUS:85092727586
VL - 31
SP - 67
EP - 87
JO - Electronic markets
JF - Electronic markets
SN - 1019-6781
IS - 1
ER -