Authors:
Milan Koch
1
;
Hao Wang
2
;
Robert Bürgel
1
and
Thomas Bäck
2
Affiliations:
1
BMW Group, Munich, Germany
;
2
Leiden University, Leiden, The Netherlands
Keyword(s):
Data-driven Service, Machine Learning, Damage Prediction, Connected Car, Vehicle Network, Online Learning.
Abstract:
Numerous recent studies show the prosperous future of data-driven business models. Some key challenges have to be dealt with when moving towards the development of data-driven car services. In this paper, a new data-driven customer service is proposed for the settlement of vehicle low speed accidents. Beyond that, we present a more general approach towards the development of data-driven car services. We point out its main challenges and suggest a method for developing new customer-oriented data-driven services. This approach illustrates key points in developing a practical service, from a technical and business related perspective. Such data-driven services are developed mostly on a small number of initial test data, which results often in a limited prediction performance. Therefore, based on an optimized CRISP-DM approach, we propose a methodology for developing initial prediction models with limited test data and stabilizing the models with newly gained data after deployment by onl
ine learning. On-board and off-board services are discussed with the result that especially off-board running services offer a large potential for future data-driven business models in a digital ecosystem. The flexibility of such an ecosystem depends on the degree of the integration of the vehicle in the ecosystem - in other words, the car needs to be enabled to deliver data on demand according to GDPR and to any applicable regional law and in cooperation with the customer. The presented method, together with the ecosystem, enables fast developments of various data-driven services.
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