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Data-driven business models in logistics: A taxonomy of optimization and visibility services

 
: Möller, F.; Stachon, M.; Hoffmann, C.; Bauhaus, H.; Otto, B.

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Hawaii International Conference on System Sciences 2020 : 06-10 January 2020, Honolulu, Hawaii; Proceedings of the 53rd Annual Hawaii International Conference on System Sciences
Honolulu: ScholarSpace, 2020
ISBN: 978-0-9981331-3-3
S.5379-5388
Hawaii International Conference on System Sciences (HICSS) <53, 2020, Honolulu/Hawaii>
Englisch
Konferenzbeitrag
Fraunhofer ISST ()

Abstract
The nature of business conduct is changing due to emerging digital technologies and the ever-increasing role of data as a critical resource. Traditional industry branches such as logistics need to adapt accordingly to keep up with change through digitization and to design adequate business models using data. The present article focuses on investigating the anatomy of these data-driven business models in the logistics sector. In order to achieve this goal, the study develops a taxonomy of data-driven business models in logistics. Start-ups serve as the frame of reference, as they are particularly suitable for deriving explicitly novel and vital business models. The study focuses on two particular types of data-driven business models, namely those offering visibility or optimization services in logistics. The goal of the taxonomy is to uncover the structural composition of such business models and to make the results usable as a morphology for innovation.

: http://publica.fraunhofer.de/dokumente/N-639692.html