Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228943
Authors: 
Stradner, Sascha
Brunner, Uwe
Year of Publication: 
2020
Citation: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Title:] Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29 [ISBN:] 978-3-7531-2346-2 [Year:] 2020 [Pages:] 911-938
Abstract: 
Purpose: Digitalization and its technologies enables especially logistics startups to enter the market with low barriers. Well-established enterprises potentially suffer from this development by overlooking the ongoing technological leap. Therefore, the paper includes a research to point out current dynamic of the market and potential risks and chances for current enterprises. Methodology: For this purpose, an overview of the market development and the fundamental re-thinking of logistics business models based on a literature research is given and reflected. It focuses on technologies, entry barriers for startups and re-thinking of current business models. Subsequently, the impact for current enterprises should be shown up. Findings: The research points out the dynamic of the business and the risks for well-established enterprises on the market. Startups accelerate the market dynamic by introducing new technologies on the market, which are seen either as risk or chance for enterprises. Organisational inflexibility could lead to a replacement of market leaders. Originality: Using modern technology business models can be absolutely re-defined. While an abundance of startups could mostly cover performance spectrum of established enterprises in a new way, the latter suffers from rigid company structure. By conducting the research, the current state and risks and chances for current enterprises should be outlined.
Subjects: 
Logistics
Industry 4.0
Digitalization
Innovation
Supply Chain Management
Artificial Intelligence
Data Science
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-sa/4.0/
Document Type: 
Conference Paper

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