Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228921
Authors: 
Zielske, Malena
Held, Tobias
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:] 187-219
Abstract: 
Purpose: To meet rapidly changing requirements and increasing product complexity, a growing number of traditional companies and startups increases their agility by using agile methods. The logistics industry in particular is known to be a comparatively slow adapter to changes in general, but especially to new organizational innovations. The objective of the Delphi study conducted is to assess how traditional logistics companies and logistics startups use agile methods in their IT departments to deal with fast changing internal and external influences and how they respond to change. Methodology: A Delphi study will be conducted over several complementary rounds as an iterative expert judgement process. After the analysis of the first results, insights can be gained on the following points covering traditional logistics companies and logistics startups: a) The selection of agile methods and practices, b) the benefits that these methods and practices offer and c) the challenges of applying these methods and practices. Findings: The first results of the Delphi study show that traditional logistics companies as well as logistics startups use agile methods and practices to deal with a high degree of market uncertainty and change, and reveal what advantages and challenges they face. Originality: This originality of the Delphi study presented lies in its contribution to the largely unexplored area of agility in traditional logistics companies and logistics startups.
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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