Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228954
Authors: 
Weigell, Jürgen
Ilin, Igor
Maydanova, Svetlana
Dubgorn, Alissa
Jahn, Carlos
Kersten, Wolfgang
Year of Publication: 
2020
Citation: 
[Editor:] Jahn, Carlos [Editor:] Kersten, Wolfgang [Editor:] Ringle, Christian M. [Title:] Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 30 [ISBN:] 978-3-7531-2347-9 [Year:] 2020 [Pages:] 309-336
Abstract: 
Purpose: The sustainable development of Arctic maritime supply chains requires an effective balance between economic growth, environmental protection and social relations. Existing Arctic development projects are often distinguished by single-criterion decision making - economic growth to the detriment of other components. The paper aims to analyze existing approaches and practices of Arctic supply chains development from a sustainable development viewpoint. Methodology: Best practices of other industries are identified by a thoroughly literature research of relevant publications and developing a model for sustainability issues in Arctic maritime supply chains. The model compromises relevant indicators in regard to economic, social and environmental performance for the Arctic region. Findings: The result of this paper will be a thoroughly overview over current sustainability issues in the Arctic framework (economic, social and environmental). Findings for example will be how companies adjust to the ban of using heavy Sulphur fuel in the Arctic or social and economic changes in remote areas due to more shipping in the Arctic. Originality: The originality of the research is defined by the sustainability viewpoint on the problem: the combination of social, economic as well as environmental issues is the main focus of this paper especially with a focus on remote areas of the Arctic.
Subjects: 
Logistics
Industry 4.0
Supply Chain Management
Sustainability
City Logistics
Maritime Logistics
Data Science
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-sa/4.0/
Document Type: 
Conference Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.