Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228948
Authors: 
Schreiber, Lucas
Jarmer, Jan-Philipp
Kamphues, Josef
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:] 129-155
Abstract: 
Purpose: Due to changing customer requirements and political regulations more and more companies strive to optimize their energy efficiency in regards to products and processes. The optimization of processes within supply chain design (SCD) is one lever in this regard. Since required data is often not available, this paper elaborates how data can be generated on a suitable level of aggregation. Methodology: In order to highlight the research gap, established energy measurement procedures as well as existing energy databases for procurement, production and transportation are analyzed and compared with data requirements for SCD tasks. Based on these findings, necessary methods and procedures for data preparation are presented. Findings: Firstly, it is shown that addressing energy efficiency within SCD leads to new challenges in regards to data availability and preparation. Secondly, this paper elaborates the requirements for necessary data usable in the context of SCD. The findings are the basis for a comprehensive approach combining collection, aggregation and clustering of energy and product related data. Originality: This paper works out the gap between usually available energy related information and the requirements of SCD. Since key conditions for optimizing energy efficiency are defined in strategic planning, the findings create a necessary prerequisite for realizing energy-optimized supply chains on a large scale in the future.
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

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