Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1472
This item is licensed with a CreativeCommons licence by-sa/4.0
Publisher URL: https://www.epubli.de/shop/buch/2000000069144
Title: Data source taxonomy for supply network structure visibility
Language: English
Authors: Zrenner, Johannes 
Hassan, Ahmad Pajam 
Otto, Boris 
Marx Gómez, Jorge Carlos 
Editors: Kersten, Wolfgang 
Blecker, Thorsten 
Ringle, Christian M.  
Keywords: data source taxonomy;supply chain visibility;supply network structure;design science research
Issue Date: Oct-2017
Publisher: epubli
Source: Digitalization in supply chain management and logistics
Volume number: 23
Journal or Series Name: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Conference: Hamburg International Conference of Logistics (HICL) 2017 
Abstract (english): The supply network structure of manufacturers is complex and non-transparent. In order to achieve a higher visibility and consequently increase the performance, the existing lack of data has to be closed. This paper answers the questions, how to identify, describe and compare suitable data sources for an end-to-end visibility. Following the design science research process, two artifacts are developed based on conceptual-to-empirical approaches. The initial conceptualizations result from literature reviews. The conceptual representation of supply network structure data sources clarifies the relevant data entities and attributes. It supports the identification process of relevant data sources. The data source taxonomy (i.e. classification scheme) describes data sources using fourteen dimensions and up to four potential characteristics. It assists a standardized description. Both artifacts are demonstrated in case studies with German automotive Original Equipment Manufacturers. The findings add to the knowledge base of supply network visibility with a focus on the network structure. A large part of the existing literature about supply chain visibility is too vague on the data perspective. Therefore, this paper closes an important gap regarding the supply chain digitalization by introducing two applicable results, which enable a new course of action for practitioners and researchers.
URI: http://tubdok.tub.tuhh.de/handle/11420/1475
DOI: 10.15480/882.1472
ISBN: 978-3-7450-4328-0
ISSN: 2365-5070
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
Appears in Collections:Publications (tub.dok)

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