Data source taxonomy for supply network structure visibility - Johannes Zrenner; Ahmad Pajam Hassan; Boris Otto; Jorge Carlos Marx Gómez (Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 23, 2017)
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.
urn:nbn:de:gbv:830-88217690; 10419/209192; https://econpapers.repec.org/RePEc:zbw:hiclpr:23
Keywords: 650; data source taxonomy; supply chain visibility; supply network structure; design science research