Schnasse, Felix Menzefricke, Jörn Steffen Gabriel, Stefan Hobscheidt, Daniela Parlings, Matthias Kühn, Arno Dumitrescu, Roman
Year of Publication:
[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:] 625-660
Purpose: Industry 4.0 provides significant potentials for companies. Despite the promising opportunities, companies, especially SME, are still hesitant to implement new technologies. The main reasons are far-reaching changes with respect to the socio-technical dimensions causing risks that are difficult to assess. This research provides a methodology to identify these socio-technical changes for Industry 4.0 use cases. Methodology: Based on the three Design Science Research Cycles, a procedure and the corresponding methods for identifying socio-technical changes and risks during the introduction of Industry 4.0 will be designed. Findings: The developed tool enables the derivation of use-case specific changes and risks in the socio-technical dimensions of human, technology and organization. These interactions have to be considered when introducing Industry 4.0 use cases in order to ensure a promising usage. In addition, the need for further research in the field of socio-technical risk management is identified. Originality: Classical approaches do not address socio-technical interdependencies during the implementation of Industry 4.0 solutions. To bridge this gap, this methodological approach combines risk management and the concept of socio-technical system design.
Logistics Industry 4.0 Digitalization Innovation Supply Chain Management Artificial Intelligence Data Science