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Gast, Johannes
Wehrle, Rebecca
Wiens, Marcus
Schultmann, Frank
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[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:] 247-278
Purpose: Transport infrastructures form the backbone of today's interconnected real economy. Interruptions in the availability of these critical infrastructures occur, among other things, due to maintenance operations. Since companies and Supply Chains are dependent on planning security, an adequate notification time in ad-vance of such interruptions is required. Otherwise, insufficient notification time limit available mitigation strategies. Methodology: First, the authors conduct a workshop concept to obtain expert knowledge from stakeholders to identify critical thresholds of notification times, which affect maintenance and logistics operations in inland waterway transport. Second, this research analyses the notification time of closures on an exemplary real-world network of inland navigation. Findings: The research reveals a high impact of notification times on logistics oper-ations and determines the planning reliability for all parties involved. Data analysis found that the notification time for the majority of the considered closures is below the identified critical threshold. Efficient planning must address this as they pose threat to Supply Chains operations. Originality: This research is innovative as there is little analysis on inland waterways, even though there exist accessible historical data. This research contributes to this account by linking critical infrastructure, expert knowledge, and supply chain oper-ations. Quantitative methods extend the base of qualitative knowledge gained from interdisciplinary research. Stakeholders can account for notification time in their risk mitigation strategies.
Industry 4.0
Supply Chain Management
Artificial Intelligence
Data Science
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Conference Paper

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