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    Item type:Publication, Access status: with files ,
    Digitization of the car: Impact on automotive logistics
    (epubli, 2021-12-01)
    Fruhner, Daniel
    ;
    Klingebiel, Katja
    Purpose: The digitization of the car is advancing in the four prominent areas called CASE (Connected, Autonomous, Shared & Services, Electric). The respective changes to the product structure will further increase the complexity of automotive logistics. This paper has methodologically clustered these changes and identified the impacts on logistics. Methodology: Via a systematic literature analysis, the automotive product structure has been categorized into six main clusters and subclusters. Subsequently, the anticipated changes in the product structure triggered by digitalization have been classified. In the last step, the resulting impacts and necessary developments for logistics have been deduced. Findings: In the course of the digitization of the car, only very few systems and modules remain unchanged. Adaptations in the product structure can be expected to accelerate in the future. Conceptual solutions for automotive logistics are necessary. First approaches, as well as remaining gaps, have been identified. Originality: Digitalization and technological trends pose new challenges on logistics not yet met by scientific literature, though practice offers first conceptual ideas. This paper is the first to methodologically and holistically identify the implications of the digitalization of the car on logistics, thus providing the basis for assessing conceptional solutions.
    Publicationtype: Conference Paper
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    Item type:Publication, Access status: with files ,
    Smart risk analytics design for proactive early warning
    (epubli, 2019-09-26)
    Diedrich, Katharina
    ;
    Klingebiel, Katja
    Purpose: Automobile manufacturers are highly dependent on supply chain performance which is endangered by risks. They are not yet able to proactively manage these risks, often requiring reactive bottleneck management. A proactive and digitalized early warning method is needed. Methodology: The publication provides methodological-empirical contribution to proactive early warning resulting in a smart risk management approach. The methodological approach is carried out according to the design science research approach. Findings: The developed smart risk management enables an automated, objective and real-time ex-ante-assessment of supply chain risks in to secure the supply of the automobile manufacturer. Smart risk analytics based on artificial intelligence is shown with its suitability for proactive early warning using the example of inaccurate demand planning. Originality: The analytical approach provides insights into the flexibility of supply chains under risk and the impact over time, which is applied in the proactive early warning design. Artificial intelligence is applied to predict and assess supply chain risk events.
    Publicationtype: Conference Paper
      337  510