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Automotive supply-chain requirements for a time-critical knowledge management

: Tietze, Ann-Carina; Cirullies, Jan; Otto, Boris

Kersten, Wolfgang (Ed./Hrsg.) ; TU Hamburg-Harburg, Institut für Logistik und Unternehmensführung:
Digitalization in supply chain management and logistics : Smart and digital solutions for an industry 4.0 environment; Hamburg International Conference of Logistics 2017, Hamburg
Berlin: epubli, 2017 (Proceedings of the Hamburg International Conference of Logistics (HICL) 23)
ISBN: 978-3-7450-4328-0
ISBN: 3-7450-4328-6
Hamburg International Conference of Logistics (HICL) <11, 2017, Hamburg>
Fraunhofer ISST ()
time-critical knowledge management; bottleneck management; Automotive industry requirement; case study research

Transforming increasingly growing data volumes into knowledge and improving its usage requires knowledge management models (KMM). KMM structures the workflow for decision taking based on knowledge. Industry-suitable requirements for a KMM, in particular for automotive supply chains (SC) and supply-critical bottlenecks, are not raised, especially concerning the crucial parameter of timecriticality. As none of the investigated models suits time-related specifications, requirements for time-critical knowledge management (KM) are derived from former case studies (CS) in the manufacturing automotive industry by literature research. These requirements will be used to evaluate existing KMM proposed in literature. Requirements for a KMM, which supports the manufacturing automotive industry (AI) in time-critical cases, are collected from practice by means of group discussions, generalised, abstracted and verified such as real-time capability, availability and accessibility, incentives for knowledge-sharing or intuitive handling. In particular, it addresses the application case of a supply-critical bottleneck in the inbound logistics. This results in rethinking of knowledge as a fundamental, time-critical resource for the reduction of supply risks. Currently, there are neither KMMs that involve time-criticality supporting industry to deal with increasing data and knowledge volumes nor precise requirements for time-critical KM in case of a supply-bottleneck in the AI. The importance of time-critical knowledge in contrast to mere data is shown. Finally, time-criticality is highlighted by showing its value to minimise production-breakdown-risks. The aim is to raise awareness about the need for changes in existing processes in the AI and to define the scope of scientific research needs.