Big data in logistics-identifying potentials through literature, case study and expert interview analyses

Autor(en): Frehe, V.
Kleinschmidt, T.
Teuteberg, F. 
Herausgeber: Plodereder, E.
Grunske, L.
Ull, D.
Schneider, E.
Stichwörter: Human resource management; Personnel training, Case study analysis; Interdisciplinary research; Interview analysis; Management decisions; Management support; Operating companies; State of research; Systematic literature review, Big data
Erscheinungsdatum: 2014
Herausgeber: Gesellschaft fur Informatik (GI)
Journal: Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
Volumen: P-232
Startseite: 173
Seitenende: 186
Zusammenfassung: 
In this contribution, we elaborate the current state of research and practice of Big Data in the field of logistics by means of a systematic literature review, a case study analysis and expert interviews. Although all interviewees are from Germany, viable perspectives and opinions of practitioners from worldwide operating companies were gained. Based on the analyzed information and the identified knowledge gaps, we developed implications for practice and for research. We call for an advanced interdisciplinary research, which integrate practitioners as early as possible. Practitioners should identify how Big Data can improve management decision or daily business. Management support was identified as essential in Big Data projects, besides department staff should be integrated and a holistic approach should be followed. Therefore, appropriate training for project members or hiring of new staff is needed. Thus, this paper offers fundamental new insights in the field of Big Data useful for practitioners and researchers.
Beschreibung: 
Conference of 44. Jahrestagung der Gesellschaft fur Informatik INFORMATIK 2014 - Big Data - Komplexitat meistern - Big Data - Mastering Complexity: 44th Annual Meeting of the Society for Computer Science, INFORMATICS 2014 ; Conference Date: 22 September 2014 Through 26 September 2014; Conference Code:110425
ISBN: 9783885796268
ISSN: 16175468
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922563632&partnerID=40&md5=061a686f98d939c97bd9e08db25b6fd8

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