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Influence of patterns and data-analytics on production logistics

In: Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 23

Author

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  • Kormann, Benjamin
  • Altendorfer-Kaiser, Susanne

Abstract

The flow of information is an essential part of Industry 4.0, as more reliable process data is available and subsequent hardware changes provide for processing power to enable large-scale data analysis. Due to the fact that most data analytics and big data frameworks presume that Data-Mining- and Data-Analytics-Activities are conducted in form of projects, this paper focuses on the integration of data analytics and data mining into operational processes and the resulting consequences of the organization. Therefore a framework to implement data analytics workflows in production logistics to improve decision-making and processes is presented. By integrating data analytics workflows in production logistics applying the presented framework, more resources can be devoted to proactively discover and counteract possible bottlenecks or constrictions instead of resorting to firefighting and taskforce-activities. The methodology to derive such framework consists of developing and implementing data analytics use-cases along the supply chain in production logistics according to current big-data and data-analytics frameworks in cooperation with a large automotive supplier and modifying current frameworks and approaches to fit the company's requirements.

Suggested Citation

  • Kormann, Benjamin & Altendorfer-Kaiser, Susanne, 2017. "Influence of patterns and data-analytics on production logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg Inter, volume 23, pages 233-254, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:209311
    DOI: 10.15480/882.1460
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    References listed on IDEAS

    as
    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. Souza, Gilvan C., 2014. "Supply chain analytics," Business Horizons, Elsevier, vol. 57(5), pages 595-605.
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