• DocumentCode
    3576687
  • Title

    Big data analytics for supply chain management

  • Author

    Leveling, Jens ; Edelbrock, Matthias ; Otto, Boris

  • Author_Institution
    Software Eng., Fraunhofer-Inst. for Mater. Flow & Logistics IML, Dortmund, Germany
  • fYear
    2014
  • Firstpage
    918
  • Lastpage
    922
  • Abstract
    A high number of business cases are characterized by an expanded complexity. This is based on increased collaboration between companies, customers and governmental organizations on one hand and more individual products and services on the other hand. Due to that, companies are planning to address these issues with Big Data solutions. This paper deals with Big Data solutions focusing on Supply Chains, which represents a key discipline for handling the increased collaboration next to vast amounts of exchanged data. Today, the main focus lays on optimizing Supply Chain Visibility to handle complexity and to support decision making for handling risks and interruptions along supply chains. Therefore, Big Data concepts and technologies will play a key role. This paper describes the current skituation, actual solutions and presents exemplary use-cases for illustration. A classification regarding the area of application and potential benefits arising from Big Data Analytics are also given. Furthermore, this paper outlines general technologies to show capabilities of Big Data analytics.
  • Keywords
    Big Data; data analysis; decision making; production engineering computing; risk management; supply chain management; Big Data analytics; data handling; decision making; risk handling; supply chain management; supply chain visibility optimization; Big data; Companies; Data models; Databases; Supply chain management; Supply chains; big data; supply chain management; supply chain risk management; supply chain visibility;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/IEEM.2014.7058772
  • Filename
    7058772