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Big data analytics for supply chain management

 
: Leveling, Jens; Edelbrock, Matthias; Otto, Boris

:

Institute of Electrical and Electronics Engineers -IEEE-, Malaysia Section; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2014. Proceedings Vol.2 : 9 - 12 December 2014, Selangor Darul Ehsan, Malaysia
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-6410-9
pp.918-922
International Conference on Industrial Engineering and Engineering Management (IEEM) <2014, Malaysia>
English
Conference Paper
Fraunhofer IML ()
supply chain management; Supply Chain Visibility; supply chain risk management; Big Data

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 situation, 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.

: http://publica.fraunhofer.de/documents/N-332338.html