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
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;
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on
DOI :
10.1109/IEEM.2014.7058772