Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/209311 
Year of Publication: 
2017
Citation: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Title:] 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 [ISBN:] 978-3-7450-4328-0 [Publisher:] epubli GmbH [Place:] Berlin [Year:] 2017 [Pages:] 233-254
Publisher: 
epubli GmbH, Berlin
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.
Subjects: 
knowledge discovery in database
data analytics
process model
supply chain analytics
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Conference Paper

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