Influence of patterns and data-analytics on production logistics

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.