Over the past years, a change of feedback data in terms of quantity, quality, and timeliness could be observed in production. The generation of high resolution production feedback data enables producing companies to apply big data analytics in order to create competitive advantages. This paper describes how logistical models can be used to conduct big data analytics. It will be explained how such logistic-oriented big data analyses can be applied to improve the logistical performance of producing companies. The results will be illustrated with the help of a best practice project.

In the course of globalization, producing companies are confronted with new challenges to optimize their logistics performance based on logistical measures. In order to be able to create a quantifiable basis for the selection of optimal measures, a management tool was developed at the Institute for Production Systems and Logistics. The developed tool is based on logistical and transfer models for logistically and monetarily evaluating logistical measures. Based on implemented logistical measures, this paper focuses on the evaluation of improved logistics performance and its impact on sales.