Prediction Model for Agile Rework Scheduling

September 5, 2017

Prediction Model for Agile Rework Scheduling

Speaker: Sven Krzoska, Volkswagen (VW)

Dipl.-Wirtsch.-Ing. Sven Krzoska, expert for Data Mining applications for Industrial Engineering at Volkswagen (VW AG), presents a Prediction Model for Agile Rework Scheduling and illustrates it with an example application from the automotive industry. Already today, large amounts of data are created in production environments to document the construction state and the product quality. However, in most cases, these data are not systematically analyzed and used beyond their original documentation purpose. Through the use of data mining, data from the past can be leveraged to predict, early in the production process and specific for each assembled vehicle individually, the rework time required to fix product quality issues. In this presentation shows the development of the rework time prediction model along the CRoss-Industry Standard Process for Data Mining (CRISP-DM) and provides an outlook to using this predictions for process improve- ments.

Sven Krzoska, Volkswagen (VW)

Expert for Industrial Engineering and Data Mining Dipl.-Wirtsch.-Ing.

Sven Krzoska studied Mechanical Engineering and Engineering Management (Wirtschaftsingenieurwesen Maschinenbau) at Technical University of Braunschweig, focusing on Production System Technology, Automotive Technology, Organisation, and Leadership. Since 2013 he works at Volkswagen (VW AG) in Industrial Engineering. His special topics are the influence of Industry 4.0 on Industrial Engineering and the changes for Industrial Engineering resulting from a continuously increasing Digitization. In his on-going dissertation project, which is supervised by Prof. Dr.-Ing. Jochen Deuse, he explores and pilotes Data Mining application use cases for Industrial Engineering.