Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


Cemernek David, Gursch Heimo, Kern Roman

Big Data as a Promoter of Industry 4.0: Lessons of the Semiconductor Industry

IEEE 15th International Conference of Industrial Informatics - INDIN'2017, IEEE, Emden, Germany, 2017

The catchphrase “Industry 4.0” is widely regarded as a methodology for succeeding in modern manufacturing. This paper provides an overview of the history, technologies and concepts of Industry 4.0. One of the biggest challenges to implementing the Industry 4.0 paradigms in manufacturing are the heterogeneity of system landscapes and integrating data from various sources, such as different suppliers and different data formats. These issues have been addressed in the semiconductor industry since the early 1980s and some solutions have become well-established standards. Hence, the semiconductor industry can provide guidelines for a transition towards Industry 4.0 in other manufacturing domains. In this work, the methodologies of Industry 4.0, cyber-physical systems and Big data processes are discussed. Based on a thorough literature review and experiences from the semiconductor industry, we offer implementation recommendations for Industry 4.0 using the manufacturing process of an electronics manufacturer as an example.

Gursch Heimo, Cemernek David, Kern Roman

Multi-Loop Feedback Hierarchy Involving Human Workers in Manufacturing Processes

Mensch und Computer 2017 - Workshopband, Manuel Burghardt, Raphael Wimmer, Christian Wolff, Christa Womser-Hacker, Gesellschaft für Informatik e.V., Regensburg, 2017

In manufacturing environments today, automated machinery works alongside human workers. In many cases computers and humans oversee different aspects of the same manufacturing steps, sub-processes, and processes. This paper identifies and describes four feedback loops in manufacturing and organises them in terms of their time horizon and degree of automation versus human involvement. The data flow in the feedback loops is further characterised by features commonly associated with Big Data. Velocity, volume, variety, and veracity are used to establish, describe and compare differences in the data flows.
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