BERNHARD MITSCHANG (Stuttgart University, Germany)
Data Science in the Manufacturing Domain
The manufacturing domain is known for its high automation level that is achieved by means of a number of cooperating systems in all areas covering the product lifecycle: product design, product engineering, enterprise resource planning, supply chain management, manufacturing execution, service and support, recycling etc. This suite of systems is known to produce high volumes of data in all engineering and production areas. In order to exploit these volumes of data for optimization purposes, many data science projects are set up to analyze the available data and to come up with optimization proposals. In this talk I will first give an overview on the manufacturing domain and then dive into some data science topics, especially into data management and data analysis issues. Finally, I will come up with lessons learnt and a set of challenges for future work.
Since 1998, I am professor for Database and Information Systems and head of the department’ Applications of Parallel and Distributed Systems’ that is part of the Institute of Parallel and Distributed Systems (IPVS) at the Faculty of Computer Science, Electrical Engineering, and Information Technology at the Universität Stuttgart, Stuttgart, Germany. From 1994 to 1998 I held the position of a professor at the Technische Universität München. In 1988 I received the Ph.D. degree (Dr.-Ing.) in Computer Science from the University of Kaiserslautern, and in 1994 I got the venia legendi for practical Computer Science from the University of Kaiserslautern. From 1989 to 1990 I was on leave to IBM Almaden Research Center, San Jose, CA as a visiting scientist, and in 2003, 2008, 2013, and 2019 I was on sabbatical leave to IBM Research and Development Lab in Böblingen, Germany. Since 2015, I am Chair and member of the Graduate School of Excellence on advanced Manufacturing Engineering, part of the German Excellence Initiative. The research as well as teaching spectrum of my department covers both the broad spectrum of database applications ranging from business applications to engineering systems as well as database kernel, database middleware, and mobile technologies. Currently, there is much focus on data analytics and data-intensive applications as well as on scalable data processing architectures.