3rd International Conference on
Event-Based Control, Communication and Signal Processing
May 24-26, 2017, Funchal, Madeira, Portugal

Special Presentation

Industry 4.0: application scenarios and the impact of control and automation

The term “Industry 4.0” (or “Industrie 4.0” in German) refers to the fourth industrial revolution, a change in the organisation and management of the entire value chain throughout products’ life cycles due to increased and ubiquitous information and communication technology. Industry 4.0 relies on all the relevant information being made available in real time through networking of all the different components that make up the value chain, as well as the ability to use this data to ascertain the optimal value stream at any given point in time. This networking of human beings, objects and systems enables the emergence of dynamic, real‐time optimised and self‐organising value chains involving several different companies and providing new business models. These value chains can be optimised based on a variety of criteria such as cost, availability and resource consumption.

Thus, Industry 4.0 requires a multitude of control problems to be solved on all levels, from hard‐real time problems in the coordination of networked machines up to the optimization of supply chains between enterprises. In fact, Industry 4.0 has turned out to be a booster for control and automation research and innovation in the manufacturing and process industries and even beyond.

To structure the field of Industry 4.0, the German Industrie 4.0 Initiative has developed a doze “Application Scenarios”, covering products, processes and production resources, from engineering to operation. These Application Scenarios help to structure existing and future use cases and application examples. Furthermore, they give guidance towards the need for control and automation algorithms, both regarding functional and non‐functional requirements.

The author is a member of the Working Group 2 of the German Industrie 4.0 initiative which has elaborated these Application Scenarios and developed a research roadmap from this endeavor. Currently, the Application Scenarios are matched with similar approaches on an international level.

Alexander Fay
IEEE Member’02, Senior Member ’07

Alexander Fay received the Diploma with honors and the Ph.D. with honors in electrical engineering from the Technical University of Braunschweig, Germany, in 1995 and 1999, respectively. He worked five years at the ABB Corporate Research laboratories in Heidelberg and Ladenburg in the position of Head of Mechatronics research group and Head of Engineering Methods research group, among others. Since 2004, he is Full Professor and Head of the Institute of Automation Technology at the Helmut‐ Schmidt‐University in Hamburg, Germany.

In 2002, he was awarded one of “World’s Top 100 Innovators under 35” by MIT Technology Review. Alexander Fay received the Ring of Honor of the Association of German Engineers (VDI) in 2009. He is member of the scientific advisory board of the German Society for Measurement and Automation (GMA) and Head of its Engineering and Operation of automated Facilities department. He was a member of the IEEE Industrial Electronics Society Administration Committee 2009‐2011. Since 2009, he serves as an Associate Editor of IEEE Transactions on Industrial Informatics. He is author of more than 80 reviewed national and international journal papers and of more than 150 reviewed national and international conference papers, and of 7 patents. Currently, he has 20 researchers at his institute and is supervisor of about 20 Ph.D. theses. Between 2011 and 2013, he served as the Dean of the Mechanical Engineering Faculty, and between 2013 and 2015 as the Dean of the Industrial Engineering Department of his university.

Since 2014, he is member of the Scientific Advisory Board and of the WG 2 “Research and Innovation” of the German „Industrie 4.0“ initiative..

His main research interests are models and methods for the engineering of large automated systems, especially in the process and manufacturing industries, in buildings and transportation systems. Among other methods, he develops and employs knowledge‐based methods, ontologies and other AI techniques. The aim of his team is to develop models, methods and tools to increase engineering efficiency and to assist in the engineering and operation of industrial plants. The research projects are mostly undertaken in close cooperation with industry, i.e. suppliers and/or users of automation technology. Current research topics are, among others:

  • agent based control of transportation systems (e.g. railway freight transport, baggage transport in airports, and work piece transport within production systems)
  • requirement engineering for production systems regarding flexibility, agility, robustness etc.
  • development of a reference framework for evaluating and comparing agent‐based control algorithms in comparison to other decentralised and central control algorithms under varying degrees of disturbances
  • stability analysis of decentralised decision‐making entities with time‐delayed feedback
  • market‐based multi‐agent system for decentralized power and grid control
  • automatic code generation based on GRAFCET specifications
  • simulation‐based virtual commissioning of production automation systems
  • knowledge‐based automation of engineering tasks
  • automated reduction of alarm floods
  • methods for the systematic modernization of automation systems and adaption to “Industry 4.0” needs
  • robot‐based manufacturing of lot‐size‐1‐workpieces
  • real‐time location and location‐based services