The concept of industry 4.0 is spreading worldwide and readiness models exist to determine organizational or national maturity. On the other hand, the regional perspective of the digital transformation is yet to be widely researched, although it significantly determines how the concept of industry 4.0 can be introduced to the organisations. This book identifies the regional aspect of industry 4.0 and provides a regional (NUTS 2 classified) industry 4.0 indicator system model that is based on open data sources. This new model serves as a tool to evaluate regional economy to support governmental decisions. It also provides territorial councils with a decision-support tool for field investment decisions. And finally, this model offers investors with a heat map to evaluate regional economies successful implementation of industry 4.0 solutions.
Tabella dei contenuti
Chapter 1. Introduction.- Chapter 2. Regional aspects of Industry 4.0.- Chapter 3. Measures of regional Industry 4.0 + readiness.- Chapter 4. Evaluation of regional I4.0 readiness – data analysis and composite indicator development.- Chapter 5. Summary.
Circa l’autore
Janos Abonyi is Full Professor of Computer Science and Chemical Engineering in the Department of Process Engineering at the University of Pannonia (Hungary). Previously, he was employed at the Control Laboratory of the Delft University of Technology (The Netherlands). Dr. Abonyi has co-authored more than 250 journal papers and chapters in books and has published five research monographs and one Hungarian textbook about data mining. His research interests include complexity, process engineering, quality engineering, data mining, and regional development, and Industry 4.0.
Timea Czvetko is a researcher at University of Pannonia (Hungary) with a focus on research and innovation management.
Gergely Honti is a Ph D candidate at the Department of Process Engineering at University of Pannonia (Hungary) and researcher at Institute of Advanced Studies, Kőszeg (Hungary). He has been working as a Computer Engineer for 5 years and has been involved in cutting edge technology projects, like Siemens Syngo. His research area is big data and data analysis.
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