Dimitris Mourtzis (editor) - Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology

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John Angelopoulos Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, Greece

Alexandre Dolgui IMT Atlantique, LS2N, CNRS, Nantes, France

Paul Arthur Dreyfus Swiss Federal Institute of Technology (EPFL), SCI STI DK, Lausanne, Switzerland

Bogdan I. Epureanu Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States

Robert X. Gao Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, United States

Andreas Gtzlaff Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany

Dmitry Ivanov Berlin School of Economics and Law Supply Chain and Operations Management, Berlin, Germany

Tim Janke Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany

Matthias Jarke

Information Systems & Databases, RWTH Aachen University, Aachen

Fraunhofer FIT, Birlinghoven Castle, Sankt Augustin, Germany

Dimitris Kiritsis Swiss Federal Institute of Technology (EPFL), SCI STI DK, Lausanne, Switzerland

Istvn Koren Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

Gisela Lanza wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Xingyu Li Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States

Zhi Li Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China

Dimitris Mourtzis Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece

Aydin Nassehi Department of Mechanical Engineering, University of Bristol, Bristol, United Kingdom

Henning Neumann Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany

Nikos Panopoulos Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, Greece

Nikolaos Papakostas Laboratory for Advanced Manufacturing Simulation and Robotics, School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland

Mirco Peron NTNU, Trondheim, Norway

Sina Peukert wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Foivos Psarommatis Swiss Federal Institute of Technology (EPFL), SCI STI DK, Lausanne, Switzerland

Aswin K. Ramasubramanian Laboratory for Advanced Manufacturing Simulation and Robotics, School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland

Gnther Schuh Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany

Fabio Sgarbossa NTNU, Trondheim, Norway

Panagiotis Stavropoulos Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece

Gwen Louis Steier wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Zonggui Tian Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China

Jinjiang Wang University of Petroleum, School of Mechanical and Transportation Engineering, Beijing, China

Lihui Wang Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

Ray Y. Zhong Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China

The paradigm shift from mass production to on-demand, personalized, customer-driven, and knowledge-based production reshapes manufacturing. Smart manufacturing leads to an automated world that relies more on information and communication technologies (ICTs) and sophisticated information-technology-intensive processes, enhancing flexibility. Furthermore, as automation and digital supply chain management become the norm across enterprise systems, advanced manufacturing becomes increasingly linked at a global level. Manufacturing companies are under pressure to achieve the goals of high competitiveness and profitability in a globalized and volatile environment. To address these challenges, engineers have to develop and implement new design and operation methodologies for production networks taking also into account mass personalization and market uncertainty. In the era of digitalization, the integration of cloud-based approaches can elevate enterprise performance. Therefore, to meet these challenges, new technologies such as cyber-physical systems (CPS), artificial intelligence (AI), augmented reality (AR), big data analytics, the Internet of things (IoT), and the industrial Internet of things (IIoT) must be integrated.