In manufacturing contexts, Industry 4.0 and Industry 5.0 are forefront names for the concepts, technologies, practices, and skills underlying smart factories and industries of the future. Industry 5.0 extends the technological pillars of Industry 4.0 by putting more emphasis on human centricity, sustainability and resilience in the engineering (architecture, design and development), deployment (strategic/technology road mapping, business/digital transformation, and operationalization), and management (operational excellence and continual improvement) of complex cyber-physical production systems.
This book contributes to bridging some key research gaps that hinder a full adoption of these trends in industry. Key topics focus on how to ensure applicability of human centricity, sustainability and resilience requirements in all three aspects related to system design, transformation, and operation, namely (1) human behaviors, skills, and competencies; (2) activities and business processes; and (3) system structure (including technology and information), behavior (including planning, monitoring, and control), and outcomes (including products and services). Case studies are provided in various chapters of the book to illustrate these key topics.
Chapter
1. Cyber-Physical and Human Systems: A Review of Concept,
Terminology and Human Roles.
Chapter
2. Framework and Requirements for
Digital Twin-Based Smart Manufacturing.
Chapter
3. Human-Centred Digital
Twin: Concepts, Methods and Technologies.
Chapter
4. A Literature Analysis
of Mutual Influences between Industry 4.0 and 5S Continual Improvement Tool
in Different Digitalization Contexts.
Chapter
5. Digitization to Support
Lean and Optimization in High-Mix Low-volume Environment.
Chapter
6. Two
Level Integration for Data-driven Multi-operations Supply Chain System
Optimization.
Chapter
7. Decision-making in General Cargo Port in the Era of
Industry 4.0.
Chapter
8. Toward a Simulation-based Decision Support System
to Predict and Cope with Mass Influx of Calls.
Chapter
9. Technician 5.0: A
Hybrid Framework Integrating Chatbot, Digital Twin, and Machine Learning for
Human-Centric Predictive Maintenance in Industry 5.0.
Chapter
10. Machine
and Deep Learning Approaches for Advanced Process Monitoring in Quality 5.0.-
Chapter
11. Contribution of AI in Product Continuous Quality Management in
Industry 4.0/5.0.
Malek Masmoudi is an associate professor of Industrial Engineering at the University of Sharjah (UAE) and the University of Jean-Monnet (France). He received a Doctorate of Sciences (Habilitation) from Jean-Monnet University in 2017; a PhD from ISAE-SUPAERO, University of Toulouse (France), in 2011; and a Research Masters degree from École Centrale de Lille (France) in 2008. He is Head of the Modeling and Optimization for Sustainable Operations and Supply chains (MOSOS) research group at the University of Sharjah. His main research interests lie in the applications of data science and optimization techniques across various engineering and management fields. He is the co-author of two Springer books and has published more than 120 papers, including 48 articles in international journals.
Oulaid Kamach received the Master degree in applied mathematics from Franche Compte University in 2001 (France) and the PhD in control engineering from the INSA of Lyon in 2004 (France). He is full professor of industrial engineering and logistics is the Department of Electrical and Industrial Engineering of the University of Abdelmalek ESSAADI (Tangier) since 2018. His research interests include flow management, simulation, optimization, monitoring, and planification and scheduling. He has published over 72 research articles in refereed journals and conferences, and has supervised over 15 Masters and PhD candidates
Saber Darmoul is Associate Professor of Systems Engineering at the University of Twente. He worked in renowned engineering education institutions in the EMEA (Europe, Middle East, North Africa) region, such as the French SIGMA Clermont, King Saud University, Saudi Arabia, and Ecole Centrale Casablanca, Morocco. His research interests focus on exploring the use of distributed artificial intelligence (multi-agent, holonic, and bionic/bio-inspired systems) to monitor and control disturbances and risks in cyber-physical systems. Applications include intelligent manufacturing systems (IMS) and intelligent transportation systems (ITS).
Patrick Siarry received the PhD degree from the University Paris 6, in 1986, and the Doctorate of Sciences (Habilitation) from the University Paris 11, in 1994. He was first involved in the development of analog and digital models of nuclear power plants at Electricité de France (EDF). Since 1995 he is a professor in automatics and informatics. His main research interests are the applications of new stochastic global optimization heuristics to various engineering fields.
