Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology
- 1st Edition - November 10, 2021
- Imprint: Elsevier
- Editor: Dimitris Mourtzis
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 6 5 7 - 4
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 6 5 8 - 1
Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology draws on the latest industry advances to provide everything needed for the effec… Read more
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Request a sales quoteDesign and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology draws on the latest industry advances to provide everything needed for the effective implementation of this powerful tool. Shorter product lifecycles have increased pressure on manufacturers through the increasing variety and complexity of production, challenging their workforce to remain competitive and profitable. This has led to innovation in production network methodologies, which together with opportunities provided by new digital technologies has fed a rapid evolution of production engineering that has opened new solutions to the challenges of mass personalization and market uncertainty.
In addition to the latest developments in cloud technology, reference is made to key enabling technologies, including artificial intelligence, the digital twin, big data analytics, and the internet of things (IoT) to help users integrate the cloud approach with a fully digitalized production system.
- Presents diverse cases that show how cloud-based technologies can be used in different ways as part of the standard operation of global production networks
- Provides detailed reviews of new technologies like the digital twin, big data analytics, and blockchain to provide context on the role of cloud technologies in a fully digitalized system
- Explores future trends for cloud technology and production engineering
Researchers and engineers working in production and manufacturing engineering. Researchers and managers interested in digital transformation
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Acknowledgments
- Chapter 1: Introduction to cloud technology and Industry 4.0
- Abstract
- 1.1: Introduction
- 1.2: Structure of the book
- References
- Chapter 2: Expected trends in production networks for mass personalization in the cloud technology era
- Abstract
- 2.1: Introduction
- 2.2: Emerging technologies enabling new production paradigms
- 2.3: Trends in supply chain: Reconfigurable SC (X-network) and intertwined supply network (ISN)
- 2.4: Trends in production systems: Factory production networks
- 2.5: Conclusions
- References
- Chapter 3: Latest advances in cloud manufacturing and global production networks enabling the shift to the mass personalization paradigm
- Abstract
- 3.1: Motivation and section structure
- 3.2: Main characteristics of MPP
- 3.3: Global production networks (GPNs) and cloud manufacturing (CM)
- 3.4: Impact of MPP on the design and operation of GPNs
- 3.5: Practical examples
- 3.6: Summary and future research needs
- References
- Further reading
- Chapter 4: The mass personalization of global networks
- Abstract
- 4.1: Introduction
- 4.2: Evolution of manufacturing paradigms
- 4.3: Manufacturing networks life cycle
- 4.4: Industrial frameworks and case studies from mass customization (MC) toward mass personalization (MP)
- 4.5: Discussion and outlook
- 4.6: Conclusions
- References
- Chapter 5: Production management guided by industrial internet of things and adaptive scheduling in smart factories
- Abstract
- 5.1: Introduction
- 5.2: State of the art
- 5.3: Decision-making frameworks in smart factories
- 5.4: Production networks toward mass personalization
- 5.5: Discussion and outlook
- 5.6: Conclusions
- References
- Chapter 6: Digital technologies as a solution to complexity caused by mass personalization
- Abstract
- 6.1: Introduction
- 6.2: Digital technologies for product development
- 6.3: Product data and lifecycle management platforms
- 6.4: Conclusions and outlook
- References
- Chapter 7: Innovative smart scheduling and predictive maintenance techniques
- Abstract
- 7.1: Background
- 7.2: Motivation
- 7.3: CPS and industry 4.0
- 7.4: Smart scheduling
- 7.5: Predictive maintenance
- 7.6: Conclusion and future research direction
- References
- Chapter 8: Review of commercial and open technologies available for Industrial Internet of Things
- Abstract
- 8.1: Introduction
- 8.2: Fundamentals and state of the art
- 8.3: IIoT framework
- 8.4: IIoT development
- 8.5: IIoT impact
- 8.6: Summary and reflection
- References
- Chapter 9: The role of big data analytics in the context of modeling design and operation of manufacturing systems
- Abstract
- 9.1: Introduction
- 9.2: Generic framework for data utilization in industrial environments
- 9.3: Sources of data in an industrial environment
- 9.4: Data modeling, semantics, and knowledge extraction
- 9.5: Process modeling, key feature for the optimization of design and operation of manufacturing systems
- 9.6: Data contribution to design phase
- 9.7: Data contribution to the operation of manufacturing systems
- 9.8: Modeling, design and operation of manufacturing systems for achieving zero defect manufacturing
- 9.9: Conclusions
- References
- Chapter 10: Digital twins in industry 4.0
- Abstract
- 10.1: Introduction
- 10.2: Value of digital twins
- 10.3: Definition of DTs and levels
- 10.4: Enabling technologies
- 10.5: DT architecture
- 10.6: Applications in industry 4.0
- 10.7: Socio-economic impact
- 10.8: Future challenges
- 10.9: Conclusions
- References
- Chapter 11: Review of machine learning technologies and artificial intelligence in modern manufacturing systems
- Abstract
- 11.1: Introduction
- 11.2: Definition of artificial intelligence
- 11.3: Definition of machine learning
- 11.4: Applications of AI in manufacturing
- 11.5: Discussion and outlook
- 11.6: Future developments and roadmap
- References
- Chapter 12: Blockchain-enabled product lifecycle management
- Abstract
- 12.1: Introduction
- 12.2: PLM: Concepts, framework, and key phases
- 12.3: Challenges of PLM in I4.0
- 12.4: Blockchain for PLM: Opportunities
- 12.5: Blockchain-enabled PLM: Case studies
- 12.6: Conclusions
- References
- Index
- Edition: 1
- Published: November 10, 2021
- No. of pages (Paperback): 406
- No. of pages (eBook): 406
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780128236574
- eBook ISBN: 9780128236581
DM
Dimitris Mourtzis
Dimitris Mourtzis is a Professor in the Dept. of Mechanical Engineering and Aeronautics and Vice President of the Research Council of the University of Patras. He is a Founding Member of the CLEAN AVIATION Joint Undertaking Governing Body and Director of the Laboratory for Manufacturing Systems and Automation (LMS). He is a Fellow of the International Academy for Production Research (CIRP), the International Federation of Automatic Control (IFAC), the International Federation of Information Processing (IFIP), and Member of the American Society of Mechanical Engineers (ASME) and the Institute of Electrical and Electronic Engineers (IEEE). His scientific interests focus on design, planning, and control of manufacturing systems and networks, robotic systems, automation, advanced simulation including augmented, mixed, and virtual reality in manufacturing, manufacturing processes modeling and optimization, digital manufacturing, industry 4.5, industry 5.0, metaverse, and hybrid teaching factory.
Affiliations and expertise
Professor, Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics; Vice President, Research Council, University of Patras, Rio Patras, GreeceRead Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology on ScienceDirect