Designing Smart Manufacturing Systems
- 1st Edition - April 13, 2023
- Imprint: Academic Press
- Editors: Daniel Rossit, Chaudhery Mustansar Hussain
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 2 0 8 - 4
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 6 7 4 - 7
Design of Smart Manufacturing Systems covers the fundamentals and applications of smart manufacturing or Industry 4.0 system design, along with interesting case studies. Digitizat… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteDesign of Smart Manufacturing Systems covers the fundamentals and applications of smart manufacturing or Industry 4.0 system design, along with interesting case studies. Digitization and Cyber-Physical Systems (CPS) have vastly increased the amount of data available to manufacturing production systems. This book addresses the planning, modeling and experimentation of different decision-making problems as well as the conditions that affect manufacturing. In addition, recent developments in the design of smart manufacturing and its applications are explained, covering the needs of both researchers and practitioners.
To fully navigate the challenges and opportunities of smart manufacturing systems, contributions are drawn from operations research, information systems, computer science and industrial engineering as well as manufacturing engineering.
- Addresses hot topics like cybersecurity and artificial intelligence in smart manufacturing systems
- Provides case studies that show how solutions have been applied in practice
- Explores how smart manufacturing systems may impact on operators
Advanced students, researchers, and manufacturing engineers with an interest in Industry 4.0 and smart factories
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Part I: Smart manufacturing design
- Chapter 1: Cloud manufacturing implementation for smart manufacturing networks
- Abstract
- 1.1. Introduction
- 1.2. Cloud manufacturing
- 1.3. CMfg approach for smart manufacturing networks
- 1.4. Cloud manufacturing platform implementation
- 1.5. Intelligent recommendation system
- 1.6. Recommendation system implementation
- 1.7. Conclusions
- References
- Chapter 2: Improving Brazilian Engineering Education: real engineering challenges in an IIoT undergraduate course
- Abstract
- Acknowledgement
- Introduction
- Modernization of Engineering Education in Brazil
- Real-world research problem
- The Industrial Internet-of-Things course
- Challenge-based learning and CDIO frameworks as integrated active learning methodologies
- The assessment tools for projects
- Presentation rubric
- Peer assessment rubrics
- CDIO rubrics
- Ethics and privacy rubric
- The scenario for application of integrated active learning methodologies
- Results
- Final remarks
- References
- Part II: Industry 4.0 information technology developments
- Chapter 3: New verification and validation tools for Industry 4.0 software
- Abstract
- Acknowledgement
- 3.1. Introduction
- 3.2. Background in software testing
- 3.3. MSS-based testing
- 3.4. TAPIR
- 3.5. A black-box testing technique for information visualization
- 3.6. Test case. Rock.AR, a software for the mining industry
- 3.7. Conclusions & future works
- References
- Chapter 4: Stepping stone to smarter supervision: a human-centered multidisciplinary framework
- Abstract
- DSS type, their positive effects, and those more questionable
- Towards a Human-Centered Design (HCD) multidisciplinary framework for DSS
- Phase 1. Identification of decision makers' needs and specification of the context
- Phase 2. Prototypes and usability testing
- Phase 3. Final tests and evaluation
- Discussion and conclusion
- References
- Part III: Industry 4.0 business developments
- Chapter 5: How to define a business-specific smart manufacturing solution
- Abstract
- 5.1. Introduction
- 5.2. Theoretical background
- 5.3. Focus of the chapter
- 5.4. Case study
- 5.5. Conclusion
- Appendix. Value stream mapping syntax
- References
- Chapter 6: Assessment of the competitiveness and effectiveness of the business model 4.0
- Abstract
- 6.1. Introduction
- 6.2. Business model 4.0
- 6.3. Assessment of the competitiveness and effectiveness of the business model – case study
- 6.4. Summary
- References
- Chapter 7: Sustainable Business Models in the context of Industry 4.0
- Abstract
- Acknowledgements
- Introduction
- What is Industry 4.0 (I4.0) and Sustainable Business Model?
- Review methodology
- How Industry 4.0 can influence the development of Sustainable Business Models?
- Conclusion
- References
- Chapter 8: Understanding Digital Transformation challenges: evidence from Brazilian and British manufacturers
- Abstract
- 8.1. Introduction
- 8.2. Literature review
- 8.3. Main methodological procedures
- 8.4. Analysis of case studies and main findings
- 8.5. Discussion
- 8.6. Final considerations
- References
- Chapter 9: Smart green supply chain management: a configurational approach for reaching sustainable performance goals and decreasing COVID-19 impact
- Abstract
- Acknowledgements
- Introduction
- Methodology
- Supply chain and COVID-19
- Smart Supply Chain
- Green supply chain management – internal and external green practices
- Smart green supply chain management – a configurational approach
- Smart green supply chain and COVID-19
- Conclusions
- References
- Chapter 10: Multicriteria decision making approach for selection and prioritization of projects into the digital transformation journey
- Abstract
- 10.1. Introduction
- 10.2. Background and related works
- 10.3. Proposed tool – SPREDT
- 10.4. Application case, results, and discussions
- 10.5. Conclusions
- References
- Part IV: Industry 4.0 production planning and decision making
- Chapter 11: Smart manufacturing scheduling with Petri nets
- Abstract
- 11.1. Introduction
- 11.2. Background
- 11.3. Metaheuristics and Petri nets
- 11.4. Proposed approach
- 11.5. Computational tests
- 11.6. Conclusions and future work
- References
- Chapter 12: Characterizing nervousness at the shop-floor level in the context of Industry 4.0
- Abstract
- Acknowledgements
- 12.1. Introduction
- 12.2. Bibliometric analysis
- 12.3. Literature review
- 12.4. Schedule nervousness in a new context
- 12.5. The shop-floor schedule nervousness framework
- 12.6. Conclusions
- References
- Chapter 13: Digital and smart production planning and control
- Abstract
- 13.1. Production planning and control evolution
- 13.2. A bibliometric analysis on digital and smart production planning and control
- 13.3. Digital and smart production planning and control frameworks
- 13.4. Digital technologies applied in the production planning and control
- 13.5. The future of Production Planning and Control 4.0 concept
- References
- Chapter 14: Simulation-based generation of rescheduling knowledge using a cognitive architecture
- Abstract
- 14.1. Introduction
- 14.2. Conceptual modeling
- 14.3. Problem-Space Computational Model (PSCM)
- 14.4. Representation and design of schedule states and repair operators
- 14.5. Tuning repair operator proposition-evaluation knowledge (Kpe) by using reinforcement learning
- 14.6. Industrial case study
- 14.7. Concluding remarks and future work
- References
- Index
- Edition: 1
- Published: April 13, 2023
- No. of pages (Paperback): 420
- No. of pages (eBook): 420
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780323992084
- eBook ISBN: 9780323996747
DR
Daniel Rossit
Daniel Alejandro Rossit, PhD is a Researcher of CONICET (National Research Council of Argentina) and Professor in the Engineering Department of the Universidad Nacional del Sur, Bahía Blanca, Argentina. He has an Industrial Engineer degree and a PhD in Engineering. His research has focused on production problems, operations research and engineering systems optimization. He has published in journals such as Omega, International Journal of Production Research, The International Journal of Advanced Manufacturing, Computers and Electronics in Agriculture, International Journal of Computer Integrated Manufacturing, among others.
Affiliations and expertise
Engineering Department, Universidad Nacional del Sur, ArgentinaCM
Chaudhery Mustansar Hussain
Chaudhery Mustansar Hussain is an Adjunct Professor and Director of Laboratories in the Department of Chemistry & Environmental Sciences at the New Jersey Institute of Technology (NJIT), Newark, New Jersey, United States. His research is focused on the applications of nanotechnology and advanced materials, environmental management, analytical chemistry, and other industries. Dr. Hussain is the author of numerous papers in peer-reviewed journals as well as a prolific author and editor in his research areas. He has published with Elsevier, the American Chemical Society, the Royal Society of Chemistry, John Wiley & Sons, CRC Press, and Springer.
Affiliations and expertise
Adjunct Professor & Director of Laboratories, New Jersey Institute of Technology (NJIT), Newark, NJ, USARead Designing Smart Manufacturing Systems on ScienceDirect