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Designing Smart Manufacturing Systems

1st Edition - April 13, 2023

Editors: Chaudhery Mustansar Hussain, Daniel Rossit

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

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Design 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.

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