Designing smart manufacturing systems

Designing smart manufacturing systems

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 syste...

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Detalles Bibliográficos
Otros Autores: Hussain, Chaudhery Mustansar, editor (editor), Rossit, Daniel, editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England : Academic Press [2023]
Edición:First edition
Materias:
Industry 4.0.
Manufacturing processes > Technological innovations.
Manufacturing processes > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835435406719
Tabla de Contenidos:
  • Front Cover
  • Designing Smart Manufacturing Systems
  • Copyright
  • Contents
  • Contributors
  • Part I Smart manufacturing design
  • 1 Cloud manufacturing implementation for smart manufacturing networks
  • 1.1 Introduction
  • 1.2 Cloud manufacturing
  • 1.3 CMfg approach for smart manufacturing networks
  • Database module
  • Intelligent assessment and optimization module
  • Functional compatibility engine
  • Intelligent optimization engine
  • Decision-making module
  • Supplier decision-making engine
  • Customer decision-making engine
  • 1.4 Cloud manufacturing platform implementation
  • 1.5 Intelligent recommendation system
  • 1.6 Recommendation system implementation
  • Customer profiling
  • Intelligent regression
  • Evaluation and discussion
  • 1.7 Conclusions
  • References
  • 2 Improving Brazilian Engineering Education: real engineering challenges in an IIoT undergraduate course
  • 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
  • Acknowledgment
  • References
  • Part II Industry 4.0 information technology developments
  • 3 New verification and validation tools for Industry 4.0 software
  • 3.1 Introduction
  • 3.2 Background in software testing
  • 3.2.1 Software testing in Industry 4.0
  • 3.3 MSS-based testing
  • 3.4 TAPIR
  • 3.4.1 Aspect-Oriented Programming
  • 3.4.2 Framework design, implementation, and operation
  • Design
  • Implementation
  • Operation
  • 3.4.3 Coverage criteria
  • Coverage criteria for valid sequences.
  • Coverage criteria for invalid sequences
  • 3.4.4 Generotron
  • Design
  • Implementation
  • Front-end design
  • Operation
  • 3.5 A black-box testing technique for information visualization
  • 3.6 Test case. Rock.AR, a software for the mining industry
  • 3.6.1 Bugs detected with the framework
  • First error found
  • Second error found
  • 3.6.2 Bugs detected with Generotron
  • 3.6.3 Bug detection on visual representations
  • 3.7 Conclusions &amp
  • future works
  • Acknowledgment
  • References
  • 4 Stepping stone to smarter supervision: a human-centered multidisciplinary framework
  • DSS type, their positive effects, and those more questionable
  • Understanding of DSSs' undesired effects
  • Towards a Human-Centered Design (HCD) multidisciplinary framework for DSS
  • Phase 1. Identification of decision makers' needs and specification of the context
  • Suggested activities, methods, and analyses
  • Phase 2. Prototypes and usability testing
  • Suggested activities, methods, and analyses
  • Phase 3. Final tests and evaluation
  • Suggested methods and analysis
  • Discussion and conclusion
  • References
  • Part III Industry 4.0 business developments
  • 5 How to define a business-specific smart manufacturing solution
  • 5.1 Introduction
  • 5.2 Theoretical background
  • 5.3 Focus of the chapter
  • 5.3.1 Smart manufacturing reference architectures
  • 5.3.2 Industry 4.0 maturity assessment models
  • 5.3.3 Methodologies to design smart manufacturing
  • 5.3.4 Specification languages
  • 5.3.5 Project management for Industry 4.0 transformation
  • 5.3.6 Methodologies and techniques to optimize the shop-floor
  • 5.4 Case study
  • 5.4.1 Brief description of the organization
  • 5.4.2 Initial phase
  • 5.4.3 Analysis phase
  • 5.4.3.1 Value stream analysis
  • 5.4.3.2 Maturity assessment
  • Maturity assessment of production
  • Maturity assessment of suppliers.
  • 5.4.4 Conceptualization
  • 5.5 Conclusion
  • Value stream mapping syntax
  • References
  • 6 Assessment of the competitiveness and effectiveness of the business model 4.0
  • 6.1 Introduction
  • 6.2 Business model 4.0
  • Creating value through the business model 4.0
  • Competitiveness of the business model 4.0
  • 6.3 Assessment of the competitiveness and effectiveness of the business model - case study
  • 6.4 Summary
  • References
  • 7 Sustainable Business Models in the context of Industry 4.0
  • 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?
  • Information
  • Value Chain
  • Relationship
  • Cost
  • Supply chain
  • Competitiveness
  • Human resources
  • Decision-making process
  • Innovativeness
  • Managerial practices
  • Strategy
  • Regulation
  • Infrastructure
  • Dynamic capability
  • Conclusion
  • Acknowledgments
  • References
  • 8 Understanding Digital Transformation challenges: evidence from Brazilian and British manufacturers
  • 8.1 Introduction
  • 8.2 Literature review
  • Digital Transformation
  • Technological challenges of Digital Transformation
  • Socio-managerial challenges of Digital Transformation
  • External Digital Transformation obstacles
  • Digital status of Brazilian and British manufacturing
  • 8.3 Main methodological procedures
  • 8.4 Analysis of case studies and main findings
  • Technological challenges
  • Socio-managerial challenges
  • Macroeconomic challenges
  • 8.5 Discussion
  • 8.6 Final considerations
  • References
  • 9 Smart green supply chain management: a configurational approach for reaching sustainable performance goals and decreasing COVID-19 impact
  • 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
  • Acknowledgments
  • References
  • 10 Multicriteria decision making approach for selection and prioritization of projects into the digital transformation journey
  • 10.1 Introduction
  • 10.2 Background and related works
  • 10.2.1 Digital Transformation
  • 10.2.2 Digital Maturity
  • 10.2.3 Strategic Roadmap
  • 10.2.4 AHP &amp
  • TOPSIS Multicriteria Decision Making support methods
  • 10.2.5 Prioritization for project development
  • 10.3 Proposed tool - SPREDT
  • 10.3.1 Development of the SPREDT tool
  • 10.3.2 Application of the SPREDT tool
  • 10.4 Application case, results, and discussions
  • 10.5 Conclusions
  • References
  • Part IV Industry 4.0 production planning and decision making
  • 11 Smart manufacturing scheduling with Petri nets
  • 11.1 Introduction
  • 11.2 Background
  • 11.2.1 Petri nets
  • 11.2.2 Modeling with Petri nets
  • 11.3 Metaheuristics and Petri nets
  • 11.4 Proposed approach
  • 11.4.1 Decoding
  • 11.4.2 Neighborhood
  • 11.5 Computational tests
  • 11.5.1 Preliminaries
  • 11.5.2 Calibration
  • 11.5.2.1 Performance measures
  • 11.5.2.2 Adequacy tests
  • 11.5.3 Results
  • 11.6 Conclusions and future work
  • References
  • 12 Characterizing nervousness at the shop-floor level in the context of Industry 4.0
  • 12.1 Introduction
  • 12.2 Bibliometric analysis
  • 12.3 Literature review
  • First notions of schedule nervousness (evolution of the term schedule nervousness)
  • Schedule nervousness in rescheduling and online approaches
  • Schedule nervousness in control
  • Production planning and I4.0
  • 12.4 Schedule nervousness in a new context
  • 12.5 The shop-floor schedule nervousness framework
  • The SFSN characterization
  • The SFSN scope.
  • The SFSN and the systems context
  • Relationship among rescheduling, stability, and nervousness
  • Time-related features
  • Inner system issues that leverage nervousness
  • Outer system nervousness management mechanisms
  • A simple SFSN conceptual model
  • Physical dimension
  • Temporal dimension
  • Wrapping it up
  • The framework in practice: an illustrative case
  • 12.6 Conclusions
  • Acknowledgments
  • References
  • 13 Digital and smart production planning and control
  • 13.1 Production planning and control evolution
  • 13.1.1 Production planning and control 1.0 (until 1960s)
  • 13.1.2 Production planning and control 2.0 (between 1970s and 1980s)
  • 13.1.3 Production planning and control 3.0 - (between 1990s and 2010s)
  • 13.1.4 Production planning and control 4.0 - (from 2010s)
  • 13.2 A bibliometric analysis on digital and smart production planning and control
  • 13.3 Digital and smart production planning and control frameworks
  • 13.3.1 Framework of classical PPC updated by digital technologies
  • 13.3.2 Framework of production planning and control as a service (PPCaaS)
  • 13.4 Digital technologies applied in the production planning and control
  • 13.4.1 Additive manufacturing (AM)
  • 13.4.2 Big data analytics (BDA)
  • 13.4.3 Digital twin (DT)
  • 13.4.4 Machine learning (ML)
  • 13.5 The future of Production Planning and Control 4.0 concept
  • References
  • 14 Simulation-based generation of rescheduling knowledge using a cognitive architecture
  • 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
  • 14.4.1 Design of repair operators proposition-evaluation, decision, and application knowledge
  • 14.4.1.1 Design and implementation of operators proposition-evaluation knowledge (Kpe).
  • 14.4.1.2 Operator decision and application using decision procedure and application knowledge (Ka).

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