Details

Autor(en) / Beteiligte

• Hussain, Chaudhery Mustansar,
• Rossit, Daniel,

Titel

Designing smart manufacturing systems

Ort / Verlag

London, England : Academic Press,

Erscheinungsjahr

[2023]

Link zum Volltext

• Elsevier Books AutoHoldings

Beschreibungen/Notizen

• Includes bibliographical references and index.
• 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).
• Description based on print version record.