Designing smart manufacturing systems / Chaudhery Mustansar Hussain and Daniel Rossit, editors.

Format:

Book

Contributor:

Hussain, Chaudhery Mustansar, editor.

Rossit, Daniel, editor.

Language:

English

Subjects (All):

Industry 4.0.

Manufacturing processes--Technological innovations.

Manufacturing processes.

Manufacturing processes--Data processing.

Physical Description:

1 online resource (422 pages)

Edition:

First edition.

Place of Publication:

London, England : Academic Press, [2023]

Summary:

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.

Contents:

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

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

Front-end design

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

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

Phase 3. Final tests and evaluation

Suggested methods and analysis

Discussion and conclusion

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

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

7 Sustainable Business Models in the context of Industry 4.0

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

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

9 Smart green supply chain management: a configurational approach for reaching sustainable performance goals and decreasing COVID-19 impact

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

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

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

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

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

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).

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9780323996747

0323996744

OCLC:

1376934147