Implementing Industry 4. 0 in SMEs : : Concepts, Examples and Applications.

• Author: Matt, Dominik T.
• TeilnehmendeR: Modrák, Vladimír.; Zsifkovits, Helmut.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; ©2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (449 pages)

Table of Contents

Implementing Industry 4.0 in SMEs -- Preface -- Acknowledgments -- About This Book/Project -- Contents -- Notes on Contributors -- List of Figures -- List of Tables -- PartI Implementing Industry 4.0 for Smart Manufacturing in SMEs -- 1 Status of the Implementation of Industry 4.0 in SMEs and Framework for Smart Manufacturing -- 1.1 Introduction -- 1.2 Status of Industry 4.0 Implementation in SMEs -- 1.2.1 Review of Literature on Industry 4.0 Implementation in SMEs -- 1.2.2 Summary on the Adoption of Industry 4.0 Technologies in SMEs -- 1.3 Framework and Guidelines for Smart Manufacturing in SMEs -- 1.3.1 Axiomatic Design Guidelines for Implementing Industry 4.0 in SMEs -- 1.3.2 Framework for Highly Adaptable and Smart Manufacturing in SMEs -- 1.3.3 Three-Stage Model for Implementing Industry 4.0 in SMEs -- 1.4 Industry 4.0+: An Outlook on Future Challenges for SMEs -- References -- 2 Computational Intelligence in the Context of Industry 4.0 -- 2.1 Introduction -- 2.2 Neural Networks -- 2.2.1 Fundamentals of Neural Networks -- 2.2.2 Use of Neural Networks in the Context of Industry 4.0 -- 2.3 Fuzzy Systems -- 2.3.1 Fundamentals of Fuzzy Systems -- 2.3.2 Use of Fuzzy Systems in the Context of Industry 4.0 -- 2.4 Evolutionary Computation -- 2.4.1 Fundamentals of Evolutionary Computation -- 2.4.2 Use of Evolutionary Computation in the Context of Industry 4.0 -- 2.5 Swarm Intelligence -- 2.5.1 Fundamentals of Swarm Intelligence -- 2.5.2 Use of Swarm Intelligence in the Context of Industry 4.0 -- 2.6 Artificial Immune Systems -- 2.6.1 Fundamentals of Artificial Immune Systems -- 2.6.2 Use of Artificial Immune Systems in the Context of Industry 4.0 -- 2.7 Big Data -- 2.8 Deep Learning -- 2.8.1 Fundamentals of Deep Learning -- 2.8.1.1 Autoencoders -- 2.8.1.2 Recurrent Neural Networks (RNN) -- 2.8.1.3 Convolutional Neural Networks (CNN).

2.8.2 Use of Deep Learning in the Context of Industry 4.0 -- 2.9 Use of Computational Intelligence in Cyber-Physical Systems -- 2.10 Case Study: Industrial Parts Recognition by Convolutional Neural Networks for Assisted Assembly -- 2.10.1 Input Samples Generation from 3D Virtual Models -- 2.10.2 Identification of a Region of Interest for Recognition of Small Parts -- 2.10.3 Convolutional Network Transform Learning -- 2.10.4 Implementation into Devices for Assisted Assembly -- 2.10.4.1 Implementation into Embedded Devices -- 2.10.4.2 Implementation to VR/AR Devices -- 2.11 Discussion -- 2.12 Conclusion and Future Prospects -- References -- 3 AI and ML for Human-Robot Cooperation in Intelligent and Flexible Manufacturing -- 3.1 Introduction -- 3.2 Artificial Intelligence and Machine Learning -- 3.2.1 What's Artificial Intelligence? -- 3.2.2 What's Machine Learning? -- 3.2.3 What's the Relation Between Artificial Intelligence and Machine Learning? -- 3.3 Human-Robot Cooperation for Smart Manufacturing -- 3.3.1 CPS and Safety -- 3.3.2 Human-Robot Cooperation in Assembly -- 3.4 Conclusions -- References -- 4 Industrial Assistance Systems to Enhance Human-Machine Interaction and Operator's Capabilities in Assembly -- 4.1 Introduction -- 4.2 Theoretical Background -- 4.2.1 Industrial Assistance Systems -- 4.2.2 User Groups in Production -- 4.2.3 Importance of Human-Machine Interaction in Production -- 4.2.4 Relevance of Assistance Systems in Literature -- 4.3 Overview of Industrial Assistance Systems in Production -- 4.3.1 Sensorial Worker Assistance Systems -- 4.3.2 Physical Worker Assistance Systems -- 4.3.3 Cognitive Worker Assistance Systems -- 4.4 Discussion of Risks, Challenges, and Potential -- 4.5 Conclusion -- References -- PartII Implementing Industry 4.0 for Smart Logistics in SMEs.

5 Investigation of the Potential to Use Real-Time Data in Production Planning and Control of Make to Order (MTO) Manufacturing Companies -- 5.1 Introduction -- 5.2 Problem Formulation -- 5.3 Related Work -- 5.4 Research Design/Methodology -- 5.5 Results and Discussion -- 5.5.1 Research Question 1 (RQ1): Comparative Evaluation of PPC Strategies -- 5.5.2 Research Question 2 (RQ2): Evaluation of Real-Time Data Usage within the PPC Strategies -- 5.6 Conclusions and Outlook -- References -- 6 Readiness Model for Integration of ICT and CPS for SMEs Smart Logistics -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Readiness Model for Integration of ICT and CPS for Smart Logistics -- 6.4 Stages of Readiness -- 6.5 Readiness Process Areas -- 6.6 Conclusion and Outlook -- References -- 7 Automated Performance Measurement in Internal Logistics Systems -- 7.1 Introduction -- 7.2 Problem Formulation -- 7.3 Monitoring and Controlling-Enablers for High-Level Responsiveness and Systematic Planning -- 7.4 State-of-the-Art and Literature Review -- 7.5 Deduction of a Model for Availability and Performance Assessment -- 7.6 Discussion and Further Research Directions -- 7.7 Conclusions -- References -- 8 A Case Study: Industry 4.0 and Human Factors in SMEs -- 8.1 Introduction -- 8.2 Problem Formulation -- 8.3 Related Work -- 8.4 Learning and Learning Culture -- 8.5 A Case of Human Factors in Implementing New Technology -- 8.5.1 The Objective of Investigation: The Company 'Precision Machine Products, Inc.' (PMP) -- 8.5.2 The Project -- 8.5.3 The Human Factor -- 8.6 Conclusions and Outlook -- References -- PartIII Organizational and Management Models for Smart SMEs -- 9 Transition of SMEs Towards Smart Factories: Business Models and Concepts -- 9.1 Introduction -- 9.2 Importance of Systems Approach in Transforming Organizations.

9.3 Transition of SMEs Towards Platform-Based Business Models -- 9.3.1 A Quantitative Analysis of Platform-Based Business Models -- 9.3.2 A Qualitative Analysis of Platform-Based Business Models -- 9.3.3 Typical Features of Platform-Based Business Models -- 9.4 New Work Roles in Industry 4.0 Environment -- 9.5 Conclusions -- References -- 10 Toward SME 4.0: The Impact of Industry 4.0 Technologies on SMEs' Business Models -- 10.1 Introduction -- 10.2 Background -- 10.2.1 Industry 4.0 -- 10.2.2 Business Model -- 10.2.3 Small- and Medium-Sized Enterprises -- 10.3 Methodology -- 10.3.1 Literature Review Methodology -- 10.3.2 Contingency Analysis of the Literature Review Findings -- 10.3.3 Secondary Data Analysis Methodology -- 10.4 Results -- 10.4.1 Content Analysis of the Reviewed Papers -- 10.4.1.1 Overarching Trends in the Reviewed Papers -- 10.4.1.2 Business Model Building Blocks Modified by Industry 4.0 Implementation -- 10.4.1.3 Contingency Analysis of Industry 4.0 Technologies and Business Model Building Blocks -- 10.4.2 Secondary Data Analysis -- 10.5 Discussion and Conclusion -- Appendix I: 30 Sample SMEs -- References -- 11 General Assessment of Industry 4.0 Awareness in South India-A Precondition for Efficient Organization Models? -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Problem Description -- 11.4 Methodology -- 11.5 Results and Discussion -- 11.5.1 General Awareness, Age and Education -- 11.5.2 Expectations of Importance for SMEs -- 11.5.3 Living Conditions Effects Expectations -- 11.6 Conclusions -- References -- 12 Implementation Strategies for SME 4.0: Insights on Thailand -- 12.1 Introduction -- 12.2 Implementation Strategies for SMEs -- 12.2.1 Phase 1-Analysis -- 12.2.2 Phase 2-Development Plan -- 12.2.3 Phase 3-Implementation Strategies -- 12.3 Industry 4.0 Implementation in Thailand -- 12.4 Case Study-Thai Agritech SME.

12.4.1 Business Idea of Agritech -- 12.4.2 Plant Factory-The Foresight of Agritech Business -- 12.4.3 Technology Blueprint Development-Plant Factory -- 12.4.4 Requirement of New Skills-Addressing SME 4.0 -- 12.4.5 Implementation Strategies for SME 4.0 -- 12.5 Discussion -- References -- Index.