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For Authors
Submit a ProposalKnowledge Management and Industrial Revolution 4.0
 | Edited by Rajendra Kumar, Vishal Jain, Venus C. Ibarra, Corrienna Abdul Talib and Vinay Kukreja
Copyright: 2024 | Expected Pub Date:2024/07/30 ISBN: 9781394242610 | Hardcover | 442 pages |
One Line Description
Get up to speed with how the latest knowledge management and industry 4.0 technology help make enterprises secure, controlled, and optimized for a better future.
Audience
The book will have a wide audience within academia, education, businesses, and industrial organizations, especially those who are undergoing industry 4.0 changes to optimize for a better future.
The book will have a wide audience within academia, education, businesses, and industrial organizations, especially those who are undergoing industry 4.0 changes to optimize for a better future.
Description
This book focuses on how the practices of Industrial Revolution 4.0 and knowledge management interact to create value. In recent years, value chain relationships and related activities have utilized new technologies so that existing conceptual frameworks require a roadmap for innovation strategies and effective implementation. The chapters in this book include case studies contributed by researchers and industry practitioners that showcase the impact of practices and challenges presented by technological changes, upgrading of old systems, and internal and external factors.
Knowledge Management and Industrial Revolution 4.0 describes how knowledge management impacts the automation of the industry in secure, controlled, and optimized ways. For instance, the use of the latest technologies and sensors can lead to significant time and cost savings, and operators can utilize their machines and equipment from remote locations. The Industrial Revolution 4.0 incorporates the latest technologies for automation and, in many cases, the result is similar to working from home, even in manufacturing.
The use of deep learning should offer many quality control benefits. Furthermore, blockchain technology can help the industry with automation in secure and transparent ways. Apart from industry automation, other departments like human resources can also use effective knowledge management for better outcomes. The use of HR knowledge management allows employees to find and access the information they require without the assistance of the HR department.
The book focuses on every aspect of the industry to help all the stakeholders of an organization. The benefits include a reduction in time required for accessing information, easier training, decreased operational expenses, improved stakeholders’ satisfaction, faster problem-solving, increased pace of innovation, simpler employee review and progress reports
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This book focuses on how the practices of Industrial Revolution 4.0 and knowledge management interact to create value. In recent years, value chain relationships and related activities have utilized new technologies so that existing conceptual frameworks require a roadmap for innovation strategies and effective implementation. The chapters in this book include case studies contributed by researchers and industry practitioners that showcase the impact of practices and challenges presented by technological changes, upgrading of old systems, and internal and external factors.
Knowledge Management and Industrial Revolution 4.0 describes how knowledge management impacts the automation of the industry in secure, controlled, and optimized ways. For instance, the use of the latest technologies and sensors can lead to significant time and cost savings, and operators can utilize their machines and equipment from remote locations. The Industrial Revolution 4.0 incorporates the latest technologies for automation and, in many cases, the result is similar to working from home, even in manufacturing.
The use of deep learning should offer many quality control benefits. Furthermore, blockchain technology can help the industry with automation in secure and transparent ways. Apart from industry automation, other departments like human resources can also use effective knowledge management for better outcomes. The use of HR knowledge management allows employees to find and access the information they require without the assistance of the HR department.
The book focuses on every aspect of the industry to help all the stakeholders of an organization. The benefits include a reduction in time required for accessing information, easier training, decreased operational expenses, improved stakeholders’ satisfaction, faster problem-solving, increased pace of innovation, simpler employee review and progress reports
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Author / Editor Details
Rajendra Kumar, PhD, is an associate professor in the Department of Computer Science & Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India. He has published 5 textbooks and about 35 papers in national and international journals and conferences.
Rajendra Kumar, PhD, is an associate professor in the Department of Computer Science & Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India. He has published 5 textbooks and about 35 papers in national and international journals and conferences.
Vishal Jain, PhD, is an associate professor at Sharda University, Greater Noida, India. His research interests focus on information retrieval, the semantic web, ontology engineering, data mining, etc. He has edited 50 books for a variety of publishers and authored 95 research papers for reputed conferences and journals. Jain has several awards, which include the 2012 Young Active Member Award and the 2019 Best Researcher Award.
Venus C. Ibarra, MD, DBA, works as the dean of the MBA/DBA Department of San Pablo Colleges, Philippines. Ibarra also works as a lecturer at the Ateneo Graduate School of Business in management accounting. Ibarra earned her master’s degree in business administration at the Philippines Women’s University. She has professional experience in administration, finance, budgeting, and statistical functions. In 2009, Ibarra was recognized as an outstanding finance educator by CITI NBank and Bank Central in the Philippines.
Corrienna Abdul Talib, PhD, is a senior lecturer in the Department of Educational Science, Mathematics, and Creative Multimedia, Universiti Teknologi, Malaysia. She is a certified computational thinking master trainer who designs professional skill courses at UTM and researches and leads projects on computational skills, microscale chemistry, visual programming language, and educational robotic and game-based learning.
Vinay Kukreja, PhD, is a professor of computer science & engineering at Chitkara University, Punjab, India. His research interests primarily focus on machine learning, deep learning, agile software development, image processing, etc. He has about 90 articles indexed in Scopus and has authored 3 books. In 2018, Kukreja received the first prize in the SIH Hackathon from the Ministry of Housing & Urban Affairs, India.
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Table of Contents
Preface
Acknowledgement
1. The 5G Technology IR4 and Knowledge Management
Vyshali Rao K. P., Shanthi M. B. and Sudhakar K. N.
1.1 Introduction
1.2 5G Technology Architecture
1.3 Technology Features of 5G
1.4 Applications of 5G Technology in Industry 4.0 (4IR)
1.5 Knowledge Management in Industry 4.0
1.5.1 Architecture of Knowledge Management
1.6 The Role of 5G Technology in Knowledge Management
1.7 Challenges of Implementing 5G Technology for Knowledge Management
1.8 Future Trends and Developments in 5G Technology and Knowledge Management
References
2. Impact of Knowledge Management on Industry 4.0
Amala J. and Anitha K.
2.1 Introduction
2.1.1 Concept of KM
2.1.2 Definitions
2.2 State of Art
2.2.1 Industry 4.0
2.2.2 Knowledge Management
2.3 Models of Knowledge Management
2.3.1 Von Krogh and Roos Model
2.3.2 Nonaka and Takeuchi Model
2.3.3 Choo Sense-Making KM Model
2.3.4 WIIGS KM Model
2.3.5 Boisot I-Space
2.3.6 ZACK Knowledge Management
2.3.7 Complex Adaptive System Models
2.4 Proposed Model for Industry 4.0
2.5 Conclusion
References
3. Synergizing Knowledge Management in the Era of Industry 4.0: A Technological Revolution for Organizational Excellence
Anita Mohanty, Bright Keswani, Subrat Kumar Mohanty, Ambarish G. Mohapatra, Sasmita Nayak and Md Mobin Akhtar
3.1 Introduction
3.1.1 The Foundation of Knowledge Management
3.1.2 Defining Knowledge Management
3.1.3 Overview of the Industrial Revolution(s)
3.1.4 The Importance of the Relationship Between Knowledge and Industrial Progress
3.2 Historical Perspective
3.2.1 Brief Overview of the First, Second, and Third Industrial Revolutions
3.2.2 The Role of Knowledge and Innovation in Each Revolution
3.2.3 Lessons Erudite from Historical Industrial Revolutions
3.3 Knowledge Management in the Fourth Industrial Revolution (Industry 4.0)
3.3.1 Introduction to Industry 4.0 and Its Key Characteristics
3.3.2 The Data-Driven Nature of Industry 4.0
3.3.3 The Role of Digital Technology, IoT, and AI in Industry 4.0
3.3.4 The Explosion of Data and Information in the Digital Age
3.4 The Intersection of Knowledge and Technology
3.4.1 How Technology Facilitates Knowledge Management
3.4.2 Knowledge Management Tools and Platforms in the Digital Age
3.4.3 Data Analytics and Artificial Intelligence in Knowledge Management
3.5 Knowledge Creation and Capture
3.5.1 Strategies to Foster Knowledge Creation and Capture in the Industrial Context
3.5.2 The Vital Role of Innovation and R&D in Knowledge Generation
3.5.3 Effective Approaches for Documenting and Safeguarding Knowledge
3.6 Facilitating Knowledge Sharing and Dissemination
3.6.1 Prioritizing Intra-Organizational Knowledge Sharing
3.6.2 Utilizing Collaborative Tools and Platforms for Enhanced Knowledge Exchange
3.6.3 Overcoming Challenges and Implementing Solutions in Knowledge Distribution
3.7 Knowledge Transfer and Training Initiative
3.7.1 Addressing the Skills Gap in the Fourth Industrial Revolution Era
3.7.2 Knowledge Transfer from Seasoned Professionals to New Recruits
3.7.3 Training and Development Programs Tailored for Knowledge-Intensive Industries
3.8 Strategic Knowledge Management for Fostering Innovation
3.8.1 The Catalytic Role of Knowledge Management in Fueling Innovation Within the Context of Industry 4.0
3.8.2 Illustrative Case Studies of Organizations Leveraging Knowledge to Drive
3.8.3 Cultivating an Innovative Culture Through Proficient Knowledge Management Practices
3.8.4 Knowledge Management Challenges and Solutions
3.9 Common Challenges in Managing Knowledge During Industrial Revolutions
3.9.1 Solutions and Best Practices for Overcoming Knowledge Management Obstacles
3.9.2 The Role of Leadership and Organizational Culture in Addressing Challenges
3.10 Conclusion
3.10.1 Ongoing Importance of Knowledge Management in the Face of Industrial Revolutions
3.10.2 The Potential Impact of Future Industrial Revolutions on Knowledge Management
3.10.3 Final Thoughts and Recommendations for Organizations
References
4. Improving Manufacturing: Organizational Innovation Through Effective Knowledge Management, A McElroy Knowledge Life Cycle Approach
Arpita Nayak, Atmika Patnaik, Ipseeta Satpathy, Vishal Jain, B.C.M. Patnaik and Majidul Islam
4.1 Introduction
4.2 Unleashing the Innovation Possibilities: The Effectiveness of Knowledge Creation in Manufacturing Organizations
4.3 Leveraging Manufacturing Industries’ Potential Through Efficient Knowledge Capture
4.4 Knowledge Sharing as a Tool for Innovation in Manufacturing Organization
4.5 From Theories to Action: The Impact of Knowledge Application in Manufacturing Industries
4.6 Knowledge Management Practices in Global Manufacturing Organizations: Boosting Innovation and Performance
4.7 Conclusion
References
5. Industry 4.0 Trends and Strategies: A Modern Approach with Focus on Knowledge Management
Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Sachin Chaudhary and Rajneesh Panwar
5.1 Introduction
5.1.1 Background and Significance
5.1.2 Scope and Objectives
5.2 Understanding Industry 4.0
5.2.1 Evolution of Industrial Revolutions
5.2.2 Key Concepts of Industry 4.0
5.2.3 Enabling Technologies in Industry 4.0
5.3 Literature Review
5.4 Emerging Trends in Industry 4.0
5.4.1 Digital Transformation and Connectivity
5.4.2 Integration of Cyber and Physical Systems
5.4.3 Data Governance and Security Measures 1
5.4.4 Workforce Training and Skill Development
5.4.5 Overcoming Implementation Challenges
5.5 Knowledge Management in Industry 4.0
5.5.1 Role of Knowledge in Industry 4.0
5.5.2 Knowledge Acquisition and Creation
5.5.3 Knowledge Sharing and Collaboration
5.5.4 Knowledge Storage and Retrieval
5.6 Leveraging Knowledge Management for Successful Industry 4.0 Implementation
5.6.1 Enhancing Decision Making with Knowledge Insights
5.6.2 Improving Process Efficiency Through Knowledge Integration
5.6.3 Enabling Continuous Learning and Innovation
5.6.4 Mitigating Risks and Enhancing Adaptability
5.7 Case Studies: Industry 4.0 Implementation with Knowledge Management
5.7.1 Manufacturing Sector Case Study
5.7.2 Supply Chain Optimization Case Study
5.7.3 Service Industry Transformation Case Study
5.8 Future Directions and Challenges
5.8.1 Evolving Trends in Industry 4.0
5.8.2 Opportunities for Further Knowledge Management Integration
5.8.3 Addressing Ethical and Security Concerns
5.8.4 Regulatory and Policy Implications
5.9 Conclusion
5.9.1 Recap of Key Findings
5.9.2 Implications for Industries and Knowledge Management
5.9.3 Prospects for Future Research and Development
References
6. Artificial Intelligence on Knowledge Management and Industry Revolution 4.0: Impacts and Challenges
Sridevi and Tukkappa K. Gundoor
6.1 Introduction
6.2 Background of the Study
6.3 Industry 4.0 Revolution
6.4 Technology is Driving Industry 4.0
6.4.1 The Internet of Things (IoT)
6.4.2 AI and ML
6.4.3 Big Data Analytics
6.4.4 Automation and Robotics
6.4.5 Cloud Computing
6.4.6 AR and VR
6.4.7 Cybersecurity and Data Privacy
6.4.8 Additive Manufacturing (3D Printing)
6.4.9 Blockchain
6.5 Role of AI in KM and IR 4.0
6.5.1 Extraction and Discovery of Knowledge
6.5.2 Knowledge Retrieval and Skillful Search
6.5.3 Decision Support and Cognitive Computing
6.5.4 Virtual Assistants and Chatbots
6.5.5 Using Machine Learning to Improve Knowledge
6.5.6 Knowledge Management and Predictive Analytics
6.5.7 Robotic Process Automation (RPA) and Automation
6.6 The Effects of AI on IR 4.0 and KM
6.6.1 Enhanced Application and Acquisition of Knowledge
6.6.2 Improved Search and Retrieval of Information
6.6.3 Enhanced Decision Making and Cognitive Support
6.6.4 Personalized Knowledge Delivery
6.6.5 Automation of Knowledge-Intensive Processes
6.6.6 Improved Knowledge Creation and Innovation
6.6.7 Proactive Knowledge Management
6.6.8 Enhanced Collaboration and Knowledge Sharing
6.7 Key Challenges of AI in KM and IR 4.0
6.7.1 Quality and Availability of Data
6.7.2 Ethics and Privacy Concerns
6.7.3 Insufficient Skill and Knowledge
6.7.4 Interoperability and Integration
6.7.5 Trust and Explainability
6.7.6 Cultural Adoption and Change Management
6.7.7 Fairness and Bias
6.7.8 Cost and Scalability
6.8 Limitation of the IR 4.0 and KM
6.8.1 Difficulties with Technological Complexity and Integration
6.8.2 Investment Cost and Return
6.8.3 Workplace Skill Gaps and Readiness
6.8.4 Privacy and Data Security Concerns
6.8.5 Bias and Ethical Considerations
6.8.6 Cultural and Organizational Difficulties
6.8.7 Insufficient Human Engagement and Proficiency
6.8.8 Error and Unpredictability
6.9 Future Direction
6.9.1 Better Integration of AI with KM
6.9.2 Giving Knowledge Analytics and Insights Priority
6.9.3 Collaboration and the Management of Social Knowledge
6.9.4 Integrating New Technologies with Knowledge Management
6.9.5 Prioritizing Knowledge Ethics and Bias Mitigation
6.9.6 Adaptive Education and Customized Information Dissemination
6.9.7 Assessing Knowledge Ecosystems and Open Knowledge
6.9.8 Agile Knowledge Management and Ongoing Education
6.10 Research Challenges
6.10.1 The Acquisition and Extraction of Knowledge
6.10.2 Knowledge Structure and Representation
6.10.3 Collaboration and the Sharing of Knowledge
6.10.4 Validation of Knowledge and Quality Control
6.10.5 Consequences for Law and Ethics
6.10.6 Human–Machine Interaction in KM
6.10.7 Management of Change and Culture in Organizations
6.10.8 Measuring and Assessing the Impact of KM
6.10.9 Sustainability and Scalability
6.11 Conclusion
References
7. Blending of Knowledge Management with Industry 4.0: A New Formula for Success!
Kavitha R. Gowda, Sunanda Vincent Jaiwant, Joseph Varghese Kureethara and Jayanta Banerjee
7.1 Introduction
7.1.1 Knowledge Management
7.1.2 Industry 4.0
7.2 History of Industry 4.0
7.2.1 Opportunities and Benefits of Industry 4.0
7.2.2 Challenges of Industry 4.0 and Knowledge Management
7.3 Objectives of the Chapter
7.3.1 To Examine the Impact of Technology on Knowledge Creation Among Employees
7.3.2 To Observe the Knowledge Sharing and Prospect of Industry 4.0
7.3.3 To Analyze the Relationship of Knowledge Management with Industry 4.0 7.3.4 To Understand the Benefits of Blending of Knowledge Management with Industry 4.0
7.4 Conclusion
References
8. Modern Approaches and Implications Toward Industry 4.0
Bishnu Kant Shukla, Amit Tripathi, Gaurav Bharti, Bhupender Parashar, Nitin Bhardwaj, Aakash Gupta and Shivam Verma
8.1 Introduction
8.1.1 Background of Industry 4.0
8.1.2 Significance of Modern Approaches
8.1.3 Objective of the Study
8.2 Literature Review
8.2.1 Evolution of Industry 4.0
8.2.2 Previous Studies on Modern Approaches
8.2.3 Gaps in Current Literature
8.3 Modern Methodologies Supporting Industry 4.0
8.3.1 Cyber-Physical Systems (CPS)
8.3.1.1 Definition and Characteristics
8.3.1.2 Application in Industry
8.3.2 Industrial Internet of Things (IIoT)
8.3.2.1 Components and Structure
8.3.2.2 Role in Smart Manufacturing
8.3.3 Advanced Analytics
8.3.3.1 Importance in Data Processing
8.3.3.2 Case Studies
8.3.4 Machine Learning (ML)
8.3.4.1 Algorithms and Application
8.3.4.2 Role in Predictive Maintenance
8.3.5 Robotics
8.3.5.1 Current Innovations
8.3.5.2 Robotics in Modern Production Lines
8.3.6 Additive Manufacturing
8.3.6.1 Types and Materials
8.3.6.2 Benefits in Customization and Production
8.4 Implications of Adopting Industry 4.0 Practices
8.4.1 Workforce Adaptation
8.4.1.1 Training and Skill Enhancement
8.4.1.2 Job Roles and Evolution
8.4.2 Cybersecurity
8.4.2.1 Risks and Challenges
8.4.2.2 Best Practices in Cybersecurity for Industry 4.0
8.4.3 Return on Investment
8.4.3.1 Cost Analysis
8.4.3.2 Long-Term Benefits
8.5 Cross-Sector Collaboration
8.5.1 Role of Academic Institutions
8.5.2 Industry Stakeholders and Contributions
8.5.3 Policymaking and Regulatory Landscape
8.6 Strategies for Successful Adoption
8.6.1 Standardized Frameworks and Guidelines
8.6.1.1 Importance of Interoperability
8.6.1.2 Security Measures
8.6.1.3 Scalability Considerations
8.6.2 Continuous Improvement and Adaptability
8.6.3 Case Studies and Real-World Applications
8.7 Future Prospects and Research Directions
8.7.1 Emerging Technologies in Industry 4.0
8.7.2 Challenges and Opportunities
8.7.3 Potential Growth Areas and Innovations
8.7.3.1 Personalized Manufacturing
8.7.3.2 Augmented Workforce
8.7.3.3 Circular Economy
8.8 Conclusion
References
9. Managing Knowledge in the Era of Industry 4.0: Challenges and Strategies
Pawan Whig, Jhansi Bharathi Madavarapu, Nikhitha Yathiraju and Ramya Thatikonda
9.1 Introduction
9.2 Literature Review
9.3 Impacts of Industry 4.0
9.4 Importance of Knowledge Management in the Context of Industry 4.0
9.5 Knowledge Management
9.6 Overview of Industry 4.0 and Technologies
9.7 Implications of Industry 4.0 for Knowledge Management
9.8 Role of Artificial Intelligence, Big Data, and IoT in Knowledge Management
9.9 Challenges of Knowledge Management in Industry 4.0
9.10 Strategies for Effective Knowledge Management
9.11 Best Practices for Knowledge Management in Industry 4.0
9.12 Strategies for Leveraging Technology for Effective Knowledge Management
9.13 Case Studies of Successful Knowledge Management in Industry 4.0
9.14 Result
9.15 Discussion
9.16 Conclusion
9.17 Future Directions
References
10. Enhance Knowledge Management in Industry 4.0
Ambika N.
10.1 Introduction
10.2 Background
10.3 Literature Survey
10.4 Previous Study
10.5 Case Study
10.6 Analysis of the Work
10.7 Challenges of Data Analytics
10.8 Future Scope
10.9 Conclusion
References
11. Industrialized Control and Automation System (ICAS): A Software-Defined Analysis Framework for Industry 4.0
Karthikeyan V., Dani Reagan Vivek J., Gopalakrishnan K. and Ezhil Kalaimannan
11.1 Introduction
11.1.1 Industry Fourth-Generation Technologies
11.1.1.1 The Industrial IoT (IIoT)
11.1.1.2 Automation
11.1.1.3 AI—Artificial Intelligence
11.1.1.4 Big Data (BD) and Analytics
11.1.1.5 The Cloud
11.1.1.6 Cybersecurity
11.1.2 Business Drivers of i4.0
11.1.3 Industrial Internet
11.2 Related Works
11.2.1 Technologies
11.2.2 Applications
11.2.3 Management Innovation
11.3 Healthcare Applications in Industries
11.3.1 Major Challenges Associated with Healthcare
11.3.2 IoT-Based Smart Healthcare System
11.3.3 Advanced Technologies Used in Healthcare
11.3.4 Open Research Issues to be Addressed
11.4 Inventory Management and Quality Control
11.4.1 Inventory Management (IM)
11.4.2 Inventory
11.4.3 Types of Inventory Management
11.4.4 Inventory Management and IIoT
11.4.5 Open Research Issues to be Addressed
11.5 Analysis of a Machine Learning Algorithm to Predict Wine Quality
11.5.1 Objectives
11.5.2 Conventional Schemes for Wine Quality Assessment (WQA)
11.5.3 Proposed Autonomous ML Scheme-Based WQA
11.5.4 Open Research Issues to be Addressed
11.6 Conclusion and Future Directions
References
12. Structural Understanding of the Relationship Between Various Consciousness of Programming and Creative Attitudes as Part of Knowledge Management Process
Masanori Fukui, Eng Tek Ong, Khar Thoe Ng, Yoon Fah Lay and Subuh Anggoro
12.1 Introduction
12.1.1 The Purpose of this Study
12.1.2 Research Background
12.1.3 Identification of Problems
12.2 Method
12.2.1 Survey Targets and Procedures
12.2.2 Survey Items
12.3 Analysis Procedure
12.4 Results
12.4.1 Preliminary Analysis
12.4.2 Descriptive Statistics and Results of Correlation Analysis
12.4.3 Result of Covariance Structural Analysis of All Factors and Items
12.5 Discussion
12.6 Conclusion
12.6.1 Summary of this Research
12.6.2 Limitations and Reflections for Future Study
Acknowledgments
Note
References
13. Blended-Mode Instruction for Knowledge Management Toward IR4.0: Exemplars in Lifelong STREAM Education and The Way Forward
Kamolrat Intaratat, Guan Xing Zhi, Subuh Anggoro, Khar Thoe Ng and Dungkamol Intaratat
13.1 Introduction
13.1.1 Background/Overview of Literacy and Technology- Enhanced Knowledge Management (KM) Platforms
13.1.2 Problem Statement and Rationale
13.1.3 Research Objectives and Methodologies Implemented
13.2 Analysis of Findings and Illustrations of Case Exemplars
13.2.1 Operational Definitions of BMI, KM in STREAM Education Toward IR4.0 Reflecting SDGs (RO1)
13.2.1.1 Blended-Mode Instruction (BMI)
13.2.1.2 Knowledge Management (KM) and its Roles in STREAM Education
for IR4.0 in Line with SDGs
13.2.2 Exemplars on How BMI Facilitated Lifelong Learning with KM Considering Local Wisdom and Literacy (RO2)
13.2.2.1 Case Exemplar(s) on How BMI Facilitated Lifelong Learning Considering Literacy with KM
13.2.2.2 Case Exemplar(s) on How BMI Facilitated Lifelong Learning Considering Local Wisdom with KM
13.3 Conclusion
13.3.1 Summary and Implications/Significance
13.3.2 Limitations and Recommendations
Acknowledgments
References
14. Insight Review on Advanced Digital Manufacturing Technology Solutions for Industry 4.0
D. David Neels Ponkumar, K. Saravanan, Riboy Cheriyan and Chinnadurai Manthiramoorthy
14.1 Introduction
14.2 Digitization of Manufacturing Sectors
14.3 Knowledge Management Practices Adopted in Manufacturing
14.4 Challenges in Digitization of Industry 4.0
14.5 Key Factors in Addressing Obstacles and Hindrances
14.6 Conclusion
References
15. Enhancing Students’ Learning Achievement, 21st-Century Skills, and Self-Regulation Skills—Knowledge Management and Education 4.0 Perspective
Maria Cindy F. Cardona, Amelia T. Buan, Ellen D. Inutan and Rajendra Kumar
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.3.1 The Model
15.4 Participants
15.5 Research Instruments
15.6 Data Collection
15.7 Data Analysis
15.8 Results and Discussion
15.8.1 Performance of Students in Achievement Tests
15.8.2 Performance of Students in Terms of the 21st-Century Skills and Self-Regulation Skills
15.9 Conclusion
References
Index
Back to Top
Preface
Acknowledgement
1. The 5G Technology IR4 and Knowledge Management
Vyshali Rao K. P., Shanthi M. B. and Sudhakar K. N.
1.1 Introduction
1.2 5G Technology Architecture
1.3 Technology Features of 5G
1.4 Applications of 5G Technology in Industry 4.0 (4IR)
1.5 Knowledge Management in Industry 4.0
1.5.1 Architecture of Knowledge Management
1.6 The Role of 5G Technology in Knowledge Management
1.7 Challenges of Implementing 5G Technology for Knowledge Management
1.8 Future Trends and Developments in 5G Technology and Knowledge Management
References
2. Impact of Knowledge Management on Industry 4.0
Amala J. and Anitha K.
2.1 Introduction
2.1.1 Concept of KM
2.1.2 Definitions
2.2 State of Art
2.2.1 Industry 4.0
2.2.2 Knowledge Management
2.3 Models of Knowledge Management
2.3.1 Von Krogh and Roos Model
2.3.2 Nonaka and Takeuchi Model
2.3.3 Choo Sense-Making KM Model
2.3.4 WIIGS KM Model
2.3.5 Boisot I-Space
2.3.6 ZACK Knowledge Management
2.3.7 Complex Adaptive System Models
2.4 Proposed Model for Industry 4.0
2.5 Conclusion
References
3. Synergizing Knowledge Management in the Era of Industry 4.0: A Technological Revolution for Organizational Excellence
Anita Mohanty, Bright Keswani, Subrat Kumar Mohanty, Ambarish G. Mohapatra, Sasmita Nayak and Md Mobin Akhtar
3.1 Introduction
3.1.1 The Foundation of Knowledge Management
3.1.2 Defining Knowledge Management
3.1.3 Overview of the Industrial Revolution(s)
3.1.4 The Importance of the Relationship Between Knowledge and Industrial Progress
3.2 Historical Perspective
3.2.1 Brief Overview of the First, Second, and Third Industrial Revolutions
3.2.2 The Role of Knowledge and Innovation in Each Revolution
3.2.3 Lessons Erudite from Historical Industrial Revolutions
3.3 Knowledge Management in the Fourth Industrial Revolution (Industry 4.0)
3.3.1 Introduction to Industry 4.0 and Its Key Characteristics
3.3.2 The Data-Driven Nature of Industry 4.0
3.3.3 The Role of Digital Technology, IoT, and AI in Industry 4.0
3.3.4 The Explosion of Data and Information in the Digital Age
3.4 The Intersection of Knowledge and Technology
3.4.1 How Technology Facilitates Knowledge Management
3.4.2 Knowledge Management Tools and Platforms in the Digital Age
3.4.3 Data Analytics and Artificial Intelligence in Knowledge Management
3.5 Knowledge Creation and Capture
3.5.1 Strategies to Foster Knowledge Creation and Capture in the Industrial Context
3.5.2 The Vital Role of Innovation and R&D in Knowledge Generation
3.5.3 Effective Approaches for Documenting and Safeguarding Knowledge
3.6 Facilitating Knowledge Sharing and Dissemination
3.6.1 Prioritizing Intra-Organizational Knowledge Sharing
3.6.2 Utilizing Collaborative Tools and Platforms for Enhanced Knowledge Exchange
3.6.3 Overcoming Challenges and Implementing Solutions in Knowledge Distribution
3.7 Knowledge Transfer and Training Initiative
3.7.1 Addressing the Skills Gap in the Fourth Industrial Revolution Era
3.7.2 Knowledge Transfer from Seasoned Professionals to New Recruits
3.7.3 Training and Development Programs Tailored for Knowledge-Intensive Industries
3.8 Strategic Knowledge Management for Fostering Innovation
3.8.1 The Catalytic Role of Knowledge Management in Fueling Innovation Within the Context of Industry 4.0
3.8.2 Illustrative Case Studies of Organizations Leveraging Knowledge to Drive
3.8.3 Cultivating an Innovative Culture Through Proficient Knowledge Management Practices
3.8.4 Knowledge Management Challenges and Solutions
3.9 Common Challenges in Managing Knowledge During Industrial Revolutions
3.9.1 Solutions and Best Practices for Overcoming Knowledge Management Obstacles
3.9.2 The Role of Leadership and Organizational Culture in Addressing Challenges
3.10 Conclusion
3.10.1 Ongoing Importance of Knowledge Management in the Face of Industrial Revolutions
3.10.2 The Potential Impact of Future Industrial Revolutions on Knowledge Management
3.10.3 Final Thoughts and Recommendations for Organizations
References
4. Improving Manufacturing: Organizational Innovation Through Effective Knowledge Management, A McElroy Knowledge Life Cycle Approach
Arpita Nayak, Atmika Patnaik, Ipseeta Satpathy, Vishal Jain, B.C.M. Patnaik and Majidul Islam
4.1 Introduction
4.2 Unleashing the Innovation Possibilities: The Effectiveness of Knowledge Creation in Manufacturing Organizations
4.3 Leveraging Manufacturing Industries’ Potential Through Efficient Knowledge Capture
4.4 Knowledge Sharing as a Tool for Innovation in Manufacturing Organization
4.5 From Theories to Action: The Impact of Knowledge Application in Manufacturing Industries
4.6 Knowledge Management Practices in Global Manufacturing Organizations: Boosting Innovation and Performance
4.7 Conclusion
References
5. Industry 4.0 Trends and Strategies: A Modern Approach with Focus on Knowledge Management
Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Sachin Chaudhary and Rajneesh Panwar
5.1 Introduction
5.1.1 Background and Significance
5.1.2 Scope and Objectives
5.2 Understanding Industry 4.0
5.2.1 Evolution of Industrial Revolutions
5.2.2 Key Concepts of Industry 4.0
5.2.3 Enabling Technologies in Industry 4.0
5.3 Literature Review
5.4 Emerging Trends in Industry 4.0
5.4.1 Digital Transformation and Connectivity
5.4.2 Integration of Cyber and Physical Systems
5.4.3 Data Governance and Security Measures 1
5.4.4 Workforce Training and Skill Development
5.4.5 Overcoming Implementation Challenges
5.5 Knowledge Management in Industry 4.0
5.5.1 Role of Knowledge in Industry 4.0
5.5.2 Knowledge Acquisition and Creation
5.5.3 Knowledge Sharing and Collaboration
5.5.4 Knowledge Storage and Retrieval
5.6 Leveraging Knowledge Management for Successful Industry 4.0 Implementation
5.6.1 Enhancing Decision Making with Knowledge Insights
5.6.2 Improving Process Efficiency Through Knowledge Integration
5.6.3 Enabling Continuous Learning and Innovation
5.6.4 Mitigating Risks and Enhancing Adaptability
5.7 Case Studies: Industry 4.0 Implementation with Knowledge Management
5.7.1 Manufacturing Sector Case Study
5.7.2 Supply Chain Optimization Case Study
5.7.3 Service Industry Transformation Case Study
5.8 Future Directions and Challenges
5.8.1 Evolving Trends in Industry 4.0
5.8.2 Opportunities for Further Knowledge Management Integration
5.8.3 Addressing Ethical and Security Concerns
5.8.4 Regulatory and Policy Implications
5.9 Conclusion
5.9.1 Recap of Key Findings
5.9.2 Implications for Industries and Knowledge Management
5.9.3 Prospects for Future Research and Development
References
6. Artificial Intelligence on Knowledge Management and Industry Revolution 4.0: Impacts and Challenges
Sridevi and Tukkappa K. Gundoor
6.1 Introduction
6.2 Background of the Study
6.3 Industry 4.0 Revolution
6.4 Technology is Driving Industry 4.0
6.4.1 The Internet of Things (IoT)
6.4.2 AI and ML
6.4.3 Big Data Analytics
6.4.4 Automation and Robotics
6.4.5 Cloud Computing
6.4.6 AR and VR
6.4.7 Cybersecurity and Data Privacy
6.4.8 Additive Manufacturing (3D Printing)
6.4.9 Blockchain
6.5 Role of AI in KM and IR 4.0
6.5.1 Extraction and Discovery of Knowledge
6.5.2 Knowledge Retrieval and Skillful Search
6.5.3 Decision Support and Cognitive Computing
6.5.4 Virtual Assistants and Chatbots
6.5.5 Using Machine Learning to Improve Knowledge
6.5.6 Knowledge Management and Predictive Analytics
6.5.7 Robotic Process Automation (RPA) and Automation
6.6 The Effects of AI on IR 4.0 and KM
6.6.1 Enhanced Application and Acquisition of Knowledge
6.6.2 Improved Search and Retrieval of Information
6.6.3 Enhanced Decision Making and Cognitive Support
6.6.4 Personalized Knowledge Delivery
6.6.5 Automation of Knowledge-Intensive Processes
6.6.6 Improved Knowledge Creation and Innovation
6.6.7 Proactive Knowledge Management
6.6.8 Enhanced Collaboration and Knowledge Sharing
6.7 Key Challenges of AI in KM and IR 4.0
6.7.1 Quality and Availability of Data
6.7.2 Ethics and Privacy Concerns
6.7.3 Insufficient Skill and Knowledge
6.7.4 Interoperability and Integration
6.7.5 Trust and Explainability
6.7.6 Cultural Adoption and Change Management
6.7.7 Fairness and Bias
6.7.8 Cost and Scalability
6.8 Limitation of the IR 4.0 and KM
6.8.1 Difficulties with Technological Complexity and Integration
6.8.2 Investment Cost and Return
6.8.3 Workplace Skill Gaps and Readiness
6.8.4 Privacy and Data Security Concerns
6.8.5 Bias and Ethical Considerations
6.8.6 Cultural and Organizational Difficulties
6.8.7 Insufficient Human Engagement and Proficiency
6.8.8 Error and Unpredictability
6.9 Future Direction
6.9.1 Better Integration of AI with KM
6.9.2 Giving Knowledge Analytics and Insights Priority
6.9.3 Collaboration and the Management of Social Knowledge
6.9.4 Integrating New Technologies with Knowledge Management
6.9.5 Prioritizing Knowledge Ethics and Bias Mitigation
6.9.6 Adaptive Education and Customized Information Dissemination
6.9.7 Assessing Knowledge Ecosystems and Open Knowledge
6.9.8 Agile Knowledge Management and Ongoing Education
6.10 Research Challenges
6.10.1 The Acquisition and Extraction of Knowledge
6.10.2 Knowledge Structure and Representation
6.10.3 Collaboration and the Sharing of Knowledge
6.10.4 Validation of Knowledge and Quality Control
6.10.5 Consequences for Law and Ethics
6.10.6 Human–Machine Interaction in KM
6.10.7 Management of Change and Culture in Organizations
6.10.8 Measuring and Assessing the Impact of KM
6.10.9 Sustainability and Scalability
6.11 Conclusion
References
7. Blending of Knowledge Management with Industry 4.0: A New Formula for Success!
Kavitha R. Gowda, Sunanda Vincent Jaiwant, Joseph Varghese Kureethara and Jayanta Banerjee
7.1 Introduction
7.1.1 Knowledge Management
7.1.2 Industry 4.0
7.2 History of Industry 4.0
7.2.1 Opportunities and Benefits of Industry 4.0
7.2.2 Challenges of Industry 4.0 and Knowledge Management
7.3 Objectives of the Chapter
7.3.1 To Examine the Impact of Technology on Knowledge Creation Among Employees
7.3.2 To Observe the Knowledge Sharing and Prospect of Industry 4.0
7.3.3 To Analyze the Relationship of Knowledge Management with Industry 4.0 7.3.4 To Understand the Benefits of Blending of Knowledge Management with Industry 4.0
7.4 Conclusion
References
8. Modern Approaches and Implications Toward Industry 4.0
Bishnu Kant Shukla, Amit Tripathi, Gaurav Bharti, Bhupender Parashar, Nitin Bhardwaj, Aakash Gupta and Shivam Verma
8.1 Introduction
8.1.1 Background of Industry 4.0
8.1.2 Significance of Modern Approaches
8.1.3 Objective of the Study
8.2 Literature Review
8.2.1 Evolution of Industry 4.0
8.2.2 Previous Studies on Modern Approaches
8.2.3 Gaps in Current Literature
8.3 Modern Methodologies Supporting Industry 4.0
8.3.1 Cyber-Physical Systems (CPS)
8.3.1.1 Definition and Characteristics
8.3.1.2 Application in Industry
8.3.2 Industrial Internet of Things (IIoT)
8.3.2.1 Components and Structure
8.3.2.2 Role in Smart Manufacturing
8.3.3 Advanced Analytics
8.3.3.1 Importance in Data Processing
8.3.3.2 Case Studies
8.3.4 Machine Learning (ML)
8.3.4.1 Algorithms and Application
8.3.4.2 Role in Predictive Maintenance
8.3.5 Robotics
8.3.5.1 Current Innovations
8.3.5.2 Robotics in Modern Production Lines
8.3.6 Additive Manufacturing
8.3.6.1 Types and Materials
8.3.6.2 Benefits in Customization and Production
8.4 Implications of Adopting Industry 4.0 Practices
8.4.1 Workforce Adaptation
8.4.1.1 Training and Skill Enhancement
8.4.1.2 Job Roles and Evolution
8.4.2 Cybersecurity
8.4.2.1 Risks and Challenges
8.4.2.2 Best Practices in Cybersecurity for Industry 4.0
8.4.3 Return on Investment
8.4.3.1 Cost Analysis
8.4.3.2 Long-Term Benefits
8.5 Cross-Sector Collaboration
8.5.1 Role of Academic Institutions
8.5.2 Industry Stakeholders and Contributions
8.5.3 Policymaking and Regulatory Landscape
8.6 Strategies for Successful Adoption
8.6.1 Standardized Frameworks and Guidelines
8.6.1.1 Importance of Interoperability
8.6.1.2 Security Measures
8.6.1.3 Scalability Considerations
8.6.2 Continuous Improvement and Adaptability
8.6.3 Case Studies and Real-World Applications
8.7 Future Prospects and Research Directions
8.7.1 Emerging Technologies in Industry 4.0
8.7.2 Challenges and Opportunities
8.7.3 Potential Growth Areas and Innovations
8.7.3.1 Personalized Manufacturing
8.7.3.2 Augmented Workforce
8.7.3.3 Circular Economy
8.8 Conclusion
References
9. Managing Knowledge in the Era of Industry 4.0: Challenges and Strategies
Pawan Whig, Jhansi Bharathi Madavarapu, Nikhitha Yathiraju and Ramya Thatikonda
9.1 Introduction
9.2 Literature Review
9.3 Impacts of Industry 4.0
9.4 Importance of Knowledge Management in the Context of Industry 4.0
9.5 Knowledge Management
9.6 Overview of Industry 4.0 and Technologies
9.7 Implications of Industry 4.0 for Knowledge Management
9.8 Role of Artificial Intelligence, Big Data, and IoT in Knowledge Management
9.9 Challenges of Knowledge Management in Industry 4.0
9.10 Strategies for Effective Knowledge Management
9.11 Best Practices for Knowledge Management in Industry 4.0
9.12 Strategies for Leveraging Technology for Effective Knowledge Management
9.13 Case Studies of Successful Knowledge Management in Industry 4.0
9.14 Result
9.15 Discussion
9.16 Conclusion
9.17 Future Directions
References
10. Enhance Knowledge Management in Industry 4.0
Ambika N.
10.1 Introduction
10.2 Background
10.3 Literature Survey
10.4 Previous Study
10.5 Case Study
10.6 Analysis of the Work
10.7 Challenges of Data Analytics
10.8 Future Scope
10.9 Conclusion
References
11. Industrialized Control and Automation System (ICAS): A Software-Defined Analysis Framework for Industry 4.0
Karthikeyan V., Dani Reagan Vivek J., Gopalakrishnan K. and Ezhil Kalaimannan
11.1 Introduction
11.1.1 Industry Fourth-Generation Technologies
11.1.1.1 The Industrial IoT (IIoT)
11.1.1.2 Automation
11.1.1.3 AI—Artificial Intelligence
11.1.1.4 Big Data (BD) and Analytics
11.1.1.5 The Cloud
11.1.1.6 Cybersecurity
11.1.2 Business Drivers of i4.0
11.1.3 Industrial Internet
11.2 Related Works
11.2.1 Technologies
11.2.2 Applications
11.2.3 Management Innovation
11.3 Healthcare Applications in Industries
11.3.1 Major Challenges Associated with Healthcare
11.3.2 IoT-Based Smart Healthcare System
11.3.3 Advanced Technologies Used in Healthcare
11.3.4 Open Research Issues to be Addressed
11.4 Inventory Management and Quality Control
11.4.1 Inventory Management (IM)
11.4.2 Inventory
11.4.3 Types of Inventory Management
11.4.4 Inventory Management and IIoT
11.4.5 Open Research Issues to be Addressed
11.5 Analysis of a Machine Learning Algorithm to Predict Wine Quality
11.5.1 Objectives
11.5.2 Conventional Schemes for Wine Quality Assessment (WQA)
11.5.3 Proposed Autonomous ML Scheme-Based WQA
11.5.4 Open Research Issues to be Addressed
11.6 Conclusion and Future Directions
References
12. Structural Understanding of the Relationship Between Various Consciousness of Programming and Creative Attitudes as Part of Knowledge Management Process
Masanori Fukui, Eng Tek Ong, Khar Thoe Ng, Yoon Fah Lay and Subuh Anggoro
12.1 Introduction
12.1.1 The Purpose of this Study
12.1.2 Research Background
12.1.3 Identification of Problems
12.2 Method
12.2.1 Survey Targets and Procedures
12.2.2 Survey Items
12.3 Analysis Procedure
12.4 Results
12.4.1 Preliminary Analysis
12.4.2 Descriptive Statistics and Results of Correlation Analysis
12.4.3 Result of Covariance Structural Analysis of All Factors and Items
12.5 Discussion
12.6 Conclusion
12.6.1 Summary of this Research
12.6.2 Limitations and Reflections for Future Study
Acknowledgments
Note
References
13. Blended-Mode Instruction for Knowledge Management Toward IR4.0: Exemplars in Lifelong STREAM Education and The Way Forward
Kamolrat Intaratat, Guan Xing Zhi, Subuh Anggoro, Khar Thoe Ng and Dungkamol Intaratat
13.1 Introduction
13.1.1 Background/Overview of Literacy and Technology- Enhanced Knowledge Management (KM) Platforms
13.1.2 Problem Statement and Rationale
13.1.3 Research Objectives and Methodologies Implemented
13.2 Analysis of Findings and Illustrations of Case Exemplars
13.2.1 Operational Definitions of BMI, KM in STREAM Education Toward IR4.0 Reflecting SDGs (RO1)
13.2.1.1 Blended-Mode Instruction (BMI)
13.2.1.2 Knowledge Management (KM) and its Roles in STREAM Education
for IR4.0 in Line with SDGs
13.2.2 Exemplars on How BMI Facilitated Lifelong Learning with KM Considering Local Wisdom and Literacy (RO2)
13.2.2.1 Case Exemplar(s) on How BMI Facilitated Lifelong Learning Considering Literacy with KM
13.2.2.2 Case Exemplar(s) on How BMI Facilitated Lifelong Learning Considering Local Wisdom with KM
13.3 Conclusion
13.3.1 Summary and Implications/Significance
13.3.2 Limitations and Recommendations
Acknowledgments
References
14. Insight Review on Advanced Digital Manufacturing Technology Solutions for Industry 4.0
D. David Neels Ponkumar, K. Saravanan, Riboy Cheriyan and Chinnadurai Manthiramoorthy
14.1 Introduction
14.2 Digitization of Manufacturing Sectors
14.3 Knowledge Management Practices Adopted in Manufacturing
14.4 Challenges in Digitization of Industry 4.0
14.5 Key Factors in Addressing Obstacles and Hindrances
14.6 Conclusion
References
15. Enhancing Students’ Learning Achievement, 21st-Century Skills, and Self-Regulation Skills—Knowledge Management and Education 4.0 Perspective
Maria Cindy F. Cardona, Amelia T. Buan, Ellen D. Inutan and Rajendra Kumar
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.3.1 The Model
15.4 Participants
15.5 Research Instruments
15.6 Data Collection
15.7 Data Analysis
15.8 Results and Discussion
15.8.1 Performance of Students in Achievement Tests
15.8.2 Performance of Students in Terms of the 21st-Century Skills and Self-Regulation Skills
15.9 Conclusion
References
Index
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