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Submit a ProposalFactories of the Future
 | Technological Advances in the Manufacturing Industry Edited by Chandan Deep Singh and Harleen Kaur Copyright: 2023 | Status: Published ISBN: 9781119864943 | Hardcover | 302 pages | 81 illustrations Price: $195 USD  |
One Line Description
The book provides insight into various technologies adopted and to be adopted in the future by industries and measures the impact of these technologies on manufacturing performance and their sustainability.
Audience
The book will be read by academic researchers and industry engineers, managers, and specialists in industrial manufacturing and production, mechanical and electronics engineering and their allied disciplines. It will also be helpful to those in industrial R&D departments, as industries are always adopting new technologies and advancements are continually made in this sector.
The book will be read by academic researchers and industry engineers, managers, and specialists in industrial manufacturing and production, mechanical and electronics engineering and their allied disciplines. It will also be helpful to those in industrial R&D departments, as industries are always adopting new technologies and advancements are continually made in this sector.
Description
Businesses and manufacturers face a slew of demands beyond the usual issues of staying agile and surviving in a competitive landscape within a rapidly changing world. Factories of the Future deftly takes the reader through the continuous technology changes and looks ten years down the road at what manufacturing will mostly look like.
The book is divided into two parts: Emerging technologies and advancements in existing technologies. Emerging technologies consist of Industry 4.0 and 5.0 themes, machine learning, intelligent machining, advanced maintenance, reliability, and green manufacturing. The advances of existing technologies consist of digital manufacturing, artificial intelligence in machine learning, Internet of Things, product life cycle, and the impact of factories on the future of manufacturing performance of the manufacturing industries.
Readers will find in this illuminating book:
• A comprehensive discussion of almost all emerging technologies, including “green” manufacturing;
• An overview of the social, economic, and technical aspects of these technologies;
• An explanation of these technological advancements on manufacturing performance, through case studies and other analytical tools.
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Businesses and manufacturers face a slew of demands beyond the usual issues of staying agile and surviving in a competitive landscape within a rapidly changing world. Factories of the Future deftly takes the reader through the continuous technology changes and looks ten years down the road at what manufacturing will mostly look like.
The book is divided into two parts: Emerging technologies and advancements in existing technologies. Emerging technologies consist of Industry 4.0 and 5.0 themes, machine learning, intelligent machining, advanced maintenance, reliability, and green manufacturing. The advances of existing technologies consist of digital manufacturing, artificial intelligence in machine learning, Internet of Things, product life cycle, and the impact of factories on the future of manufacturing performance of the manufacturing industries.
Readers will find in this illuminating book:
• A comprehensive discussion of almost all emerging technologies, including “green” manufacturing;
• An overview of the social, economic, and technical aspects of these technologies;
• An explanation of these technological advancements on manufacturing performance, through case studies and other analytical tools.
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Author / Editor Details
Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. He has published over 100 papers in various peer-reviewed international journals and conferences.
Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. He has published over 100 papers in various peer-reviewed international journals and conferences.
Harleen Kaur, PhD, is doing project work with the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. Previously, she worked as a manager at DELBREC Industries, Pvt. Ltd., as well as an assistant professor of management at Asra Institute of Advanced Studies, Bhawanigarh, India.
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Table of Contents
Preface
1. Factories of the Future
Talwinder Singh and Davinder Singh
1.0 Introduction
1.1 Factory of the Future
1.1.1 Plant Structure
1.1.2 Plant Digitization
1.1.3 Plant Processes
1.1.4 Industry of the Future: A Fully Integrated Industry
1.2 Current Manufacturing Environment
1.3 Driving Technologies and Market Readiness
1.4 Connected Factory, Smart Factory, and Smart Manufacturing
1.4.1 Potential Benefits of a Connected Factory
1.5 Digital and Virtual Factory
1.5.1 Digital Factory
1.5.2 Virtual Factory
1.6 Advanced Manufacturing Technologies
1.6.1 Advantages of Advanced Manufacturing Technologies
1.7 Role of Factories of the Future (FoF) in Manufacturing Performance
1.8 Socio-Econo-Techno Justification of Factories of the Future
References
2. Industry 5.0
Talwinder Singh, Davinder Singh, Chandan Deep Singh and Kanwaljit Singh
2.1 Introduction
2.1.1 Industry 5.0 for Manufacturing
2.1.1.1 Industrial Revolutions
2.1.2 Real Personalization in Industry 5.0
2.1.3 Industry 5.0 for Human Workers
2.2 Individualized Human-Machine-Interaction
2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them
2.4 Concerns in Industry 5.0
2.5 Humans Closer to the Design Process of Manufacturing
2.5.1 Enablers of Industry 5.0
2.6 Challenges and Enablers (Socio-Econo-Techno Justification)
2.6.1 Social Dimension
2.6.2 Governmental and Political Dimension
2.6.3 Interdisciplinarity
2.6.4 Economic Dimension
2.6.5 Scalability
2.7 Concluding Remarks
References
3. Machine Learning – A Survey
Navdeep Singh and Aanchal Goyal
3.1 Introduction
3.2 Machine Learning
3.2.1 Unsupervised Machine Learning
3.2.2 Variety of Unsupervised Learning
3.2.3 Supervised Machine Learning
3.2.4 Categories of Supervised Learning
3.3 Reinforcement Machine Learning
3.3.1 Applications of Reinforcement Learning
3.3.2 Dimensionality Reduction
3.4 Importance of Dimensionality Reduction in Machine Learning
3.4.1 Methods of Dimensionality Reduction
3.4.1.1 Principal Component Analysis (PCA)
3.4.1.2 Linear Discriminant Analysis (LDA)
3.4.1.3 Generalized Discriminant Analysis (GDA)
3.5 Distance Measures
3.6 Clustering
3.6.1 Algorithms in Clustering
3.6.2 Applications of Clustering
3.6.3 Iterative Distance-Based Clustering
3.7 Hierarchical Model
3.8 Density-Based Clustering
3.8.1 DBSCAN
3.8.2 OPTICS
3.9 Role of Machine Learning in Factories of the Future
3.10 Identification of the Probable Customers
3.11 Conclusion
References
4. Understanding Neural Networks
Er. Lal Chand, Sikander Singh Cheema and Manpreet Kaur
4.1 Introduction
4.2 Components of Neural Networks
4.2.1 Neurons
4.2.2 Synapses and Weights
4.2.3 Bias
4.2.4 Architecture of Neural Networks
4.2.5 How Do Neural Networks Work?
4.2.6 Types of Neural Networks
4.2.6.1 Artificial Neural Network (ANN)
4.2.6.2 Recurrent Neural Network (RNN)
4.2.6.3 Convolutional Neural Network (CNN)
4.2.7 Learning Techniques in Neural Network
4.2.8 Applications of Neural Network
4.2.9 Advantages of Neural Networks
4.2.10 Disadvantages of Neural Network
4.2.11 Limitations of Neural Networks
4.3 Back-Propagation
4.3.1 Working of Back-Propagation
4.3.2 Types of Back-Propagation
4.3.2.1 Static Back-Propagation
4.3.2.2 Recurrent Back-Propagation
4.3.2.3 Advantages of Back-Propagation
4.3.2.4 Disadvantages of Back-Propagation
4.4 Activation Function (AF)
4.4.1 Sigmoid Active Function
4.4.1.1 Advantages
4.4.1.2 Disadvantages
4.4.2 RELU Activation Function
4.4.2.1 Advantages
4.4.2.2 Disadvantages
4.4.3 TANH Active Function
4.4.3.1 Advantages
4.4.3.2 Disadvantages
4.4.4 Linear Function
4.4.5 Advantages
4.4.6 Disadvantages
4.4.7 Softmax Function
4.4.8 Advantages
4.5 Comparison of Activation Functions
4.6 Machine Learning
4.6.1 Applications of Machine Learning
4.7 Conclusion
References
5. Intelligent Machining
Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak
5.1 Introduction
5.2 Requirements for the Developments of Intelligent Machining
5.3 Components of Intelligent Machining
5.3.1 Intelligent Sensors
5.3.1.1 Features of Intelligent Sensors
5.3.1.2 Functions of Intelligent Sensors
5.3.1.3 Data Acquisition and Management System to Process and Store Signals
5.3.2 Machine Learning and Knowledge Discovery Component
5.3.3 Database Knowledge Discovery
5.3.4 Programmable Logical Controller (PLC)
5.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing
5.3.6 Information Integration via Knowledge Graphs
5.4 Conclusion
References
6. Advanced Maintenance and Reliability
Davinder Singh and Talwinder Singh
6.1 Introduction
6.2 Condition-Based Maintenance
6.3 Computerized Maintenance Management Systems (CMMS)
6.4 Preventive Maintenance (PM)
6.5 Predictive Maintenance (PdM)
6.6 Reliability Centered Maintenance (RCM)
6.6.1 RCM Principles
6.7 Condition Monitoring and Residual Life Prediction
6.8 Sustainability
6.8.1 Role of Sustainability in Manufacturing
6.9 Concluding Remarks
References
7. Digital Manufacturing
Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak
7.1 Introduction
7.2 Product Life Cycle and Transition
7.3 Digital Thread
7.4 Digital Manufacturing Security
7.5 Role of Digital Manufacturing in Future Factories
7.6 Digital Manufacturing and CNC Machining
7.6.1 Introduction to CNC Machining
7.6.2 Equipment’s Used in CNC Machining
7.6.3 Analyzing Digital Manufacturing Design Considerations
7.6.4 Finishing of Part After Machining
7.7 Additive Manufacturing
7.7.1 Objective of Additive Manufacturing
7.7.2 Design Consideration
7.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries
7.9 Conclusion
References
8. Artificial Intelligence in Machine Learning
Sikander Singh Cheema, Er. Lal Chand and Bhagwant Singh
8.1 Introduction
8.2 Case Studies
8.3 Advantages of A.I. in ML
8.4 Artificial Intelligence – Basics
8.4.1 History of A.I.
8.4.2 Limitations of Human Mind
8.4.3 Real Artificial Intelligence
8.4.4 Artificial Intelligence Subfields
8.4.5 The Positives of A.I.
8.4.6 Machine Learning
8.4.7 Machine Learning Models
8.4.8 Neural Networks
8.4.9 Constraints of Machine Learning
8.4.10 Different Kinds of Machine Learning
8.5 Application of Artificial Intelligence
8.5.1 Expert Systems
8.5.2 Natural Language Processing
8.5.3 Speech Recognition
8.5.4 Computer Vision
8.5.5 Robotics
8.6 Neural Networks (N.N.) Basics
8.6.1 Application of Neural Networks
8.6.2 Architecture of Neural Networks
8.6.3 Working of Artificial Neural Networks
8.7 Convolution Neural Networks
8.7.1 Working of Convolutional Neural Networks
8.7.2 Overview of CNN
8.7.3 Working of CNN
8.8 Image Classification
8.8.1 Concept of Image Classification
8.8.2 Type of Learning
8.8.3 Features of Image Classification
8.8.4 Examples of Image Classification
8.9 Text Classification
8.9.1 Text Classification Examples
8.9.2 Phases of Text Classification
8.9.3 Text Classification API
8.10 Recurrent Neural Network
8.10.1 Type of Recurrent Neural Network
8.11 Building Recurrent Neural Network
8.12 Long Short Term Memory Networks (LSTMs)
References
9. Internet of Things
Davinder Singh
9.1 Introduction
9.2 M2M and Web of Things
9.3 Wireless Networks
9.4 Service Oriented Architecture
9.5 Complexity of Networks
9.6 Wireless Sensor Networks
9.7 Cloud Computing
9.8 Cloud Simulators
9.9 Fog Computing
9.10 Applications of IoT
9.11 Research Gaps and Challenges in IoT
9.12 Concluding Remarks
References
10. Product Life Cycle
Harpreet Singh, Neetu Kaplas, Amant Sharma and Sahil Raj
10.1 Introduction
10.2 Product Lifecycle Management (PLM)
10.2.1 Why Product Lifecycle Management?
10.2.2 Biological Product Lifecycle Stages
10.2.3 An Example Related to Stages in Product Lifecycle Management
10.2.4 Advanced Stages in Product Lifecycle Management
10.2.5 Strategies of Product Lifecycle Management
10.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies
10.3.1 Considerations in High and Low-Level Pricing
10.3.2 Penetration Pricing Strategy
10.3.3 Example for Penetration Pricing Strategy
10.3.4 Considerations in Penetration Pricing
10.4 How Do Product Lifecycle Management Work?
10.5 Application Process of Product Lifecycle Management (PLM)
10.6 Role of Unified Modelling Language (UML)
10.6.1 UML Activity Diagrams
10.7 Management of Product Information Throughout the Entire Product Lifecycle
10.8 PDM System in an Organization
10.8.1 Benefits of PDM
10.8.2 How Does the PDM Work?
10.8.3 The Services of Product Data Management
10.9 System Architecture
10.9.1 Process of System Architecture
10.10 Concepts of Model-Based System Engineering (MBSE)
10.10.1 Benefits of Model-Based System Engineering (MBSE)
10.11 Challenges of Post-COVID 19 in Manufacturing Sector
10.12 Recent Updates in Product Life Cycle
10.13 Conclusion
References
11. Case Studies
Chandan Deep Singh and Harleen Kaur
11.1 Case Study in a Two-Wheeler Manufacturing Industry
11.1.1 Company Strategy
11.1.2 Initiatives Towards Technological Advancement
11.1.3 Management Initiatives
11.1.4 Sustainable Development Goals
11.1.5 Growth Framework with Customer Needs
11.1.6 Vision for the Future
11.2 Case Study in a Four-Wheeler Manufacturing Unit
11.2.1 Company Principles
11.2.2 Company Objectives
11.2.3 Company Strategy and Business Initiatives
11.2.4 Technology Initiatives
11.2.5 Management Initiatives
11.2.6 Quality
11.2.7 Sustainable Development Goals
11.2.8 Future Plan of Action
11.3 Conclusions
11.3.1 Limitations
11.3.2 Suggestions for Future Work
Index
Back to Top
Preface
1. Factories of the Future
Talwinder Singh and Davinder Singh
1.0 Introduction
1.1 Factory of the Future
1.1.1 Plant Structure
1.1.2 Plant Digitization
1.1.3 Plant Processes
1.1.4 Industry of the Future: A Fully Integrated Industry
1.2 Current Manufacturing Environment
1.3 Driving Technologies and Market Readiness
1.4 Connected Factory, Smart Factory, and Smart Manufacturing
1.4.1 Potential Benefits of a Connected Factory
1.5 Digital and Virtual Factory
1.5.1 Digital Factory
1.5.2 Virtual Factory
1.6 Advanced Manufacturing Technologies
1.6.1 Advantages of Advanced Manufacturing Technologies
1.7 Role of Factories of the Future (FoF) in Manufacturing Performance
1.8 Socio-Econo-Techno Justification of Factories of the Future
References
2. Industry 5.0
Talwinder Singh, Davinder Singh, Chandan Deep Singh and Kanwaljit Singh
2.1 Introduction
2.1.1 Industry 5.0 for Manufacturing
2.1.1.1 Industrial Revolutions
2.1.2 Real Personalization in Industry 5.0
2.1.3 Industry 5.0 for Human Workers
2.2 Individualized Human-Machine-Interaction
2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them
2.4 Concerns in Industry 5.0
2.5 Humans Closer to the Design Process of Manufacturing
2.5.1 Enablers of Industry 5.0
2.6 Challenges and Enablers (Socio-Econo-Techno Justification)
2.6.1 Social Dimension
2.6.2 Governmental and Political Dimension
2.6.3 Interdisciplinarity
2.6.4 Economic Dimension
2.6.5 Scalability
2.7 Concluding Remarks
References
3. Machine Learning – A Survey
Navdeep Singh and Aanchal Goyal
3.1 Introduction
3.2 Machine Learning
3.2.1 Unsupervised Machine Learning
3.2.2 Variety of Unsupervised Learning
3.2.3 Supervised Machine Learning
3.2.4 Categories of Supervised Learning
3.3 Reinforcement Machine Learning
3.3.1 Applications of Reinforcement Learning
3.3.2 Dimensionality Reduction
3.4 Importance of Dimensionality Reduction in Machine Learning
3.4.1 Methods of Dimensionality Reduction
3.4.1.1 Principal Component Analysis (PCA)
3.4.1.2 Linear Discriminant Analysis (LDA)
3.4.1.3 Generalized Discriminant Analysis (GDA)
3.5 Distance Measures
3.6 Clustering
3.6.1 Algorithms in Clustering
3.6.2 Applications of Clustering
3.6.3 Iterative Distance-Based Clustering
3.7 Hierarchical Model
3.8 Density-Based Clustering
3.8.1 DBSCAN
3.8.2 OPTICS
3.9 Role of Machine Learning in Factories of the Future
3.10 Identification of the Probable Customers
3.11 Conclusion
References
4. Understanding Neural Networks
Er. Lal Chand, Sikander Singh Cheema and Manpreet Kaur
4.1 Introduction
4.2 Components of Neural Networks
4.2.1 Neurons
4.2.2 Synapses and Weights
4.2.3 Bias
4.2.4 Architecture of Neural Networks
4.2.5 How Do Neural Networks Work?
4.2.6 Types of Neural Networks
4.2.6.1 Artificial Neural Network (ANN)
4.2.6.2 Recurrent Neural Network (RNN)
4.2.6.3 Convolutional Neural Network (CNN)
4.2.7 Learning Techniques in Neural Network
4.2.8 Applications of Neural Network
4.2.9 Advantages of Neural Networks
4.2.10 Disadvantages of Neural Network
4.2.11 Limitations of Neural Networks
4.3 Back-Propagation
4.3.1 Working of Back-Propagation
4.3.2 Types of Back-Propagation
4.3.2.1 Static Back-Propagation
4.3.2.2 Recurrent Back-Propagation
4.3.2.3 Advantages of Back-Propagation
4.3.2.4 Disadvantages of Back-Propagation
4.4 Activation Function (AF)
4.4.1 Sigmoid Active Function
4.4.1.1 Advantages
4.4.1.2 Disadvantages
4.4.2 RELU Activation Function
4.4.2.1 Advantages
4.4.2.2 Disadvantages
4.4.3 TANH Active Function
4.4.3.1 Advantages
4.4.3.2 Disadvantages
4.4.4 Linear Function
4.4.5 Advantages
4.4.6 Disadvantages
4.4.7 Softmax Function
4.4.8 Advantages
4.5 Comparison of Activation Functions
4.6 Machine Learning
4.6.1 Applications of Machine Learning
4.7 Conclusion
References
5. Intelligent Machining
Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak
5.1 Introduction
5.2 Requirements for the Developments of Intelligent Machining
5.3 Components of Intelligent Machining
5.3.1 Intelligent Sensors
5.3.1.1 Features of Intelligent Sensors
5.3.1.2 Functions of Intelligent Sensors
5.3.1.3 Data Acquisition and Management System to Process and Store Signals
5.3.2 Machine Learning and Knowledge Discovery Component
5.3.3 Database Knowledge Discovery
5.3.4 Programmable Logical Controller (PLC)
5.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing
5.3.6 Information Integration via Knowledge Graphs
5.4 Conclusion
References
6. Advanced Maintenance and Reliability
Davinder Singh and Talwinder Singh
6.1 Introduction
6.2 Condition-Based Maintenance
6.3 Computerized Maintenance Management Systems (CMMS)
6.4 Preventive Maintenance (PM)
6.5 Predictive Maintenance (PdM)
6.6 Reliability Centered Maintenance (RCM)
6.6.1 RCM Principles
6.7 Condition Monitoring and Residual Life Prediction
6.8 Sustainability
6.8.1 Role of Sustainability in Manufacturing
6.9 Concluding Remarks
References
7. Digital Manufacturing
Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak
7.1 Introduction
7.2 Product Life Cycle and Transition
7.3 Digital Thread
7.4 Digital Manufacturing Security
7.5 Role of Digital Manufacturing in Future Factories
7.6 Digital Manufacturing and CNC Machining
7.6.1 Introduction to CNC Machining
7.6.2 Equipment’s Used in CNC Machining
7.6.3 Analyzing Digital Manufacturing Design Considerations
7.6.4 Finishing of Part After Machining
7.7 Additive Manufacturing
7.7.1 Objective of Additive Manufacturing
7.7.2 Design Consideration
7.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries
7.9 Conclusion
References
8. Artificial Intelligence in Machine Learning
Sikander Singh Cheema, Er. Lal Chand and Bhagwant Singh
8.1 Introduction
8.2 Case Studies
8.3 Advantages of A.I. in ML
8.4 Artificial Intelligence – Basics
8.4.1 History of A.I.
8.4.2 Limitations of Human Mind
8.4.3 Real Artificial Intelligence
8.4.4 Artificial Intelligence Subfields
8.4.5 The Positives of A.I.
8.4.6 Machine Learning
8.4.7 Machine Learning Models
8.4.8 Neural Networks
8.4.9 Constraints of Machine Learning
8.4.10 Different Kinds of Machine Learning
8.5 Application of Artificial Intelligence
8.5.1 Expert Systems
8.5.2 Natural Language Processing
8.5.3 Speech Recognition
8.5.4 Computer Vision
8.5.5 Robotics
8.6 Neural Networks (N.N.) Basics
8.6.1 Application of Neural Networks
8.6.2 Architecture of Neural Networks
8.6.3 Working of Artificial Neural Networks
8.7 Convolution Neural Networks
8.7.1 Working of Convolutional Neural Networks
8.7.2 Overview of CNN
8.7.3 Working of CNN
8.8 Image Classification
8.8.1 Concept of Image Classification
8.8.2 Type of Learning
8.8.3 Features of Image Classification
8.8.4 Examples of Image Classification
8.9 Text Classification
8.9.1 Text Classification Examples
8.9.2 Phases of Text Classification
8.9.3 Text Classification API
8.10 Recurrent Neural Network
8.10.1 Type of Recurrent Neural Network
8.11 Building Recurrent Neural Network
8.12 Long Short Term Memory Networks (LSTMs)
References
9. Internet of Things
Davinder Singh
9.1 Introduction
9.2 M2M and Web of Things
9.3 Wireless Networks
9.4 Service Oriented Architecture
9.5 Complexity of Networks
9.6 Wireless Sensor Networks
9.7 Cloud Computing
9.8 Cloud Simulators
9.9 Fog Computing
9.10 Applications of IoT
9.11 Research Gaps and Challenges in IoT
9.12 Concluding Remarks
References
10. Product Life Cycle
Harpreet Singh, Neetu Kaplas, Amant Sharma and Sahil Raj
10.1 Introduction
10.2 Product Lifecycle Management (PLM)
10.2.1 Why Product Lifecycle Management?
10.2.2 Biological Product Lifecycle Stages
10.2.3 An Example Related to Stages in Product Lifecycle Management
10.2.4 Advanced Stages in Product Lifecycle Management
10.2.5 Strategies of Product Lifecycle Management
10.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies
10.3.1 Considerations in High and Low-Level Pricing
10.3.2 Penetration Pricing Strategy
10.3.3 Example for Penetration Pricing Strategy
10.3.4 Considerations in Penetration Pricing
10.4 How Do Product Lifecycle Management Work?
10.5 Application Process of Product Lifecycle Management (PLM)
10.6 Role of Unified Modelling Language (UML)
10.6.1 UML Activity Diagrams
10.7 Management of Product Information Throughout the Entire Product Lifecycle
10.8 PDM System in an Organization
10.8.1 Benefits of PDM
10.8.2 How Does the PDM Work?
10.8.3 The Services of Product Data Management
10.9 System Architecture
10.9.1 Process of System Architecture
10.10 Concepts of Model-Based System Engineering (MBSE)
10.10.1 Benefits of Model-Based System Engineering (MBSE)
10.11 Challenges of Post-COVID 19 in Manufacturing Sector
10.12 Recent Updates in Product Life Cycle
10.13 Conclusion
References
11. Case Studies
Chandan Deep Singh and Harleen Kaur
11.1 Case Study in a Two-Wheeler Manufacturing Industry
11.1.1 Company Strategy
11.1.2 Initiatives Towards Technological Advancement
11.1.3 Management Initiatives
11.1.4 Sustainable Development Goals
11.1.5 Growth Framework with Customer Needs
11.1.6 Vision for the Future
11.2 Case Study in a Four-Wheeler Manufacturing Unit
11.2.1 Company Principles
11.2.2 Company Objectives
11.2.3 Company Strategy and Business Initiatives
11.2.4 Technology Initiatives
11.2.5 Management Initiatives
11.2.6 Quality
11.2.7 Sustainable Development Goals
11.2.8 Future Plan of Action
11.3 Conclusions
11.3.1 Limitations
11.3.2 Suggestions for Future Work
Index
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