Industry 4. 0 and Climate Change

At present, both Industry 4.0 and industrial engineering management developments are reshaping the industrial sector worldwide. Industry 4.0 and sustainability are considered as the crucial emerging trends in industrial production systems. The resulting transformations are changing production modes from traditional to digital, intelligent, and decentralized. It is expected that Industry 4.0 will help drive sustainability in industries thanks to the implementation of advanced technology and a move towards social sustainability. This book reflects on the consequences of the transition to Industry 4.0 for climate change. The book presents a systemic overview of the current negative impacts of digitization on the environment and showcases a new outline of the energy domain and expected changes in environmental pollution levels under Industry 4.0. It also analyzes the ecological consequences of the growth and development of Industry 4.0 and considers Industry 4.0 as an alternative to fighting climate change, in the sense of shifting the global community’s attention from environmental protection to consolidation of the digital economy. This book will be of interest to academicians and practitioners in the fields of climate change and development of Industry 4.0, and it will contribute to national economic policies for fighting climate change and corporate strategies of sustainable development under Industry 4.0. Cover Half Title Series Page Title Page Copyright Page Table of Contents Editor biographies List of contributors 1 Optimization of milling machine parameters by using Artificial Neural Network model 1.1 Introduction 1.2 Literature review 1.3 Methodology 1.3.1 Data collection from the experimental setup 1.3.2 Developing Artificial Neural Network model 1.3.3 Execution of the experiment 1.3.4 Prediction using ANN tool 1.4 Result and discussion 1.5 Conclusion References 2 Facility layout optimization: Continuous improvement 2.1 Introduction 2.2 Literature review 2.3 Case study and methodology 2.3.1 Overview and general layout of the company 2.3.2 Gap in the current layout 2.3.2.1 Factor 1 – material flow and inventory handling 2.3.2.2 Factor 2 – productivity 2.3.2.3 Factor 3 – quality 2.3.2.4 Factor 4 – safety 2.4 Discussion and recommendations 2.5 Conclusion References 3 Multi-criteria decision analysis applications and trends in manufacturing domain 3.1 Introduction 3.2 Applications and trends of MCDM in manufacturing decision-making 3.3 Conclusion References 4 To implement Six Sigma to minimize defects in the manufacturing of draft gear of railway wagon 4.1 Introduction 4.2 Business process mapping (SIPOC diagrams) 4.2.1 Purpose 4.2.2 Definitions 4.3 Define phase 4.3.1 SIPOC logic 4.4 Measure phase 4.4.1 DPMO 4.4.2 Control chart 4.5 Analyze phase 4.5.1 Root cause analysis 4.5.2 Defect name: Scab 4.6 Improve phase 4.7 Control phase 4.8 Result and discussion 4.9 Conclusion References 5 Changeover time reduction through SMED approach: Case study of an Indian steel processing centre 5.1 Introduction 5.2 Literature review 5.3 Methodology 5.3.1 Principles of SMED 5.4 Case study 5.4.1 Background 5.4.2 Analysis 5.4.3 Summary of recommendations 5.5 Results and discussion 5.6 Conclusion References 6 SWOT analysis – on maintenance frameworks for SMEs 6.1 Introduction 6.1.1 Requirement of a maintenance framework 6.1.2 Framework comparison (conceptual basis) 6.2 SWOT analysis of maintenance frameworks 6.2.1 Group A frameworks 6.2.2 Group B frameworks 6.2.3 Group C frameworks 6.2.4 Group D frameworks 6.3 Conclusion References 7 Development of maintenance framework for SMEs by an ISM approach 7.1 Introduction 7.2 Framework design requirement 7.3 Development philosophy of maintenance framework 7.3.1 Various elements of the proposed maintenance framework for SMEs 7.3.2 Development of key elements with sub-elements for the proposed framework for SMEs 7.4 ISM 7.4.1 Problem identification 7.4.2 Elements identifications 7.4.3 Structure-based decision 7.4.4 To Identify pair-wise relation 7.4.5 Development of SSIM (Self-Structural Interpretive Matrix) 7.4.6 Initial reachability matrix development 7.4.7 Incorporating transitivity and developing final reachability matrix 7.4.8 Partitioning of reachability and antecedent sets 7.4.9 Development of conical matrix 7.4.10 Digraph development 7.5 Conclusion References 8 Multi-objective parametric optimization of wire electric discharge machining for Die Hard Steels using supervised machine learning techniques 8.1 Introduction 8.2 Research methodology 8.2.1 Dataset description 8.2.2 Supervised machine learning models 8.2.2.1 LASSO regression 8.2.2.2 K-Nearest Neighbors regression 8.2.2.3 Support Vector Regression 8.2.2.4 Artificial Neural Network regression 8.3 Results and discussion 8.3.1 LASSO regression 8.3.2 K-Nearest Neighbors regression 8.3.3 Support Vector Regression 8.3.4 Artificial Neural Network regression 8.4 Conclusion References 9 Investigation of dragline productivity 9.1 Introduction 9.2 Dragline 9.3 Methodology 9.3.1 Current maintenance scenario 9.3.2 Electric supply 9.4 Analysis of dragline 9.4.1 Analysis of the data 9.4.2 Calculation of repair cost 9.5 Result and discussion 9.6 Conclusion References 10 Lean administration in the Order-to-Cash process 10.1 Introduction 10.2 Methodology 10.3 Implementation 10.3.1 Phase I: understanding the As-Is scenario through swim lane and Kaizen bursts 10.3.2 Phase II: identic fi ation of activities/process steps as VA, NVA, and W 10.3.3 Phase III: setting targets and defining future-state map 10.4 Results and inferences 10.5 Conclusion References 11 Modelling and analysis of Lean Six Sigma framework along with its environmental impact on the business process: A review 11.1 Introduction 11.2 Literature review 11.2.1 Six Sigma and Lean manufacturing in the context of Sustainability 11.2.2 Integrated impact of Green manufacturing and Lean Six Sigma (LSS) on the manufacturing industries’ environmental performance 11.2.3 LSS framework advancements with time 11.3 Findings and discussions 11.4 Conclusion and recommendations References 12 Optimum order allocation in a multi-supplier environment using linear programming model: Case study on heavy industry in India 12.1 Introduction 12.2 Literature review 12.3 Methodology 12.4 Case study 12.5 Conclusion References 13 Formulation of an optimal ordering policy with quadratic demand: Weibull distribution deterioration and partial backlogging 13.1 Introduction 13.2 Assumptions and notations 13.3 Mathematical formulation 13.4 Numerical illustration 13.5 Sensitivity analysis 13.6 Conclusions References 14 An optimal replenishment policy with exponential declining demand: Weibull distribution deterioration and partial backlogging 14.1 Introduction 14.2 Assumptions and notations 14.3 Model development and analysis 14.4 Numerical example 14.5 Sensitivity analysis 14.6 Conclusions References 15 Smart materials advancements, applications and challenges in the shift to Industry 4.0 15.1 Introduction 15.2 Shape memory alloys 15.3 Applications of Nitinol 15.3.1 Aerospace 15.3.2 Actuators 15.3.3 Medical applications 15.4 Challenges 15.5 Conclusion 15.6 Future prospects References 16 Virtual Try On – a study on the changing dimensions of jewellery retailing through augmented reality 16.1 Introduction 16.2 Statement of the problem 16.3 Objectives of the study 16.4 Research methodology 16.5 Operational definitions of key terms 16.5.1 Virtual Try On 16.5.2 Augmented reality 16.5.3 Personalized marketing 16.6 Discussion and analysis 16.6.1 Virtual Try On centred marketing strategies of Indian branded jewellery retailers 16.7 Future research directions 16.7.1 User’s attitude and satisfaction with Virtual Try On applications of luxury brands 16.7.2 Data privacy and Virtual Try On applications 16.7.3 Virtual Try On and its implications in smart retailing 16.8 Conclusion References 17 Analysis of the barriers of blockchain adoption in Land Record System 17.1 Introduction 17.2 Review of literature 17.2.1 Barriers of blockchain usage for land records 17.2.2 Proposed framework for the land registry system 17.2.2.1 New transaction block 17.2.2.2 Smart land title contract 17.2.2.3 Algorithm for pre-agreement 17.3 Research methodology 17.4 Case study 17.5 Discussion 17.5.1 Theoretical implications 17.5.2 Practical implications 17.6 Conclusion References 18 The concept of Industry 4.0: Role of ergonomics and Human Factors 18.1 Introduction 18.2 Advancement in technologies in industries with regard to technology advancement components 18.2.1 Ergonomics 18.3 Evolution of Industry 1.0 to 4.0 18.3.1 First Industrial Revolution 18.3.2 Industrial Revolution 18.3.3 Third Industrial Revolution 18.3.4 Fourth Industrial Revolution (Industry 4.0) 18.3.4.1 Key concepts 18.4 Ergonomics and Human Factors (HFs) 18.5 Case study: CEIT Ergonomics Analysis Application (CERAA) 18.6 Conclusions and future scope References 19 Carbon nanotubes as an advanced coating material for cutting tool in sustainable production in Industry 4.0 19.1 Introduction 19.2 Synthesis of CNT 19.2.1 Physical Vapor Deposition (PVD) techniques 19.2.1.1 Pulse laser deposition 19.2.1.2 Arc discharge 19.2.2 Chemical Vapor Deposition (CVD) techniques 19.3 Purification of CNTs 19.4 Properties of CNTs 19.4.1 Physical properties 19.4.2 Electrical properties 19.4.3 Thermal properties 19.5 Applications of CNTs 19.5.1 Genetic engineering 19.5.2 Aerospace and automotive industry 19.5.3 Electronics and chip manufacturing 19.6 Conclusions and future scope References 20 Integrating AI with Green Manufacturing for process industry 20.1 Introduction 20.2 Challenges of process safety in the context of Green Manufacturing (GM) 20.3 Framework of process safety for GM in the process industry 20.3.1 Artificial Intelligence 20.3.1.1 Integration of information via Knowledge Graph 20.3.1.2 Risk assessment and decision-making by using Bayesian Network 20.3.1.3 Early warning by using Deep Learning 20.4 Result and discussion 20.5 Conclusion References 21 Sustainable recycling methods for different types of eco-friendly cutting fluids and their characteristics: An impetus for circular economy 21.1 Introduction 21.2 Eco-friendly cutting fluid 21.2.1 Vegetable-based cutting fluids 21.2.2 Minimum Quality Lubrication (MQL) 21.2.3 Bio-oil cutting fluid 21.2.4 Cryogenic cutting fluids 21.2.5 Nanofluids 21.3 Recycling methods 21.4 Conclusions 21.5 Future scope 21.6 Results and discussions References 22 Sustainable automobiles: Major obstacles on the path of electrifying mobility in India, existing barriers and challenges 22.1 Introduction 22.2 Literature review 22.3 Growth and strategy 22.4 Barriers to Electric Vehicles (EVs) in the Indian market 22.4.1 Skill gap 22.4.2 Cost constraint 22.4.3 Consumer perception 22.4.4 Raw materials 22.4.5 Battery life 22.4.6 Driving range 22.4.7 Duration of charging 22.4.8 Safety regulations 22.4.9 Environmental factors 22.4.10 Government policies 22.4.11 Charging infrastructure 22.5 Discussing the present scenario 22.6 Conclusion and results References 23 Development of heuristic DSS for supply chain architecture 23.1 Introduction 23.2 General optimization objectives of the supply chain network 23.3 Optimization models 23.4 Statement of an optimization problem 23.5 Literature review on multi-stage supply chain architecture models 23.5.1 Motivation for the study 23.6 Problem statement and research objectives 23.6.1 Research objectives 23.7 Optimization of three-stage supply chain architecture using NLIW-PSO algorithm 23.7.1 Particle development in PSO algorithm of three-stage supply chain architecture 23.7.2 Velocity determination and position modification equations used for the optimization of SCN architecture 23.7.3 Structure of NLIW-PSO algorithm 23.7.4 Development of initial set particles in the PSO algorithms 23.7.5 Generation of initial velocities in the proposed PSO algorithms 23.8 Performance analysis of SCN architecture 23.8.1 Experimental design 23.8.2 Pilot studies for parameters settings 23.8.3 Results and discussions 23.8.4 Supply Chain Setting (SCS) 23.9 Conclusion Index "At present both Industry 4.0 and industrial engineering management developments are reshaping the industrial sector worldwide. Industry 4.0 and sustainability are considered as the crucial emerging trends in industrial production systems. Resulting transformations are changing production modes from traditional to digital, intelligent and decentralized. It is expected that Industry 4.0 will help drive sustainability in industries thanks to the implementation of advanced technology and a move towards the social sustainability. This book reflects on the consequences of the transition to Industry 4.0 for climate change. The book presents a systemic overview of the current negative consequences of digitization for the environment, presents a new outline of the energy domain and expected changes in environmental pollution levels under Industry 4.0. The book also analyses the ecological consequences of growth and development of Industry 4.0, and considers Industry 4.0 as an alternative to fighting climate change, in the sense of shifting the global community's attention from environmental protection to consolidation of the digital economy. This book will be of interest to academics and practitioners in the fields of climate change and development of Industry 4.0, and it will contribute to national economic policies for fighting climate change and corporate strategies of sustainable development under Industry 4.0"-- Provided by publisher