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Advances in Mathematics for Industry 4.0 examines key tools, techniques, strategies, and methods in engineering applications. By covering the latest knowledge in technology for engineering design and manufacture, chapters provide systematic and comprehensive coverage of key drivers in rapid economic development. Written by leading industry experts, chapter authors explore managing big data in processing information and helping in decision-making, including mathematical and optimization techniques for dealing with large amounts of data in short periods. Focuses on recent research in mathematics applications for Industry 4.0 Provides insights on international and transnational scales Identifies mathematics knowledge gaps for Industry 4.0 Describes fruitful areas for further research in industrial mathematics, including forthcoming international studies and research Advances in Mathematics for Industry 4.0 Copyright Contents List of contributors About the editor Preface Acknowledgments 1 Trust-enhancing technologies: Blockchain mathematics in the context of Industry 4.0 1.1 Introduction 1.2 Trust for all 1.3 Privacy by design 1.4 Conclusions, pending challenges, and future works References 2 Optimization techniques to support decision-making processes via MSM—an Industry 4.0 approach 2.1 Introduction 2.2 The multilayer stream mapping approach 2.3 A synopsis of the proposed methodology 2.4 Problem statement—a real case study 2.4.1 The company and the production system 2.4.2 Processing characteristics and job constraints 2.4.3 Manufacturing processes description 2.4.4 Indicators 2.4.4.1 Flow indicators 2.4.4.2 Resources indicators 2.4.4.3 Associated costs 2.5 Production system model 2.5.1 Model description 2.5.2 Practical description 2.5.3 Model applicability 2.6 Optimization technique 2.6.1 Heuristic approaches 2.6.2 Genetic algorithm 2.7 Acquired results 2.7.1 Heuristics 2.7.2 Genetic algorithm strategy 2.7.3 Optimization approaches enforcement 2.7.3.1 Comparison between the applied heuristics and GA 2.7.3.2 Simulating scenarios 2.8 Conclusions Acknowledgments References 3 A probabilistic approach to reconfigurable interactive manufacturing and coil winding for Industry 4.0 3.1 Introduction 3.2 Probabilistic framework 3.2.1 Signal processing 3.2.1.1 Smoothing and normalization 3.2.2 Gaussian mixture model 3.2.3 Incremental Gaussian mixture model 3.2.4 Gaussian mixture regression 3.2.5 System effectiveness 3.3 Automatic assembly 3.3.1 Task and system description 3.3.2 Methodology 3.3.2.1 Learning phase 3.3.2.2 Module and door identification 3.3.3 Results 3.4 Manufacturing of electric motors 3.4.1 Task and system description 3.4.1.1 Human–robot interface 3.4.1.2 Lab scenario 3.4.1.3 Industrial scenario 3.4.2 Methodology 3.4.2.1 Pole selection 3.4.2.2 Entrance and exit position estimation 3.4.2.3 Robot movement 3.4.3 Results 3.4.3.1 Lab scenario 3.4.3.2 Industrial scenario 3.5 Conclusions References 4 The tolerance scheduling problem for maximum lateness in Industry 4.0 systems 4.1 Introduction 4.2 Industry 4.0 production environments 4.2.1 Cyber-physical systems 4.2.2 Cyber-physical production systems 4.2.3 Decision-making in cyber-physical production systems 4.3 Scheduling 4.4 The tolerance scheduling problem 4.4.1 Dynamic scheduling 4.4.2 Inverse scheduling 4.4.3 Tolerance scheduling 4.5 Application case: single machine scheduling minimizing Lmax 4.6 Conclusions References 5 Digitalization and security: a new challenge for Mathematics 4.0 5.1 Introduction 5.2 The role of mathematics in Industry 4.0—a special kind of decision-making 5.3 The mathematics of data science—challenges to Industry 4.0 5.3.1 Making decisions—preparation of data 5.4 Digitalization and security 5.4.1 Challenges and opportunities in the course of intelligent process optimization 5.4.2 A duck tale and Industry 4.0 5.5 What do we mean by “digitalization”? 5.6 Efficiency enhancement and added value through digitalization 5.7 Digital distribution channels 5.8 Digital economy and Industry 4.0 5.8.1 Strategic digitalization—intelligent tracking and complex security 5.8.2 Interaction and intelligent overall system 5.9 Digital processes as a supreme discipline—computerization versus digitalization 5.10 Maturity models 5.11 Other success stories of mathematics and Industry 4.0 5.12 General technological advantages 5.12.1 Cloud computing 5.13 Success stories in companies 5.13.1 Bayer biopharmaceutical located in Garbagnate, Italy: the correct use of data 5.13.2 Haier located in Qingdao, China: predicted maintenance needs 5.13.3 Phoenic contact located in Bad Pyrmont and Blomberg, Germany: digital twins 5.13.4 Siemens located in Chengdu, China: augmented reality 5.13.5 NX: a platform for the development of solutions 5.13.6 Bosch Automotive in Wuxi, China 5.13.7 Summary 5.14 Outlook: mathematics and Industry 4.0—digitalization and security 5.14.1 Digitization and security 5.14.1.1 A new challenge for Mathematics 4.0 References Further reading 6 Proposal and application of a framework to measure the degree of maturity in Quality 4.0: A multiple case study 6.1 Introduction 6.2 Industry 4.0: Pillars and perspectives of integration 6.3 Quality 4.0: Alignment of quality in the new scenario of the Fourth Industrial Revolution 6.3.1 Quality 4.0 organizational dimensions 6.3.1.1 Data 6.3.1.2 Analytics 6.3.1.3 Connectivity 6.3.1.4 Collaboration 6.3.1.5 App development 6.3.1.6 Scalability 6.3.1.7 Management system 6.3.1.8 Compliance 6.3.1.9 Culture 6.3.1.10 Leadership 6.3.1.11 Competence 6.4 Framework development to assess the organization’s Quality 4.0 maturity 6.4.1 Systematic application and data treatment based on a numerical approach 6.5 Quality 4.0: Multiple case study 6.5.1 Quality 4.0 maturity in the automotive industry 6.5.2 Quality 4.0 maturity in the energy industry 6.5.3 Weaknesses and potentialities identified 6.6 Comments and future perspectives References 7 Intelligent manufacturing as a social institute: Internal and external regulation 7.1 Introduction 7.2 Literature review 7.3 Materials and method 7.4 Results 7.4.1 Evaluation of the effectiveness of the existing practices of internal and external stimulation of the development of ... 7.4.2 Innovative practices of stimulation of the development of intellectual production for its quick and successful instit... 7.4.3 Modeling of the institutionalization of intellectual production and practical recommendations (policy implications) 7.5 Conclusions Acknowledgments References 8 Production planning and supply chain management under the conditions of Industry 4.0 8.1 Introduction 8.2 Literature review 8.3 Materials and method 8.4 Results 8.4.1 Regional models of production planning and supply chain management in the modern global economy 8.4.2 Perspective model of production planning and supply chain management under the conditions of Industry 4.0 8.4.3 Adapting the perspective model of production planning and supply chain management under the conditions of Industry 4.... 8.5 Conclusions Acknowledgment References 9 Infrastructural provision and organization of production on the basis of the Internet of Things 9.1 Introduction 9.2 Literature review 9.3 Materials and method 9.4 Results 9.4.1 The essence and specific features of the Fourth Industrial Revolution and advantages of automatized production on the... 9.4.2 Modeling of production and distribution processes in the Internet economy during automatized production on the basis ... 9.4.3 Infrastructural provision of automatized production on the basis of the Internet of Things: Specific features, defici... 9.5 Conclusions Acknowledgment References 10 Artificial intelligence as the core of production of the future: Machine learning and intellectual decision supports 10.1 Introduction 10.2 Literature review 10.3 Materials and method 10.4 Results 10.4.1 The new economic practice in the sphere of the development of artificial intelligence 10.4.2 Scenarios of digital modernization of the modern economy depending on the functions of artificial intelligence in th... 10.4.3 Algorithms of artificial intelligence training for the execution of various functions within the compiled scenarios 10.5 Conclusions Acknowledgment References 11 Active digital manufacturing: Conceptual foundations and practical solutions 11.1 Introduction 11.2 Literature review 11.3 Materials and method 11.4 Results 11.4.1 The scientific concept of active digital manufacturing, its principles, priorities, and differences from the concept... 11.4.2 Comparative analysis of the advantages from the development of passive and active digital manufacturing 11.4.3 Current problems of starting and implementing active digital manufacturing and their perspective solutions 11.5 Conclusions Acknowledgment References 12 Big Data management and data analysis: Applied solutions in view of the spheres of the modern economy 12.1 Introduction 12.2 Literature review 12.3 Materials and method 12.4 Results 12.4.1 Evaluation of the sufficiency of the existing statistical data bases and tools for processing of large arrays of sta... 12.4.2 The conceptual model of application of breakthrough digital technologies of management and Big Data analysis for sta... 12.4.3 The algorithm of statistical accounting and analytics for various spheres of a modern economy in the conditions of I... 12.5 Conclusions References 13 Infusion–diffusion process-based modeling and profit estimation for manufacturing industries 13.1 Introduction 13.2 Role of warranty in determining overall profit: A literature review 13.3 Formulation of sales function 13.4 Optimization problem formulation 13.4.1 Manufacturing cost modeling 13.4.1.1 Model for quantity produced 13.4.1.2 Manufacturing cost 13.4.2 Warranty cost modeling 13.4.2.1 Possible number of complaints 13.4.2.2 Warranty probability 13.4.2.3 Actual number of complaints 13.4.2.4 Complaint factor 13.4.3 Formulation of warranty probability in terms of product performance and customer expectation 13.4.4 Problem formulation for optimal profit 13.5 Numerical illustration 13.6 Managerial implications 13.7 Conclusions References 14 Application of AHP in evaluating the financial performance of industries 14.1 Introduction 14.2 Financial ratios 14.2.1 Liquidity ratios 14.2.2 Financial leverage ratios 14.2.3 Profitability ratios 14.2.4 Growth ratios 14.3 Methodology 14.3.1 Multicriteria decision-making 14.3.2 Analytical hierarchy process 14.4 Experiments and results 14.4.1 Data analysis 14.4.1.1 Criteria level 14.4.1.2 Subcriteria level 14.5 Discussion and conclusions References 15 Application of Internet of Things-aided simulation and digital twin technology in smart manufacturing 15.1 Introduction 15.2 Smart manufacturing systems and Industry 4.0 technologies—a glimpse 15.2.1 Autonomous mobile robots 15.2.2 The industrial Internet of Things 15.2.3 Additive manufacturing 15.2.3.1 Additive manufacturing processes 15.2.3.2 Additive manufacturing technologies 15.2.3.3 Additive manufacturing materials 15.2.4 Augmented reality 15.2.4.1 Augmented reality for product design, inspection, and maintenance 15.2.4.2 Augmented reality for upskilling and productivity 15.2.4.3 Augmented reality for quality assurance 15.2.5 Simulation and virtual reality 15.2.6 Cloud computing 15.2.7 Big Data and analytics 15.2.7.1 Big Data analytics technologies and tools 15.2.7.2 How Big Data analytics works 15.3 Digital twin-driven smart manufacturing 15.3.1 A reference model for the digital twin 15.4 Creation of a digital twin in a smart manufacturing system 15.5 Summary References 16 Mathematical models for the dimensional accuracy of products generated by additive manufacturing 16.1 Introduction to dimensional quality in additive manufacturing 16.2 Main factors for dimensional accuracy in additive manufacturing 16.2.1 Effects of layer thickness and surface orientation 16.2.2 Effects of extruder errors 16.2.3 Effects of material shrinkage and beam offset 16.3 Mathematical modeling of dimensional deviations in additive manufacturing 16.3.1 Dimensional deviations in additive manufacturing-generated parts 16.3.2 Surface roughness in additive manufacturing-generated parts 16.4 Accuracy improvement in additive manufacturing by optimization or compensation techniques 16.4.1 Optimization of part orientation in additive manufacturing 16.4.2 Compensation of extruder errors in additive manufacturing 16.4.3 Compensation of shrinkage effect and beam offset 16.5 Conclusions References Index Advances in Mathematics for Industry 4.0, (2020) i-iii. doi:10.1016/B978-0-12-818906-1.00017-6