
Next-Gen Supply Chains
Beschreibung
This book masterfully bridges the critical gap between theoretical concepts and practical implementation, offering readers actionable strategies, detailed case studies from industry leaders such as Amazon and Lenovo, and robust frameworks for risk management, ethical AI governance and circular economy integration. With its unique emphasis on the synergy between technological innovation and the necessary human capital development, the book is an indispensable resource for supply chain executives, operations managers, technology implementers and academics seeking to future-proof their organizations and master the strategic imperatives of the modern supply chain landscape.
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Personen
Pushan Kumar Dutta is an associate professor and Erasmus Mundus scholar, specializing in AI and edge computing. His research focuses on bridging data analytics and sustainable technology for smart cities and healthcare.
Mudassir Khan is an assistant professor and postdoctoral fellow, specializing in big data analytics and AI. His research on deep learning and IoT applications concerns healthcare and computer science.
Marta Starostka-Patyk is a professor specializing in logistics and supply chain management. Her research focuses on sustainable logistics, reverse logistics and information technologies in modern supply chains.
Inhalt
- Cover
- Title Page
- Copyright Page
- Contents
- Preface
- Chapter 1. AI and Automation: Building Resilient and Sustainable Supply Chains in Uncertain Times
- 1.1. Introduction
- 1.2. Understanding supply chain resilience
- 1.3. Risk management frameworks
- 1.4. Impact of the pandemic on supply chain vulnerabilities
- 1.4.1. Disruptions caused by global crises
- 1.4.2. The cascading effects on global supply networks
- 1.4.3. Case studies of major supply chain disruptions during the pandemic
- 1.5. Building resilience post-pandemic
- 1.5.1. The role of technology in strengthening resilience: AI, automation and blockchain
- 1.5.2. Strategic sourcing and diversification of suppliers
- 1.5.3. Collaboration and transparency within supply chains for better risk management
- 1.6. The future of supply chain resilience
- 1.6.1. Lessons learned and long-term strategies
- 1.6.2. How organizations can prepare for future disruptions
- 1.6.3. The integration of sustainable practices into resilient supply chains
- 1.6.4. Preparing for an unpredictable future
- 1.7. Conclusion
- 1.8. References
- Chapter 2. Generative AI's Impact on Supply Chain Decision-Making
- 2.1. Introduction
- 2.2. Literature review
- 2.3. Comparison table
- 2.4. Challenges
- 2.4.1. Data quality, fragmentation and integration
- 2.4.2. Computational cost and resource intensity
- 2.4.3. Ethical, privacy and security risks
- 2.4.4. Model limitations: hallucination, bias and explainability
- 2.4.5. Organizational and workforce barriers
- 2.5. Technologies
- 2.6. Future scope
- 2.7. References
- Chapter 3. Circular Supply Chain Economics
- 3.1. Introduction
- 3.2. Conceptual foundations
- 3.2.1. Supply chains and principles of the circular economy
- 3.3. Circular supply-chain economic mechanisms
- 3.3.1. Unit economics and cost structures
- 3.4. Demand and revenue models
- 3.4.1. Performance contracting
- 3.4.2. Externalities and market failures
- 3.4.3. Circular supply-chain economics modeling
- 3.4.4. PMC models for input-output (IO) and material flow analysis (MFA)
- 3.5. Operations research models: closed-loop inventory, pricing and remanufacturing
- 3.5.1. Models based on agents and system dynamics
- 3.5.2. Metrics and indicators for circular supply chains
- 3.6. Metrics for businesses and products
- 3.6.1. Indicators for the system and the macro
- 3.6.2. Plans for businesses and business models
- 3.6.3. Product-as-service (PaaS)
- 3.6.4. Rebuilding and refurbishing
- 3.6.5. Recycling and getting materials back
- 3.6.6. Platforms for sharing and pooling
- 3.6.7. Tools for policy and market design
- 3.7. Rules and standards set by the government
- 3.7.1. Support for public procurement and innovation
- 3.8. Changes in jobs and structures
- 3.8.1. Trade flows and logistics in the opposite direction
- 3.9. Case studies and empirical evidence
- 3.9.1. Firm examples
- 3.10. Barriers and enablers
- 3.10.1. Technological and operational barriers
- 3.10.2. Economical and market obstacles
- 3.10.3. Policies and institutional barriers
- 3.10.4. Enablers
- 3.11. Evidence from the real-world and case studies
- 3.11.1. Results for the whole world and for each sector
- 3.11.2. Examples of firms
- 3.12. Things that get in the way and things that help
- 3.12.1. Problems with technology and operations
- 3.12.2. Barriers to the economy and the market
- 3.12.3. Things that help
- 3.12.4. Checking the economic viability
- 3.13. A plan for companies to follow to put it into action
- 3.14. Research priorities and gaps
- 3.15. Conclusion
- 3.16. References
- Chapter 4. IoT Architecture for End-to-End Visibility
- 4.1. Introduction
- 4.2. Visibility of supply chains
- 4.3. Internet of Things (IoT) in logistics and supply chains
- 4.3.1. Architecture of IoT
- 4.3.2. Models of IoT for supply chains
- 4.3.3. Benefits of IoT in supply chains
- 4.4. IoT for end-to-end visibility in supply chains
- 4.5. IoT challenges and barriers to end-to-end visibility in supply chains
- 4.6. The future of IoT in supply chains and their visibility E2E
- 4.7. Conclusions
- 4.8. References
- Chapter 5. Building Blocks of a Transparent IoT Ecosystem
- 5.1. Introduction
- 5.2. Characteristics of IoT
- 5.3. IoT architecture
- 5.3.1. Sensors/actuators
- 5.3.2. Internet gateways with data acquisition system
- 5.3.3. Edge IT
- 5.3.4. Data center/cloud
- 5.4. IoT as XaaS
- 5.4.1. Benefits of XaaS
- 5.4.2. Security challenges
- 5.5. Conclusion
- 5.6. References
- Chapter 6. Blockchain Implementation for Supply Chain Transparency Modeling
- 6.1. Introduction
- 6.2. Experimental methods and materials
- 6.3. Results and discussion: case studies
- 6.3.1. Blockchain technology implementation case studies: practical implementation
- 6.3.2. Indicator analysis
- 6.4. Conclusion
- 6.5. References
- Chapter 7. Autonomous Systems in Supply Chain Operations
- 7.1. Introduction
- 7.2. Objective and scope of the chapter
- 7.3. Research procedure
- 7.4. Analysis of results and discussion
- 7.5. Conclusions
- 7.6. References
- Chapter 8. Leveraging Data and Analytics for Next-Generation Supply Chain Resilience
- 8.1. Introduction
- 8.2. The challenges of today's supply chains
- 8.3. The role of data as the foundation for optimization
- 8.4. The importance of data in supply chain management
- 8.5. Technologies supporting data collection and analysis
- 8.6. Analytical methods and optimization models in supply chain management
- 8.7. Conclusion
- 8.8. References
- Chapter 9. Data-driven Supply Chain Optimization
- 9.1. Introduction
- 9.2. Literature review
- 9.2.1. Big data
- 9.2.2. DT
- 9.2.3. AI in supply chains
- 9.2.4. ML in supply chain optimization
- 9.3. Automated ML in supply chain optimization
- 9.3.1. AutoML
- 9.3.2. H2O AutoML
- 9.3.3. Application of H2O AutoML to daily logistics demand forecasting: a case study
- 9.4. Conclusion
- 9.5. References
- Chapter 10. Sustainability Transformation Roadmaps
- 10.1. Introduction
- 10.2. Sustainability transformation
- 10.3. Sustainability roadmap structures
- 10.4. Building a sustainable transformation roadmap
- 10.5. Conclusion
- 10.6. References
- Chapter 11. Reimagining Supply Chains: Nearshoring and Network Redesign in the Age of AI, Automation and Sustainability
- 11.1. Introduction
- 11.2. Experimental methods and materials
- 11.2.1. Research approach
- 11.3. Nearshoring as a resilience strategy
- 11.4. Conceptual foundations of nearshoring
- 11.5. Drivers of nearshoring adoption
- 11.6. Benefits of nearshoring
- 11.7. Challenges and risks of nearshoring
- 11.8. Industry case studies
- 11.9. Theoretical and analytical frameworks
- 11.10. Future directions in nearshoring research
- 11.11. Network redesign and digital twins
- 11.11.1. Network redesign in supply chains
- 11.11.2. Digital twins in supply chain redesign
- 11.11.3. Synergy: digital twins driving network redesign
- 11.12. Challenges and future directions
- 11.12.1. Automation and AI in supply chain optimization
- 11.12.2. Role of automation in supply chains
- 11.12.3. AI applications
- 11.12.4. Combined impact of automation + AI
- 11.12.5. Challenges and limitations
- 11.12.6. Future directions
- 11.13. Sustainability and ESG compliance in supply chains
- 11.13.1. Environmental sustainability in supply chains
- 11.13.2. Social dimensions of ESG in supply chains
- 11.13.3. Governance and transparency
- 11.13.4. Business case for ESG-aligned supply chains
- 11.13.5. Challenges in ESG compliance
- 11.13.6. Future directions
- 11.14. Analysis of supply chain performance graphs
- 11.14.1. Chart 1: supply chain performance - baseline versus AI-optimized redesign
- 11.14.2. Chart 2: supply chain disruption index evolution
- 11.14.3. Chart 3: total cost analysis - offshore versus nearshore models
- 11.14.4. Chart 4: CO2 emissions across supply chain models
- 11.14.5. Integrated strategic analysis
- 11.15. Recommendations
- 11.16. Conclusion
- 11.17. References
- Chapter 12. Digital Supply Chain Talent Development: Preparing the Workforce for Next-Gen Supply Chains
- 12.1. Introduction: the looming talent crisis in a digital era
- 12.1.1. The transformative impact of AI, automation and global disruption
- 12.1.2. Bridging the chasm: current workforce skills versus future needs
- 12.2. Defining the next-generation supply chain professional
- 12.2.1. Core technical competencies: data analytics, AI literacy and IoT management
- 12.2.2. Essential soft skills: cognitive flexibility, cross-functional collaboration and problem-solving
- 12.2.3. The rise of hybrid roles: supply chain data scientist and automation strategist
- 12.3. A strategic framework for talent development
- 12.3.1. Pillar 1: assessing the current skills gap and forecasting future needs
- 12.3.2. Pillar 2: upskilling and reskilling the existing workforce
- 12.3.3. Pillar 3: attracting and recruiting new digital talent
- 12.3.4. Pillar 4: fostering a culture of continuous learning and adaptability
- 12.4. The critical role of academia and industry partnerships
- 12.4.1. Modernizing academic curricula for the digital supply chain
- 12.4.2. Leveraging apprenticeships, internships and corporate academies
- 12.5. Case study: building a future-ready talent pipeline in practice
- 12.6. Conclusion: securing competitive advantage through strategic talent management
- 12.6.1. Summarizing the key imperatives for leadership
- 12.6.2. Future outlook: navigating the evolving human-machine partnership
- 12.7. References
- Chapter 13. Change Management for Supply Chain Transformation
- 13.1. Introduction
- 13.2. Theoretical foundations of change management
- 13.2.1. Change management models
- 13.3. Framework for supply chain change management
- 13.4. Importance of leadership and governance structures
- 13.5. Challenges and barriers
- 13.6. Enablers and best practices
- 13.7. The future
- 13.8. References
- Chapter 14. Future Horizons: Emerging Technologies and Models
- 14.1. Introduction
- 14.1.1. Research motivation
- 14.1.2. Problem statement
- 14.1.3. Research questions
- 14.1.4. Aims and objectives
- 14.1.5. Significance of the study
- 14.1.6. Scope of the study
- 14.2. A unified framework for intelligent data migration
- 14.2.1. Architectural blueprint
- 14.2.2. AI capabilities simulated in the framework
- 14.2.3. Justification of theoretical scope
- 14.2.4. Mathematical formalization and evaluation
- 14.3. The role of generative AI in cross-domain data migration
- 14.3.1. Semantic data profiling using generative AI
- 14.3.2. Intelligent source-to-target mapping
- 14.3.3. Conversational data querying and exploration
- 14.4. Real-world applications across domains: bridging petrochemical and medical data ecosystems
- 14.4.1. Occupational exposure and health risk modeling
- 14.4.2. Medical product traceability
- 14.4.3. Incident investigation and root cause analysis
- 14.4.4. Workforce health surveillance and compliance auditing
- 14.4.5. Data quality monitoring across the industrial-clinical pipeline
- 14.4.6. Conversational access to integrated data systems
- 14.5. Synthetic evaluation and performance metrics
- 14.5.1. Evaluation design and data simulation strategy
- 14.5.2. Framework capabilities evaluated in simulation
- 14.5.3. Theoretical outcomes and observations
- 14.5.4. Conceptual strengths and limitations
- 14.5.5. Synthesis of theoretical insights
- 14.6. Future directions
- 14.6.1. Explainable AI for semantic transformation
- 14.6.2. Federated benchmarking and simulation across institutions
- 14.6.3. Ontology-driven integration and schema alignment
- 14.6.4. Ethical, regulatory and societal considerations
- 14.7. Conclusion
- 14.8. References
- Chapter 15. Cybersecurity and Zero Trust Architectures in Supply Chains
- 15.1. Introduction
- 15.2. Literature review
- 15.3. Architectures in supply chain landscape
- 15.3.1. The modern supply chain threat landscape
- 15.3.2. The failure of traditional security models
- 15.3.3. ZTA: the new paradigm
- 15.4. The pillars of Zero Trust in the supply chain context
- 15.4.1. Pillar 1: identity
- 15.4.2. Pillar 2: device
- 15.4.3. Pillar 3: network
- 15.4.4. Pillar 4: application and workload
- 15.4.5. Pillar 5: data
- 15.5. Implementing ZTA: an architectural shift
- 15.6. Zero Trust for next-generation supply chain technologies
- 15.7. Technical challenges/limitations
- 15.8. Future enhancements
- 15.9. Conclusion
- 15.10. References
- Chapter 16. Additive Manufacturing and the Rise of Digital Inventory
- 16.1. Introduction: the burden of physical inventory
- 16.2. Defining the digital inventory paradigm
- 16.3. Additive manufacturing as the enabling technology
- 16.4. Strategic benefits: resilience, agility and cost redefinition
- 16.4.1. Enhancing supply chain resilience
- 16.4.2. Enabling mass customization and personalization
- 16.4.3. Reducing warehousing and logistics costs
- 16.5. The sustainability imperative: waste reduction and localized production
- 16.6. Implementation challenges and considerations
- 16.6.1. Intellectual property and digital file security
- 16.6.2. Quality control and standardization
- 16.6.3. Materials and technological limitations
- 16.7. Future horizons: integrating digital inventory with AI and IoT
- 16.7.1. The role of AI: from reactive to predictive and generative
- 16.7.2. The role of IoT: the sensory nervous system
- 16.7.3. The converged system: the self-optimizing supply network
- 16.8. Conclusion: a roadmap for adoption
- 16.9. References
- Chapter 17. Ethical and Social Governance of AI-enabled Supply Chains
- 17.1. Introduction: the imperative for ethical AI in global supply chains
- 17.1.1. Defining ethical and social governance in the context of AI
- 17.1.2. Why AI introduces unique ethical risks in complex, global supply networks
- 17.1.3. The convergence of regulatory pressure, consumer expectations and investor focus
- 17.1.4. Chapter roadmap
- 17.2. Core ethical challenges posed by supply chain AI
- 17.2.1. Algorithmic bias and discrimination
- 17.2.2. Lack of transparency and explainability ("black box" problem)
- 17.2.3. Accountability and responsibility
- 17.2.4. Data privacy and security
- 17.2.5. Labor displacement and workforce transformation
- 17.2.6. Environmental impact
- 17.3. Societal implications and stakeholder perspectives
- 17.3.1. Impact on workers
- 17.3.2. Impact on suppliers (especially SMEs)
- 17.3.3. Impact on local communities
- 17.3.4. Consumer trust and transparency
- 17.4. Frameworks for ethical AI governance in supply chains
- 17.4.1. Foundational principles
- 17.4.2. Regulatory landscape
- 17.4.3. Industry standards and best practices
- 17.4.4. Ethical AI design and development lifecycle
- 17.5. Implementing social governance: beyond compliance
- 17.5.1. Human oversight and control ("human-in-the-loop/command")
- 17.5.2. Stakeholder engagement and co-creation
- 17.5.3. Fair labor practices and just transition
- 17.5.4. Diversity, equity and inclusion (DEI) in AI teams and data
- 17.6. Building the governance infrastructure
- 17.6.1. AI Ethics Boards and Committees
- 17.6.2. AI auditing and impact assessments
- 17.6.3. Transparency and explainability tools
- 17.6.4. Whistleblower mechanisms and grievance redress
- 17.7. Metrics, reporting and continuous improvement
- 17.7.1. Defining key performance indicators (KPIs) for ethical and social performance of AI systems
- 17.7.2. Integrating ethical AI metrics into broader ESG reporting frameworks
- 17.7.3. Establishing feedback loops for continuous monitoring and improvement of AI governance practices
- 17.8. Case studies: navigating ethical dilemmas
- 17.8.1. Illustrative examples (hypothetical or anonymized real-world) covering
- 17.8.2. Mitigating bias in AI-driven supplier selection
- 17.8.3. Implementing explainable AI for logistics routing affecting delivery workers
- 17.8.4. Managing workforce transitions due to warehouse automation
- 17.9. Conclusion: toward responsible and trustworthy AI-powered supply chains
- 17.9.1. Summarizing the critical role of ethical and social governance
- 17.9.2. Key takeaways for leaders: integrating ethics as a core strategic pillar
- 17.9.3. The ongoing journey
- 17.10. References
- Chapter 18. Revolutionizing Supply Chains with Artificial Intelligence and Machine Learning: A Conceptual Model
- 18.1. Introduction
- 18.2. Literature review
- 18.3. Methodology
- 18.4. Conceptual model
- 18.4.1. AI/ML capabilities (input layer)
- 18.4.2. SCM functional integration (process layer/mediators)
- 18.4.3. Performance outcomes (output layer)
- 18.5. Findings
- 18.6. Implications
- 18.7. Conclusion
- 18.8. Future research directions
- 18.9. References
- Chapter 19. Enabling AI in Supply Chain Transformation: An MCDM-Based Analysis of Critical Success Factors
- 19.1. Introduction
- 19.1.1. Background
- 19.1.2. Objective of study
- 19.2. Literature review
- 19.3. Methodology
- 19.3.1. Framework of methodology
- 19.3.2. Sensitivity analysis
- 19.4. Findings and discussion
- 19.4.1. Implications of study
- 19.5. Conclusion and future work
- 19.6. References
- Chapter 20. Sustainable Intelligence: Aligning Ethical AI in Global Supply Chain Systems
- 20.1. Introduction
- 20.2. Review of the literature
- 20.2.1. The integration of AI mechanisms and sustainable supply chain practices
- 20.2.2. Ethical dimensions of AI usage in global supply chain networks
- 20.2.3. Governance, transparency and accountability in AI-enabled supply chain management systems
- 20.3. Research gap
- 20.4. Theoretical framework
- 20.5. Proposed framework concerning sustainable intelligence in AI-enabled supply chains
- 20.6. Implications of the study
- 20.6.1. Development of binding standards for ethical AI in the supply chain
- 20.6.2. Establishment of sustainability metrics in AI mechanisms
- 20.6.3. Adoption of value-sensitive design as a core principle of the intelligent supply chain system
- 20.6.4. Development of governance structures considering AI oversight
- 20.6.5. Ensuring inclusive and equitable AI deployment
- 20.7. Conclusion and scope for future research work
- 20.8. References
- List of Authors
- Index
- Other titles from ISTE in Computer Engineering
- EULA
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