Next-Gen Supply Chains

Name: Next-Gen Supply Chains | AI, Automation, and Sustainability in a Disrupted World
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AI, Automation, and Sustainability in a Disrupted World

Pankaj Bhambri Pushan Kumar Dutta Mudassir Khan Marta Starostka-Patyk(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 25. Februar 2026

405 Seiten

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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
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