Enterprise AI Analysis
Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: a systematic literature review
This study examines the integration of Artificial Intelligence (AI) in supply chain management (SCM) during the transition from Industry 4.0 to Industry 6.0. The focus is on improving operational efficiency, promoting human-centric collaboration, and advancing sustainability within supply chains. As industries progress, the need to incorporate Al technologies that improve decision-making and operational resilience while ensuring sustainable practices becomes increasingly critical. This systematic review aims to explore how Al is transforming SCM through these industrial transitions.
Executive Impact
Key metrics from the analysis highlight the significant potential of AI integration in supply chain management.
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Papers Included in Review
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
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Total Studies Included
PRISMA Review Process
Records Identified (n=34)
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Duplicates Removed (n=10)
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Records Screened (n=14)
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Reports Sought (n=14)
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Eligibility Assessed (n=14)
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Studies Included (n=14)
AI-Powered Human-Robot Collaboration in Manufacturing
A leading manufacturing firm implemented human-robot collaboration (HRC) systems, enhancing productivity by 20% and reducing error rates by 10%. This Industry 5.0-aligned approach combined human flexibility with robotic precision, leading to significant improvements in operational efficiency and worker safety.
Key Takeaway: Integrating human and AI capabilities fosters innovation and improves both operational outcomes and employee well-being.
AI's Evolving Role Across Industrial Revolutions
| Category | Industry 4.0 | Industry 5.0 | Industry 6.0 | Role of AI in Supply Chain |
|---|---|---|---|---|
| Focus | Automation, digitisation, smart manufacturing | Collaboration between humans and machines | Hyper-automation, self-optimising systems | AI helps drive automation, data analysis, and decision-making across supply chain stages. |
| Key Technologies | IoT, Big Data, AI, robotics, cloud computing | AI, collaborative robots (cobots), human-machine interfaces | AI, quantum computing, autonomous systems | AI facilitates predictive analytics, real-time monitoring, and optimisation. |
| Human-Machine Interaction | Predominantly machine-driven, limited human interaction | Human-centric, with greater emphasis on personal wellbeing | Human-AI symbiosis where AI systems can autonomously learn | AI evolves from supporting decision-making to self-learning and self-improving functions. |
| Sustainability | Initial steps towards green manufacturing and energy efficiency | Integration of sustainability goals with human-centric innovation | Fully embedded in all processes, creating closed-loop supply chains | AI supports sustainable practices through better demand forecasting and waste reduction. |
| Supply Chain Management (SCM) | Focus on improving efficiency and visibility through digitalisation | Focus on resilience and flexibility, integrating human insight | Seamless, intelligent, and sustainable supply chains, fully autonomous | AI revolutionises SCM by improving flexibility, predictive analytics, and demand planning. |
| AI Contribution | AI aids in data-driven automation and improves productivity | AI shifts to support human decision-making and augment human roles | AI achieves full autonomy, managing dynamic and complex systems | AI improves operational efficiency, reduces errors, and facilitates decision-making. |
| Challenges | Data integration, high initial costs, cybersecurity | Balancing human and machine interaction, ethics, AI transparency | Managing complex, self-regulating AI systems | Complexity of integration, ethical concerns, high infrastructure requirements |
| Benefits of AI Integration | Improved efficiency, lower operational costs, improved production | Human-centric systems, improved creativity, personalised outcomes | Fully autonomous and intelligent systems, minimal human intervention | Increased transparency, resilience, adaptability, optimised resource allocation |
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Your AI Implementation Roadmap
A phased approach to integrating AI into your enterprise supply chain management, from Industry 4.0 foundations to Industry 6.0's autonomous systems.
Stage 1: Industry 4.0 - Foundation of Digitalisation
Integration of digital technologies (IoT, big data, AI) into production procedures, resulting in intelligent factories. Focus on continuous monitoring, predictive maintenance, data-driven decision-making, operational efficiency, and productivity.
Stage 2: Industry 5.0 - Human-Centric Integration
Building upon Industry 4.0, this stage emphasizes the synergy between humans and technology. Key components include collaborative robots (cobots), AI, and sophisticated human-machine interfaces, promoting creativity, problem-solving, and customisation while ensuring human wellbeing.
Stage 3: Advanced AI Integration
Deep integration of machine learning, deep learning, and advanced analytics to optimize processes, increase decision-making, and improve predictive capabilities. AI-powered systems assess large volumes of data in real-time, detecting patterns, forecasting results, and independently making decisions.
Stage 4: Industry 6.0 - Sustainable and Cyber Physical Systems
The culmination of industrial evolution, emphasizing manufacturing ecosystems that are strong and sustainable. Achieved through the integration of advanced digital technology with circular economy ideas, renewable energy sources, and resource efficiency for environmental and social responsibility.
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