Literature review
| Authors and year | Focus of the study | Method | Key findings |
|---|---|---|---|
| Loske and Klumpp (2021) | AI-based route planning in retail logistics and operational efficiency | Case analysis; fuzzy DEA variants; AHP–SBM | AI-supported routing increases efficiency; differing managerial evaluations highlight customer-centric logistics; AI–human interaction contributes to efficiency and sustainability |
| Stecke and Mokhtarzadeh (2022) | Effects of human–robot collaboration (HRC) on productivity and ergonomics | MILP, constraint programming, Benders; ergonomic risk analysis | Balanced HRC improves productivity and reduces ergonomic risks; optimal setup near robot/station ratio 0.7; 37% mobile robots yield best results |
| Maddikunta et al. (2022) | Conceptualisation of Industry 5.0 and enabling technologies | Literature review | Industry 5.0 integrates human creativity with intelligent machines; key technologies: edge computing, digital twins, cobots, blockchain, 6G |
| Dwivedi et al. (2023) | Link between Industry 5.0 and circular supply chain (CSC) | m-TISM, MICMAC; interviews | Identifies 16 drivers strengthening CSC–Industry 5.0 synergy; strongest drivers: management support and organisational readiness |
| Grosse et al. (2023) | Human-centric, resilient, sustainable systems under Industry 5.0 | Conceptual synthesis | Highlights ethical, inclusive, capability-enhancing transformation; emphasises responsible resource use beyond productivity |
| Jayarathna et al. (2023) | Logistics transition to circular economy (CE) | Qualitative; Gioia method | Three themes: environmental protection, dynamic capabilities, social welfare; identifies 47 CE logistics practices; economic value creation dominates benefits |
| Ghobakhloo et al. (2023) | Micro-mechanisms for sustainable Industry 5.0 production | Literature review + strategic roadmap | Key contributors: value network integration, sustainable governance, business model innovation, skills development; renewable energy and resilience require higher collaboration |
| Pasparakis et al. (2023) | Human–robot collaboration effects on worker outcomes in warehouses | Real-effort experiment | HRC increases job satisfaction and self-evaluation; stronger effects under robot-following scenarios |
| Karmaker et al. (2023) | Industry 5.0 for post-COVID supply chain continuity | BWM, ISM, MICMAC | Critical factors: top management support, incentives; Industry 5.0 strengthens sustainability via resource efficiency and resilience |
| Shebeshe and Sharma (2024) | SSCM impact on competitive advantage and performance | Survey (221 firms); SEM (PLS) | SSCM enhances performance directly and via competitive advantage mediating effect |
| Hsu et al. (2024) | Industry 5.0 drivers for reducing SSCRs and improving SC resilience | QFD; FDM, FDISM, ANP, TOPSIS | 20 Industry 5.0 drivers strengthen resilience and reduce risks; most critical: responsible consumption, justice, trust, innovation, energy efficiency |
| Andres et al. (2024) | Logistics 5.0 and Industry 5.0 technologies in smart logistics | Literature + real case qualitative | AI, digital twins, big data and autonomous systems improve efficiency in distribution, inventory and transport; key enablers of sustainable logistics |
| Fani et al. (2024) | Synergies among Lean, Industry 4.0 and Industry 5.0; Lean 5.0 | Literature + case study | Defines Lean 5.0 framework; Lean human-centricity supported by Industry 4.0 tech accelerates transition to Industry 5.0 |
| Sharma and Gupta (2024a) | Clean production + Industry 5.0 strategies for competitive advantage | BWM, Grey DEMATEL, GRID | Most influential strategies: AI–IoT optimisation, blockchain; digital twins less impactful in causality ranking |
| Sharma and Gupta (2024b) | Cognitive digital twins (CDTs) for sustainability and inclusivity | TOE-HOT; BWM, ISM, MICMAC | Technology dimension critical; CDTs enable real-time optimisation; strategic alignment, skills, safety, compliance vital for inclusive sustainability |
| Rame et al. (2024) | Industry 5.0, sustainability and innovation dynamics | Interdisciplinary review | Industry 5.0 integrates human expertise with advanced technology to balance growth and environmental management; identifies emerging sustainable practices |
| Jamil et al. (2024) | Industry 5.0 + SSCM effects on SCP and SCR | Survey (342 professionals); SEM | Industry 5.0 enhances performance and reduces risks via sustainable supply chain practices; convergence supports new sustainability-oriented business models |
| Locatelli et al. (2024) | Social sustainability and human-centred digitalisation | Survey-based analysis | Positive innovation attitudes but low human-centred maturity; transition requires trust-based human–machine synergies and employee well-being strategies |
| Wu et al. (2024) | AI strategies for resilience and sustainability in logistics | PA, B-BWM, Pareto | IoT monitoring, CPP, digital twins boost resilience and sustainability; AI improves safety and flow; blockchain promising but regulatory challenges remain |
| Nazarian and Khan (2024) | Industry 5.0 impact on supply chain performance | Survey; PLS-SEM | Industry 5.0 improves performance directly and indirectly; visibility strengthens responsiveness and efficiency |
| Keshvarparast et al. (2024) | Effects of cobot integration on productivity, flexibility, human factors | Systematic review | Cobots increase system flexibility, safety and working conditions; potential risks (job loss) noted; classification of collaboration types provided |
| Laddha and Agrawal (2024) | Barriers to Industry 5.0 adoption for sustainable supply chains | Literature + expert interviews; DEMATEL | Four barriers: technological, organisational, regulatory, economic; despite barriers, Industry 5.0 supports environmental and social sustainability |
| Nasir et al. (2025) | Human-centric Industry 5.0 process–system–management framework | Conceptual | Framework integrates roles (operator, designer, consumer, society) with digital tools; supports circular, resilient production |
| Płaza et al. (2025) | Ergonomics and human factors in Industry 4.0/5.0 | Systematic review | Ergonomic design of IoT, AI, AR systems improves safety, comfort and performance; aligns technological and human well-being goals |
| Zia and Haleem (2025) | Federated learning, cobots, autonomous systems synergy | SLR (92 studies) | Interaction of FL, cobots, AS improves adaptability, resilience, sustainability; identifies research gaps and strategic frameworks |
| Wu et al. (2025) | AI for resilience and sustainability in RMG/footwear supply chains | PA, B-BWM, Pareto | IoT monitoring, CPP, digital twins, RFID improve resilience and environmental outcomes; workforce safety and data security essential |
| Katariya et al. (2025) | HRMI and automation impacts in manufacturing | Conceptual + SWOT | HRMI enhances efficiency, safety, sustainability; implementation barriers and opportunities identified |
| Kharayat and Gupta (2025) | Circular economy drivers for resource efficiency | Grey causal modelling | Material substitution most effective in reducing impact; lifecycle extension and digital technologies critical; stakeholder collaboration key |
| Yasari et al. (2025) | Ergonomics-integrated scheduling for worker well-being | Scheduling algorithm; numerical experiments | Ergonomic task assignment reduces risks with minimal time cost; improves overall productivity by lowering injury incidence |
| Lin (2025) | AI-based decision support (UNISONE) for global supply chains | Case study + simulations | AI enhances efficiency, delivery speed, carbon efficiency and agility; confirms strategic role of AI-enabled human–machine collaboration |
| Sonar et al. (2025) | Challenges to achieving Industry 5.0-based carbon neutrality | Fuzzy Delphi + Neutrosophic DEMATEL | Biggest barrier: supply chain complexity; requires communication, transparency, collaboration; Industry 5.0 tech increases efficiency and sustainability |
| Dacre et al. (2025) | Supply Chain 5.0 conceptual framework | Literature + thematic analysis | Industry 5.0 strengthens resilience and capability through human–machine collaboration |
| Wang et al. (2025) | Human factors (job satisfaction) and logistics performance | Survey; PLS-SEM | Job satisfaction strongly improves logistics performance; innovation and responsiveness mediate this relationship |
| Authors and year | Focus of the study | Method | Key findings |
|---|---|---|---|
| AI-based route planning in retail logistics and operational efficiency | Case analysis; fuzzy DEA variants; AHP–SBM | AI-supported routing increases efficiency; differing managerial evaluations highlight customer-centric logistics; AI–human interaction contributes to efficiency and sustainability | |
| Effects of human–robot collaboration (HRC) on productivity and ergonomics | MILP, constraint programming, Benders; ergonomic risk analysis | Balanced HRC improves productivity and reduces ergonomic risks; optimal setup near robot/station ratio 0.7; 37% mobile robots yield best results | |
| Conceptualisation of Industry 5.0 and enabling technologies | Literature review | Industry 5.0 integrates human creativity with intelligent machines; key technologies: edge computing, digital twins, cobots, blockchain, 6G | |
| Link between Industry 5.0 and circular supply chain (CSC) | m-TISM, MICMAC; interviews | Identifies 16 drivers strengthening CSC–Industry 5.0 synergy; strongest drivers: management support and organisational readiness | |
| Human-centric, resilient, sustainable systems under Industry 5.0 | Conceptual synthesis | Highlights ethical, inclusive, capability-enhancing transformation; emphasises responsible resource use beyond productivity | |
| Logistics transition to circular economy (CE) | Qualitative; Gioia method | Three themes: environmental protection, dynamic capabilities, social welfare; identifies 47 CE logistics practices; economic value creation dominates benefits | |
| Micro-mechanisms for sustainable Industry 5.0 production | Literature review + strategic roadmap | Key contributors: value network integration, sustainable governance, business model innovation, skills development; renewable energy and resilience require higher collaboration | |
| Human–robot collaboration effects on worker outcomes in warehouses | Real-effort experiment | HRC increases job satisfaction and self-evaluation; stronger effects under robot-following scenarios | |
| Industry 5.0 for post-COVID supply chain continuity | BWM, ISM, MICMAC | Critical factors: top management support, incentives; Industry 5.0 strengthens sustainability via resource efficiency and resilience | |
| SSCM impact on competitive advantage and performance | Survey (221 firms); SEM (PLS) | SSCM enhances performance directly and via competitive advantage mediating effect | |
| Industry 5.0 drivers for reducing SSCRs and improving SC resilience | QFD; FDM, FDISM, ANP, TOPSIS | 20 Industry 5.0 drivers strengthen resilience and reduce risks; most critical: responsible consumption, justice, trust, innovation, energy efficiency | |
| Logistics 5.0 and Industry 5.0 technologies in smart logistics | Literature + real case qualitative | AI, digital twins, big data and autonomous systems improve efficiency in distribution, inventory and transport; key enablers of sustainable logistics | |
| Synergies among Lean, Industry 4.0 and Industry 5.0; Lean 5.0 | Literature + case study | Defines Lean 5.0 framework; Lean human-centricity supported by Industry 4.0 tech accelerates transition to Industry 5.0 | |
| Clean production + Industry 5.0 strategies for competitive advantage | BWM, Grey DEMATEL, GRID | Most influential strategies: AI–IoT optimisation, blockchain; digital twins less impactful in causality ranking | |
| Cognitive digital twins (CDTs) for sustainability and inclusivity | TOE-HOT; BWM, ISM, MICMAC | Technology dimension critical; CDTs enable real-time optimisation; strategic alignment, skills, safety, compliance vital for inclusive sustainability | |
| Industry 5.0, sustainability and innovation dynamics | Interdisciplinary review | Industry 5.0 integrates human expertise with advanced technology to balance growth and environmental management; identifies emerging sustainable practices | |
| Industry 5.0 + SSCM effects on SCP and SCR | Survey (342 professionals); SEM | Industry 5.0 enhances performance and reduces risks via sustainable supply chain practices; convergence supports new sustainability-oriented business models | |
| Social sustainability and human-centred digitalisation | Survey-based analysis | Positive innovation attitudes but low human-centred maturity; transition requires trust-based human–machine synergies and employee well-being strategies | |
| AI strategies for resilience and sustainability in logistics | PA, B-BWM, Pareto | IoT monitoring, CPP, digital twins boost resilience and sustainability; AI improves safety and flow; blockchain promising but regulatory challenges remain | |
| Industry 5.0 impact on supply chain performance | Survey; PLS-SEM | Industry 5.0 improves performance directly and indirectly; visibility strengthens responsiveness and efficiency | |
| Effects of cobot integration on productivity, flexibility, human factors | Systematic review | Cobots increase system flexibility, safety and working conditions; potential risks (job loss) noted; classification of collaboration types provided | |
| Barriers to Industry 5.0 adoption for sustainable supply chains | Literature + expert interviews; DEMATEL | Four barriers: technological, organisational, regulatory, economic; despite barriers, Industry 5.0 supports environmental and social sustainability | |
| Human-centric Industry 5.0 process–system–management framework | Conceptual | Framework integrates roles (operator, designer, consumer, society) with digital tools; supports circular, resilient production | |
| Ergonomics and human factors in Industry 4.0/5.0 | Systematic review | Ergonomic design of IoT, AI, AR systems improves safety, comfort and performance; aligns technological and human well-being goals | |
| Federated learning, cobots, autonomous systems synergy | SLR (92 studies) | Interaction of FL, cobots, AS improves adaptability, resilience, sustainability; identifies research gaps and strategic frameworks | |
| AI for resilience and sustainability in RMG/footwear supply chains | PA, B-BWM, Pareto | IoT monitoring, CPP, digital twins, RFID improve resilience and environmental outcomes; workforce safety and data security essential | |
| HRMI and automation impacts in manufacturing | Conceptual + SWOT | HRMI enhances efficiency, safety, sustainability; implementation barriers and opportunities identified | |
| Circular economy drivers for resource efficiency | Grey causal modelling | Material substitution most effective in reducing impact; lifecycle extension and digital technologies critical; stakeholder collaboration key | |
| Ergonomics-integrated scheduling for worker well-being | Scheduling algorithm; numerical experiments | Ergonomic task assignment reduces risks with minimal time cost; improves overall productivity by lowering injury incidence | |
| AI-based decision support (UNISONE) for global supply chains | Case study + simulations | AI enhances efficiency, delivery speed, carbon efficiency and agility; confirms strategic role of AI-enabled human–machine collaboration | |
| Challenges to achieving Industry 5.0-based carbon neutrality | Fuzzy Delphi + Neutrosophic DEMATEL | Biggest barrier: supply chain complexity; requires communication, transparency, collaboration; Industry 5.0 tech increases efficiency and sustainability | |
| Supply Chain 5.0 conceptual framework | Literature + thematic analysis | Industry 5.0 strengthens resilience and capability through human–machine collaboration | |
| Human factors (job satisfaction) and logistics performance | Survey; PLS-SEM | Job satisfaction strongly improves logistics performance; innovation and responsiveness mediate this relationship |
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