Overview and comparison of included studies
| Authors and year | Outlet | Paper category | Sample size and population | Reference to key themes | Key findings and contributions | Research gaps | Theoretical frameworks | Limitations |
|---|---|---|---|---|---|---|---|---|
| Belhadi et al. (2021) | Annals of Operations Research | Empirical (Survey) | SC managers, industry practitioners | AI, Supply Chain, Resilience, Impact | AI enhances supply chain resilience and performance amid dynamism. Empirical findings provide practical insights | Limited real-world case studies, need for industry-specific models | Resource-based view (RBV), dynamic capabilities | Focused on one industry sector, lack of deeper implementation insights |
| Cheong (2024) | Frontiers in Human Dynamics | Narrative literature review | AI system designers, policymakers | Legal and ethical challenges, AI, Transparency, Accountability | Ethical concerns in AI systems, transparency critical for accountability in decision-making | Lack of implementation framework for AI governance | Ethical decision-making, governance frameworks | Limited scope of empirical data on real-world applications |
| Dubey et al. (2022) | International Journal of Production Economics | Empirical Quantitative | Humanitarian organisations, SC managers | AI, Big data analytics, Agility, Resilience, SC | AI-driven analytics fosters agility and resilience in humanitarian supply chains | Limited focus on cross-organizational collaboration in analytics | Big data analytics, resource dependency theory | Only focused on humanitarian sector, limited application to commercial supply chains |
| Ekundayo (2024) | International Journal of Research Publication and Reviews | Conceptual study | Engineers, decision-makers | AI, DL, Reinforcement Learning, Complex System | AI optimizes decision-making processes in complex systems engineering | Lack of field-based validation of AI models in real systems | Decision theory, AI-driven decision intelligence | Small-scale focus, no real-life system validation in complex scenarios |
| Eyo-Udo (2024) | Journal of Multidisciplinary Studies | Conceptual Review – Qualitative | SC managers, industry experts | AI, SCM, Benefits, Challenges | AI optimizes logistics, inventory management, and overall supply chain performance | Industry-specific challenges, lack of data on adoption barriers | Optimization theory, AI application models | No empirical data from diverse industries or regions |
| Fosso Wamba et al. (2024) | International Journal of Production Research | Empirical – Quantitative | SC managers, researchers | Generative AI, Benefits, Challenges, SCM | Key benefits and challenges of generative AI in supply chain management | Limited to exploratory research, needs deeper case studies | Technology adoption models, innovation diffusion | Non-quantitative analysis, no clear performance metrics |
| Gupta et al. (2021) | IEEE Transactions on Engineering Management | Qualitative Empirical | SC leaders, IT managers | Supply Chain, AI, Resilience | AI enhances resilience through advanced data analytics and real-time decision-making | Limited focus on the cross-organization impact | Contingency theory, strategic alignment | Limited to AI-driven information systems, no comparison with other technologies |
| Hofmann et al. (2019) | International Journal of Physical Distribution and Logistics Management | Conceptual Review – Qualitative | Scholars, industry practitioners | Industry 4.0, Digital Transformation of SC, IoT | Industry 4.0 and digital technologies, including AI, drive transformation in SCM, improving efficiency and flexibility | Lack of focus on small-medium enterprises (SMEs) | Industry 4.0, technology adoption theory | Focus on general trends rather than specific supply chain sectors |
| Khan et al. (2022) | Sustainability | Quantitative – Empirical | SC managers, transparency experts | Supply Chain, AI, Traceability, Transparency, Tracking | Technologies like blockchain improve traceability and transparency, enhancing supply chain performance | Limited understanding of implementation challenges in complex supply chains | Blockchain theory, transparency frameworks | Limited to theoretical and pilot case studies |
| Khlie et al. (2024) | Journal of Infrastructure, Policy and Development | Conceptual Review – Qualitative | SC professionals, AI practitioners | GenAI, SCM, Predictive Maintenance | Generative AI models improve efficiency and strategic planning in supply chain management | Need for practical industry applications | Generative AI, technology adoption models | Non-empirical, focused on theoretical models without real-world validation |
| Liu et al. (2024) | Supply Chain Management: An International Journal | Empirical – Quantitative | SC managers, blockchain adopters | Blockchain, SCR, Disruptions | Blockchain adoption improves resilience through transparent, secure supply chain processes | Further study needed on leadership’s role in adoption and its practical integration | Transformational leadership theory | Limited research in non-blockchain contexts |
| Manning et al. (2022) | Trends in Food Science and Technology | Conceptual Review – Qualitative | Food industry professionals, SC experts | AI, Ethical Considerations | AI’s ethical challenges, focusing on fairness, transparency, and accountability in the food supply chain | Lack of cross-sector comparison of AI ethics | Ethics in AI, technology governance frameworks | Limited sectoral scope, focusing mainly on food supply chains |
| Modgil et al. (2022) | The International Journal of Logistics Management | Qualitative Empirical Research | SC managers, practitioners | Supply Chain, Resilience, Capabilities | AI’s role in enhancing resilience during disruptions, with a focus on Covid-19 supply chain impacts | Lack of post-pandemic studies and real-world application data | Resilience theory, technological adaptation | No empirical data beyond the pandemic context |
| Munir et al. (2024) | Technological Forecasting and Social Change | Empirical – Quantitative | SC managers, risk managers | SC, Risk Management, SCR, Decision-making | AI, analytics, and ambidexterity contribute to building resilient supply chains amidst risks | Limited empirical evidence from different sectors | Ambidexterity theory, risk management frameworks | No industry-specific deep dives, limited focus on human aspects of resilience |
| Okeleke et al. (2024) | International Journal of Engineering Research Updates | Empirical – Quantitative | Consumers, marketers, SC managers | Predictive analytics, AI, Market trends | AI models predict consumer behavior trends, helping businesses optimize supply chain decisions | Lack of consumer diversity, limited focus on different market dynamics | Predictive analytics, consumer behavior theory | Limited geographical scope and sample diversity |
| Olawale et al. (2024) | Magna Scientia Advanced Research and Reviews | Empirical – Qualitative | HR professionals, risk managers | Risk management, SC, Resilience | Human resources practices help manage risks, ensuring smooth supply chain operations | Lack of deep dive into specific risk management techniques | HR management, risk management models | Small sample size, focus on HR practices without broader perspectives |
| Pasupuleti et al. (2024) | Logistics | Empirical – Quantitative | SC managers, data scientists | SC, Agility, Sustainability, Machine Learning | Machine learning enhances supply chain sustainability by optimizing logistics and inventory | Focused more on technology, less on organizational adoption challenges | Machine learning models, supply chain agility | Limited data on organizational readiness for AI adoption |
| Rane et al. (2024) | SSRN | Empirical – Qualitative | SC professionals, logistics experts | AI, ML, Resilience, Sustainability, SCM | AI and machine learning improve resilience and sustainability in logistics and supply chain systems | Needs integration with existing non-AI systems, challenges with data privacy | Machine learning, AI in logistics theory | Small empirical sample, reliance on technology-centric views |
| Riad et al. (2024) | Logistics | Empirical – Qualitative | SC professionals, managers | AI, Sustainability, SC, Optimisation | AI fosters resilience through improved decision-making processes and enhanced supply chain optimization | Lack of in-depth case studies to understand barriers to AI adoption | AI adoption frameworks, resilience theory | Limited theoretical application in specific industries |
| Richey et al. (2023) | Journal of Business Logistics | Review | Academics, industry experts | AI, Supply Chain Management | AI’s transformative role in logistics and supply chain management through automation, predictive analytics, and decision support | Need for more empirical research across various industries | Technology adoption, decision support systems | Focus on academic perspectives, lack of real-world application data |
| Singh et al. (2019) | Journal of Industrial Engineering International | Conceptual Review – Qualitative | SC professionals, academics | SCR, Risk management | Identified performance indicators that can measure and improve supply chain resilience | Lack of alignment with new-age technologies like AI | Performance measurement, resilience theory | Theoretical framework, no application to modern technologies like AI |
| Singh and Modgil (2024) | International Journal of Productivity and Performance Management | Empirical – Quantitative | SC managers, academics | Agility, Capacity, SCR | Agility and absorptive capacity improve resilience, especially in disruptive contexts | Limited application in specific industry contexts | Organizational theory, resilience models | No empirical research beyond the conceptual model |
| Sun et al. (2023) | Journal of Enterprise Information Management | Quantitative Empirical Research (Survey) | SMEs in various sectors | Supply Chain, Resilience, Risk, Disruption | Deep learning improves risk management for SMEs in the face of supply chain disruptions like Covid-19 | Limited scope to SMEs only, need for broader sectoral research | Deep learning models, resilience theory | Limited to SMEs, no comparison with large enterprises |
| Yuan et al. (2023) | Journal of Enterprise Information Management | Quantitative Empirical Research (Survey) | SC managers, digital transformation experts | Supply Chain, Resilience, Digital Transformation | Digital transformation enhances supply chain resilience through integration of new technologies | Need for empirical validation of the model in different sectors | Digital transformation models, resilience theory | Theoretical focus, lacking real-life testing across diverse industries |
| Zamani et al. (2022) | Annals of Operations Research | Systematic Literature Review | SC managers, AI professionals | AI, Supply Chain, Resilience | AI and big data enhance supply chain resilience by optimizing decision-making and performance | More industry-specific case studies needed | Big data analytics, AI adoption theories | Theoretical review, no primary data or empirical case studies |
| Authors and year | Outlet | Paper category | Sample size and population | Reference to key themes | Key findings and contributions | Research gaps | Theoretical frameworks | Limitations |
|---|---|---|---|---|---|---|---|---|
| Empirical (Survey) | SC managers, industry practitioners | AI, Supply Chain, Resilience, Impact | AI enhances supply chain resilience and performance amid dynamism. Empirical findings provide practical insights | Limited real-world case studies, need for industry-specific models | Resource-based view (RBV), dynamic capabilities | Focused on one industry sector, lack of deeper implementation insights | ||
| Narrative literature review | AI system designers, policymakers | Legal and ethical challenges, AI, Transparency, Accountability | Ethical concerns in AI systems, transparency critical for accountability in decision-making | Lack of implementation framework for AI governance | Ethical decision-making, governance frameworks | Limited scope of empirical data on real-world applications | ||
| Empirical Quantitative | Humanitarian organisations, SC managers | AI, Big data analytics, Agility, Resilience, SC | AI-driven analytics fosters agility and resilience in humanitarian supply chains | Limited focus on cross-organizational collaboration in analytics | Big data analytics, resource dependency theory | Only focused on humanitarian sector, limited application to commercial supply chains | ||
| Conceptual study | Engineers, decision-makers | AI, DL, Reinforcement Learning, Complex System | AI optimizes decision-making processes in complex systems engineering | Lack of field-based validation of AI models in real systems | Decision theory, AI-driven decision intelligence | Small-scale focus, no real-life system validation in complex scenarios | ||
| Conceptual Review – Qualitative | SC managers, industry experts | AI, SCM, Benefits, Challenges | AI optimizes logistics, inventory management, and overall supply chain performance | Industry-specific challenges, lack of data on adoption barriers | Optimization theory, AI application models | No empirical data from diverse industries or regions | ||
| Empirical – Quantitative | SC managers, researchers | Generative AI, Benefits, Challenges, SCM | Key benefits and challenges of generative AI in supply chain management | Limited to exploratory research, needs deeper case studies | Technology adoption models, innovation diffusion | Non-quantitative analysis, no clear performance metrics | ||
| Qualitative Empirical | SC leaders, IT managers | Supply Chain, AI, Resilience | AI enhances resilience through advanced data analytics and real-time decision-making | Limited focus on the cross-organization impact | Contingency theory, strategic alignment | Limited to AI-driven information systems, no comparison with other technologies | ||
| Conceptual Review – Qualitative | Scholars, industry practitioners | Industry 4.0, Digital Transformation of SC, IoT | Industry 4.0 and digital technologies, including AI, drive transformation in SCM, improving efficiency and flexibility | Lack of focus on small-medium enterprises (SMEs) | Industry 4.0, technology adoption theory | Focus on general trends rather than specific supply chain sectors | ||
| Quantitative – Empirical | SC managers, transparency experts | Supply Chain, AI, Traceability, Transparency, Tracking | Technologies like blockchain improve traceability and transparency, enhancing supply chain performance | Limited understanding of implementation challenges in complex supply chains | Blockchain theory, transparency frameworks | Limited to theoretical and pilot case studies | ||
| Conceptual Review – Qualitative | SC professionals, AI practitioners | GenAI, SCM, Predictive Maintenance | Generative AI models improve efficiency and strategic planning in supply chain management | Need for practical industry applications | Generative AI, technology adoption models | Non-empirical, focused on theoretical models without real-world validation | ||
| Empirical – Quantitative | SC managers, blockchain adopters | Blockchain, SCR, Disruptions | Blockchain adoption improves resilience through transparent, secure supply chain processes | Further study needed on leadership’s role in adoption and its practical integration | Transformational leadership theory | Limited research in non-blockchain contexts | ||
| Conceptual Review – Qualitative | Food industry professionals, SC experts | AI, Ethical Considerations | AI’s ethical challenges, focusing on fairness, transparency, and accountability in the food supply chain | Lack of cross-sector comparison of AI ethics | Ethics in AI, technology governance frameworks | Limited sectoral scope, focusing mainly on food supply chains | ||
| Qualitative Empirical Research | SC managers, practitioners | Supply Chain, Resilience, Capabilities | AI’s role in enhancing resilience during disruptions, with a focus on Covid-19 supply chain impacts | Lack of post-pandemic studies and real-world application data | Resilience theory, technological adaptation | No empirical data beyond the pandemic context | ||
| Empirical – Quantitative | SC managers, risk managers | SC, Risk Management, SCR, Decision-making | AI, analytics, and ambidexterity contribute to building resilient supply chains amidst risks | Limited empirical evidence from different sectors | Ambidexterity theory, risk management frameworks | No industry-specific deep dives, limited focus on human aspects of resilience | ||
| Empirical – Quantitative | Consumers, marketers, SC managers | Predictive analytics, AI, Market trends | AI models predict consumer behavior trends, helping businesses optimize supply chain decisions | Lack of consumer diversity, limited focus on different market dynamics | Predictive analytics, consumer behavior theory | Limited geographical scope and sample diversity | ||
| Empirical – Qualitative | HR professionals, risk managers | Risk management, SC, Resilience | Human resources practices help manage risks, ensuring smooth supply chain operations | Lack of deep dive into specific risk management techniques | HR management, risk management models | Small sample size, focus on HR practices without broader perspectives | ||
| Empirical – Quantitative | SC managers, data scientists | SC, Agility, Sustainability, Machine Learning | Machine learning enhances supply chain sustainability by optimizing logistics and inventory | Focused more on technology, less on organizational adoption challenges | Machine learning models, supply chain agility | Limited data on organizational readiness for AI adoption | ||
| Empirical – Qualitative | SC professionals, logistics experts | AI, ML, Resilience, Sustainability, SCM | AI and machine learning improve resilience and sustainability in logistics and supply chain systems | Needs integration with existing non-AI systems, challenges with data privacy | Machine learning, AI in logistics theory | Small empirical sample, reliance on technology-centric views | ||
| Empirical – Qualitative | SC professionals, managers | AI, Sustainability, SC, Optimisation | AI fosters resilience through improved decision-making processes and enhanced supply chain optimization | Lack of in-depth case studies to understand barriers to AI adoption | AI adoption frameworks, resilience theory | Limited theoretical application in specific industries | ||
| Review | Academics, industry experts | AI, Supply Chain Management | AI’s transformative role in logistics and supply chain management through automation, predictive analytics, and decision support | Need for more empirical research across various industries | Technology adoption, decision support systems | Focus on academic perspectives, lack of real-world application data | ||
| Conceptual Review – Qualitative | SC professionals, academics | SCR, Risk management | Identified performance indicators that can measure and improve supply chain resilience | Lack of alignment with new-age technologies like AI | Performance measurement, resilience theory | Theoretical framework, no application to modern technologies like AI | ||
| Empirical – Quantitative | SC managers, academics | Agility, Capacity, SCR | Agility and absorptive capacity improve resilience, especially in disruptive contexts | Limited application in specific industry contexts | Organizational theory, resilience models | No empirical research beyond the conceptual model | ||
| Quantitative Empirical Research (Survey) | SMEs in various sectors | Supply Chain, Resilience, Risk, Disruption | Deep learning improves risk management for SMEs in the face of supply chain disruptions like Covid-19 | Limited scope to SMEs only, need for broader sectoral research | Deep learning models, resilience theory | Limited to SMEs, no comparison with large enterprises | ||
| Quantitative Empirical Research (Survey) | SC managers, digital transformation experts | Supply Chain, Resilience, Digital Transformation | Digital transformation enhances supply chain resilience through integration of new technologies | Need for empirical validation of the model in different sectors | Digital transformation models, resilience theory | Theoretical focus, lacking real-life testing across diverse industries | ||
| Systematic Literature Review | SC managers, AI professionals | AI, Supply Chain, Resilience | AI and big data enhance supply chain resilience by optimizing decision-making and performance | More industry-specific case studies needed | Big data analytics, AI adoption theories | Theoretical review, no primary data or empirical case studies |
Source(s): Authors’ own work