Cited by

• 2023Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability275 cited
• 2023Federated machine learning for privacy preserving, collective supply chain risk prediction137 cited
• 2024Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda108 cited
• 2023Assessing supply chain responsiveness, resilience and robustness (Triple-R) by computer simulation: a systematic review of the literature86 cited
• 2025Intelligent Supply Chain Management: A Systematic Literature Review on Artificial Intelligence Contributions86 cited
• 2025State of the Art of Digital Twins in Improving Supply Chain Resilience75 cited
• 2023Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions66 cited
• 2024A cyclic and holistic methodology to exploit the Supply Chain Digital Twin concept towards a more resilient and sustainable future61 cited
• 2025Towards resilient and viable supply chains: a multidimensional model and empirical analysis56 cited
• 2024Digital twin model with machine learning and optimization for resilient production–distribution systems under disruptions54 cited
• 2024Internet of behaviors: conceptual model, practical and theoretical implications for supply chain and operations management47 cited
• 2025Operations & supply chain management: principles and practice46 cited
• 2024Cash flow dynamics in the supply chain during and after disruptions43 cited
• 2024From Theory to Practice: Leveraging Digital Twin Technologies and Supply Chain Disruption Mitigation Strategies for Enhanced Supply Chain Resilience with Strategic Fit in Focus41 cited
• 2025No risk, no fun? A bioinspired adaptation-based framework for supply chain resilience in Industry 5.037 cited
• 2024Application of simulation and machine learning in supply chain management: A synthesis of the literature using the Sim-ML literature classification framework37 cited