Digital Twin-Driven Logistics Coordination for High-Stakes Institutional Operations: A Framework for Real-Time Planning and Contingency Management

@article{1722758,
      author = {Adeola Olajubu},
      title = {Digital Twin-Driven Logistics Coordination for High-Stakes Institutional Operations: A Framework for Real-Time Planning and Contingency Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {361-386},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722758.pdf},
      abstract = {The growing complexity and dependency of institutional operations are leading to the need for logistics systems which are able to provide real-time planning, resource allocation coordination, risk prediction, and contingency planning. Traditional logistics frameworks are heavily based on static plans, fragmentary data analysis, and reactive decision-making. Such approaches limit the capacity of institutions to react efficiently to dynamic changes in operational situations. This study explores the possibilities of digital twin technology as a mechanism for integration and coordination of logistics operations in high-risk situations. The conceptual model for this study was developed based on the connection of real-time operational data, digital twin, prediction intelligence, scenario simulation, logistics coordination, and contingency management. Relying on the recent literature in the field of digital twins, logistics systems, supply chain resilience, and intelligent decision support, this model outlines the continuous cycle of institution operations in which institutions will be able to detect changes in operational conditions, synchronize the operational data, make predictions about possible risks, test different scenarios, coordinate resources, intervene in case of emergencies, and learn from operations. The research further identifies critical implementation considerations, including data quality, interoperability, cybersecurity, model reliability, governance, scalability, and human oversight. The presented framework sees the digital twin not just as a monitoring or visualization tool, but as an intelligent infrastructure that is capable of providing support in making decisions during the logistics coordination process. According to the research findings, logistics based on digital twins have great potential for improving institutional resilience by allowing the early identification of disruptions, planning in response to them, allocation of resources, contingency planning, and learning from organizational experiences.},
      keywords = {digital twins; logistics coordination; institutional operations; real-time planning; contingency management; supply-chain resilience; predictive analytics; digital transformation; operational resilience.},
      month = {September},
  }