AI-Augmented Project Portfolio Digital Twin for Dynamic Capacity, Dependency, and Risk Management: A Systematic Review
DOI:
https://doi.org/10.71238/snnst.v2i02.234Keywords:
Artificial Intelligence; Design Structure Matrix; Digital Twin; Organizational Digital Twin; Portfolio Risk Management; Project Interdependencies; Project Portfolio Management; Resource Capacity Planning; Resource-Constrained Project SchedulingAbstract
Project portfolios in engineering, construction and technology firms experience resource sharing, complex cross-project relations, and correlated risks, and even though they are managed by a shared resource pool, project decisions are primarily made based on those static, periodic reports. Based on the available peer-reviewed literature published from 2001 to 2024, this systematic review brings together six streams of literature: digital twin foundations, built-environment twins, organizational twins, portfolio and risk governance, artificial intelligence (AI) for project management, and planning and dependency modelling, to develop the conceptual and technical basis of an AI-augmented project portfolio digital twin. Sources were classified in terms of capacity, dependency functions and in terms of the integration of the digital model, digital shadow and digital twin. The synthesis reveals that despite being conceptually unified, classified implementations still remain dominated by digital shadows (35%) and digital models (28%). From the survey of 175 to 182 firms, portfolio research evidence is strong on the link between front-end quality and portfolio success, and strategic control and portfolio risk management. AI evidence is wide-ranging – one scientometric review includes 4,473 articles – and expert opinion highlights schedule, cost and risk management as knowledge areas that are most vulnerable to AI. Proposed a 5-layer reference architecture for the resource constraint scheduling, RSM propagation and AI forecasting in a closed loop, predict and decide loop. Some estimates suggest that ignoring the dependencies would result in approximately 55% underestimate in aggregate exposure and approximately 10 months in the completion time of the P80 exposure. The key issues to overcome are data integration, model fidelity, explainability, and the value for these stakeholders.
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