A Decision Support Tool for Determining the Suitable Multifunctional Digital Twin Maturity Levels in Construction Projects

Digital twin (DT) is a purpose-driven technology, and its suitable maturity level for a specific project depends on the project’s requirements, informed by the adoption drivers that may reflect both project-level needs and organizational strategy. However, to date, no tool or framework exists to assist decision makers in determining the suitable DT maturity level for a project, while a mismatched level of maturity results in the failure of the project to achieve its objectives. To fill this gap, this research aims to develop a decision support tool (DST) to determine the suitable DT maturity level for a project, considering the significance of the drivers for DT adoption as well as the current state of the project. To this end, the drivers for adopting DTs are identified using a novel large language model–based multicriteria decision-making approach. Then, given the limitations of existing maturity model for multifunctional DTs, a novel four-dimensional DT maturity model (including data and technology, business, decision making, and the relationship between the DT and the physical asset) is developed using a design science research approach. The suitable maturity ranges for each driver are then identified across each dimension. Finally, a DST is developed and validated using expert opinion and real-life case studies. The research concludes that the DST provides stakeholders with high-level insights regarding the suitable DT maturity levels—essential for stakeholder buy-in—assists them in making data-driven decisions especially during the front-end planning stage, and helps determine realistic technology objectives that contribute to project success.