Table 9. Potential disadvantages of... | Emerald Publishing

Table 9.

Potential disadvantages of implementing digital twins for predictive maintenance

Challenge	Description	Impact on predictive maintenance (PdM)
Data integration and interoperability issues	Difficulty in combining data from BIM, IoT and FM systems due to vendor incompatibility and fragmented data environments	Leads to incomplete or inconsistent data sets, reducing the accuracy and reliability of PdM insights
Lack of standardization	Absence of shared frameworks and practical standards (e.g. beyond ISO 19650) for DT implementation	Causes inconsistency in data formats and exchange, limiting scalability and interoperability of PdM solutions
High implementation and operational costs	High initial investments in sensors, analytics tools and data infrastructure, with uncertain ROI	Limits adoption, especially among cost-sensitive organizations, and discourages long-term PdM integration
Organizational and cultural resistance	Resistance to change, unclear roles and siloed workflows in FM organizations	Slows DT adoption and weakens cross-disciplinary collaboration required for PdM
Competence and knowledge gaps	Lack of skilled personnel in BIM, IoT and data analytics for managing DT systems	Reduces the ability to design, maintain and interpret predictive models effectively
Data management and quality concerns	Inconsistent, incomplete or poor-quality data from sensors and models	Produces unreliable predictions, false alarms or missed maintenance needs
Lack of practical implementation guidance	Few comprehensive, actionable frameworks for DT deployment in real-world FM contexts	Causes uncertainty and inconsistency in PdM project planning and execution
Variation in DT definitions and understanding	Different interpretations of the DT concept across organizations	Creates confusion in project goals, KPIs and expected PdM outcomes
Dependence on organizational maturity	Success relies on the organization’s existing digital maturity and data infrastructure	Low-maturity organizations struggle to reach the predictive stage of DT utilization
Long-Term Vision and Patience Required	DT and PdM implementation require incremental development and ongoing commitment	Early lack of results may discourage stakeholders, jeopardizing the sustainability of DT initiatives

Challenge	Description	Impact on predictive maintenance (PdM)
Data integration and interoperability issues	Difficulty in combining data from BIM, IoT and FM systems due to vendor incompatibility and fragmented data environments	Leads to incomplete or inconsistent data sets, reducing the accuracy and reliability of PdM insights
Lack of standardization	Absence of shared frameworks and practical standards (e.g. beyond ISO 19650) for DT implementation	Causes inconsistency in data formats and exchange, limiting scalability and interoperability of PdM solutions
High implementation and operational costs	High initial investments in sensors, analytics tools and data infrastructure, with uncertain ROI	Limits adoption, especially among cost-sensitive organizations, and discourages long-term PdM integration
Organizational and cultural resistance	Resistance to change, unclear roles and siloed workflows in FM organizations	Slows DT adoption and weakens cross-disciplinary collaboration required for PdM
Competence and knowledge gaps	Lack of skilled personnel in BIM, IoT and data analytics for managing DT systems	Reduces the ability to design, maintain and interpret predictive models effectively
Data management and quality concerns	Inconsistent, incomplete or poor-quality data from sensors and models	Produces unreliable predictions, false alarms or missed maintenance needs
Lack of practical implementation guidance	Few comprehensive, actionable frameworks for DT deployment in real-world FM contexts	Causes uncertainty and inconsistency in PdM project planning and execution
Variation in DT definitions and understanding	Different interpretations of the DT concept across organizations	Creates confusion in project goals, KPIs and expected PdM outcomes
Dependence on organizational maturity	Success relies on the organization’s existing digital maturity and data infrastructure	Low-maturity organizations struggle to reach the predictive stage of DT utilization
Long-Term Vision and Patience Required	DT and PdM implementation require incremental development and ongoing commitment	Early lack of results may discourage stakeholders, jeopardizing the sustainability of DT initiatives

Source(s): Authors’ own work