A digital twin implementation strategy for predictive maintenance in facility management

Purpose Digital twin (DT) technology holds significant promise for transforming facility management (FM) by enabling real-time asset monitoring, early fault detection and data-driven decision-making for predictive maintenance (PdM). While PdM uses historical and real-time data to anticipate equipment failures, DT offers dynamic virtual replicas of physical assets for continuous performance optimization. This study aims to examine the integration of DT and building information modeling to support PdM in building facilities, aiming to address inefficiencies inherent in traditional reactive maintenance approaches. The research explores key questions around the main challenges and benefits of deploying DT for PdM, the effectiveness of DT in enhancing FM processes and sustainability and the critical components needed for a practical DT implementation strategy. Design/methodology/approach A qualitative approach was adopted, combining a systematic literature review with semistructured interviews with industry professionals. Thematic analysis was used to synthesize the results. Findings The results demonstrate DT’s potential to improve maintenance through enhanced decision-making, greater operational efficiency and stronger predictive capabilities. However, significant challenges were identified, including high implementation costs, data integration challenges, lack of standardization, organizational resistance and skill gaps. The findings highlight the significance of aligning people, processes and technology to enhance the impact of DT. Practical implications The research offers actionable insights for facility managers and policymakers aiming to implement DT-driven PdM, ultimately supporting the development of smarter and more sustainable built environments. Originality/value This study proposes a structured strategy comprising seven key elements to facilitate DT adoption in FM: (1) needs assessment, (2) incremental deployment, (3) structured data management, (4) seamless system integration, (5) organizational anchoring, (6) standardization protocols and (7) a long-term vision.