A Digital Twin (DT) is an advanced technology used in predictive maintenance (PdM) to enhance efficiency and reduce downtime. However, adopting DT in PdM is challenging. The focus of this study is to identify the key factors influencing the adoption of DT in PdM and to validate the conceptual and structural models.
The factors affecting the adoption of DT for PdM are identified through a literature review and finalized with the help of experts. Additionally, factor analysis is used to make the conceptual and structural model.
The seven factors were identified, and based on these factors, seven hypotheses were developed. Top management supports system monitoring, sustainable practices, safety and risk management and privacy and security concerns, which are supported by hypotheses. However, technical complexities and parameter optimization are not supported hypotheses.
The sample size was finalized using the Cochran formula, and 100 responses were gathered for the study.
This study helps managers and policymakers to adopt DT for PdM effectively. The results may help management allocate the required funds and provide training programmes to adopt DT technology.
This study shows the influence of factors on the adoption of DT for PdM and determines an effective structure-based modeling method. This can be used in the manufacturing industry to select and implement the DT for PdM.
