Leveraging Digital Twin for Operational Excellence - Singh, Sujit Kumar (Digital Repository of Theses, 2025)
This research project will look at how “Digital Twins” (DTs) can be utilized to improve the industrial and engineering industries' operational excellence. This research’s primary goal is to close this knowledge gap and offer proof that DTs can enhance operations. The economic viability of DT adoption, the effects on KPIs, processes optimization, operational efficiency enhancement, product quality and consistency enhancement, and DT fusion technology with “Engineering, Procurement, and Construction” (EPC) principles to increase project efficacy are just a few of the topics this research will examine to meet its objectives. The research technique will adopt a descriptive and quantitative approach, incorporating data gathering using a questionnaire survey and subsequent statistical analysis using IBM’s SPSS (Statistical Packages for Social Sciences). Economic benefits, effects on operational excellence, cost-cutting techniques, and methods for increasing operational efficiency are all things the study should illuminate. Research proves that DT technology positively and immensely affects productivity, resource utilization, and quality of output, and most of the respondents affirmed that they observed decreased downtime, minimized waste, and effective decision-making due to DT in their respective organizations. Furthermore, the integration of DT with EPC shows the effectiveness of DT in managing complex projects and enhancing project delivery. However, there was recognition of barriers to implementation, especially high initial costs. The study concludes that DT technology offers substantial long-term economic benefits and operational improvements, making it a valuable asset for organizations seeking competitive advantage. Theoretical implications include a clearer framework for evaluating DT impact on operational KPIs, while managerial implications highlight DT’s role in resource optimization, project efficiency, and cost-effectiveness. Future research is recommended to explore industry-specific DT applications, sustainable impacts, and the integration of DT with artificial intelligence to maximize its potential.