DIGITAL TWINS AS DATA-DRIVEN SUPPORT FOR PERFORMANCE MANAGEMENT IN THE EFQM MODEL: A COMPARATIVE AND THEORETICAL ANALYSIS

Abstract

Digital twins enable real-time monitoring, prediction, simulation, and optimization of physical assets and processes. however, their role in broader organizational performance management remains insufficiently defined. this paper examines how digital twin functionalities can provide data-driven support for performance management within the efqm model. a structured literature-based comparative analysis and qualitative content analysis were applied to studies addressing digital twins, efqm, quality 4.0, key performance indicators, and digital transformation. the findings indicate that digital twins can strongly support the efqm execution and results dimensions through operational monitoring, predictive analysis, and performance measurement, while scenario simulation can also inform direction. an integrative theoretical matrix connects digital twin functionalities with performance indicators, efqm dimensions, radar elements, and managerial decisions. the analysis concludes that digital twins can strengthen evidence-based assessment and continuous improvement but cannot independently evaluate leadership, organizational culture, or stakeholder perceptions.